Page 16 – DataToBiz

Yashica Chopra
January 05, 2024
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AI Product Development for Startup Apps- Top 5 Companies to Help You Get Started

AI empowers a business with the latest technology and uses analytical tools for decision-making. Startups need artificial intelligence to compete with brands that are already popular in the market. Here, we’ll discuss the top companies offering AI product development for startups. Artificial intelligence is disrupting every industry. Businesses around the globe are revamping their systems and processes to adopt the latest technologies to survive the ever-changing competitive markets. Organizations are either planning to digitally transform or have already invested in the process. This makes it crucial for startups to include AI in their business model. Harnessing AI’s potential will be the deciding factor for many businesses. The global AI market is expected to grow by a CAGR (compound annual growth rate) of 36.38% from 2023 to 2030. Another report says that the growth of AI startups has increased fourteen-fold since 2000. Moreover, 84% of senior leaders consider artificial intelligence essential to achieve business growth. The statistics reflect the increasing demand for AI in various industries. It’s not surprising that many startups ask questions like what is AI and how can it help their business. In this blog, we’ll read how artificial intelligence can redefine your business and streamline internal processes, including product development. Will AI Revolutionize the Product Development Process? Yes! Artificial intelligence is already revolutionizing product development by eliminating unwanted steps, optimizing resources, automating recurring tasks, and enhancing quality. In fact, AI product development tools can be used at every stage of product development, from ideation to delivery. Here’s how: AI Product Development for Startups: Top 5 Companies DataToBiz DataToBiz is a leading artificial intelligence company with clients from several industries. The company offers AI product development for startups, SMBs, MSMEs, and large enterprises in different continents. It helps business organizations unlock the power of data and use its true potential to achieve long-term goals. The award-winning company has many experienced AI engineers who worked on numerous projects of various complexities. With over 97% project satisfaction rate, the company provided a plethora of AI and ML solutions for 300+ firms. DataToBiz provides tailor-made AI services based on the client’s requirements. Be it platform and dashboard customization to pilot implementation, consulting, big data analytics, data engineering, digital transformation, and much more, the company knows how to handle the challenges faced by organizations. It also provides affordable and cost-effective AI services for startups to help them gain a competitive edge among established businesses. The company is a certified partner of Microsoft, Google, and AWS and offers extensive cloud solutions. The team also builds and deploys large language models for generative AI-based applications. Prosodica Prosodica is among the AI app development companies that focus on using the latest technologies in customer service. The company offers enterprise-grade analytics for speech and voice to help businesses understand conversions with customers and derive actionable insights. It has an AI platform to humanize the call center experience and help clients build a deeper and meaningful connection with customers. The platform works in four effective steps – collecting conversational data, running voice and speech analytics, deploying ML models to detect patterns, and providing solutions to reduce failures. Prosodica’s conversation analytics platform can be integrated with various apps and software to streamline data flow. It offers future-proof technology and solutions to ensure organizations stay in tune with their customers’ needs. The company also helps boost ROI by enhancing customer experience. Simultaneously, it promotes employee performance by reducing stress caused by dealing with user complaints. The company’s solutions are used in various industries like healthcare, hospitality, financial services, etc. DataVisor DataVisor is an AI product development team that provides real-time insights about fraudulent transactions. It uses applied machine learning capabilities to help businesses keep their clients safe from cyber abuse and fraud. The company has developed AI products to effectively handle modern-day fraudulent tactics and adopt preventive measures with agility. It has been recognized as the Best Transaction Fraud Monitoring and Decisioning Solution in the Datos Insights’ 2023 Fraud & AML Impact Awards. DataVisor’s fraud and risk management platform helps mitigate cybersecurity concerns in real time. The company works with establishments from the finance, banking, FinTech, credit unions, and digital payment industries. From managing application fraud to preventing transactional fraud (wire transfer, check, etc.), the company offers consistent results and complete control to businesses. The AI platforms can be customized and aligned to suit the organization. The platform is hyper-scalable, easy to use, and has low latency. Convr Convr is an AI-driven modern underwriting platform developed for insurance companies to enhance and automate their core internal processes. The company is founded by underwriters with vast industry experience to provide a reliable solution for insurance providers in the market. The company calls its platform a tool for underwriting on steroids. It is a modularized AI-powered underwriting and data analytics platform that also provides business classification and risk scores. Convr aims to solve the five biggest problems faced by commercial insurance companies- submission management, accurate business classification, gathering business information, identifying submission Q&A, and evaluating risk quality. It reduces 70% application questions and increases efficiency by 130%. The platform works by combining submissions with the best data sources to deliver reliable insights. The platform promotes transparency and speed. It can be used as an independent tool or integrated with third-party apps. People.ai People.ai is an AI development company offering an enterprise revenue intelligence platform with its patented technology. It helps businesses speed up their sales cycles and to engage with the right audience at the right time. The company helps accelerate growth, innovation, and revenue. It provides the necessary insights for sales teams to focus on the right audience instead of chasing the wrong crowd. This reduces expenses while ensuring that interested leads are not ignored or forgotten. People.ai believes that the customer is everything for a business. It also feels that every problem has a solution. The company’s approach to this is quite simple. It follows a customer-centric approach to understand what the issue is and find a solution accordingly. The company has a presence in industries like IT, life

Manpreet Kaur
January 03, 2024
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A Comprehensive Guide to LLM-Based Model Development

AI products built with LLM-based model development have completely changed how computers understand and generate human-like text. These models, a type of deep learning tech, are super useful in things like chatbots and language translation. They’ve revolutionized how we talk to computers and extract relevant information. In this blog, we’ll explore what LLMs are, how to build one step by step and discuss some popular LLMs that have made significant impacts in the field. What is a Large Language Model (LLM)? A Large Language Model, often abbreviated as LLM, is a neural network-based model designed to process and generate human language. These models can understand, generate, and manipulate text with an astonishing level of fluency and coherence. They are considered “large” because they typically consist of tens to hundreds of millions, or even billions, of parameters, which are the elements that allow the model to learn patterns and associations in language. LLMs are trained on vast amounts of text data, often including books, articles, websites, and other textual sources. During training, the model learns to predict the next word in a sentence or to generate coherent text by understanding the statistical relationships and patterns in the data. This ability to generate human-like text makes LLMs a powerful tool for a wide range of applications. How Does a Large Language Model Work? Large Language Models, such as those based on transformer architecture, operate by a two-fold process: pre-training and fine-tuning. These phases are essential for enabling the model to carry out both general language understanding and specific tasks. Pre-training Large Language Models are initially pre-trained on vast text datasets containing a wealth of information from diverse sources like encyclopedias, books, and the internet. During this phase, the model undergoes unsupervised learning, where it absorbs linguistic patterns and contextual cues from the data without explicit guidance. It’s akin to the model immersing itself in the vast sea of language. For instance, it learns that “bat” can refer to a flying mammal or a piece of sports equipment based on the surrounding text. Fine-Tuning To make the model more task-specific, it goes through a fine-tuning process. This is akin to giving the model specialized training for particular tasks. It’s like preparing a chef with a general culinary skillset and then training them to excel in French cuisine or sushi preparation. Now, let’s explore “prompt-tuning,” which is similar to fine-tuning but with a twist. Prompt-Tuning Imagine the model as a versatile assistant that can perform a wide array of tasks. In prompt-tuning, we guide the assistant by providing specific prompts or instructions for different tasks. There are two key flavors to prompt-tuning: Few-Shot Prompting: In this approach, the model learns how to respond to certain tasks by presenting it with a few examples. For instance, when training the model for sentiment analysis, you could show it pairs like: The model learns to grasp the nuances in language, connecting words like “incredibly thrilling” with positivity and “terribly boring” with negativity. Zero-Shot Prompting This method tasks the model to perform a specific function without prior examples. It’s like handing a chef a new recipe they’ve never seen before and asking them to prepare it. For sentiment analysis, you might instruct the model with a prompt like, “Determine the sentiment of ‘The weather today is fantastic.’” The model, without any examples, deduces that “fantastic” conveys a positive sentiment. In both fine-tuning and prompt-tuning, the model becomes increasingly adept at performing tasks because it has refined its understanding of how to interpret and generate text based on the specialized training provided. Building Your Large Language Model To build an LLM model, you will need: ● A massive dataset of text and code. ● A powerful computer to train the model. ● A deep learning framework, such as TensorFlow or PyTorch. Once you have these resources, you can follow these steps to train an LLM model: Collect and clean your data The first step is to collect a massive dataset of text and code. This dataset should be as diverse as possible, and it should contain examples of the types of tasks that you want your LLM model to be able to perform. Once you have collected your data, you will need to clean it and preprocess it. This may involve removing special characters, correcting spelling errors, and splitting the text into words or subwords. Choose a model architecture There are many different LLM architectures available. Some popular architectures include the Transformer and the GPT-3 family of models. The best model architecture for you will depend on the specific tasks that you want your LLM model to be able to perform. Train the model Once you have chosen a model architecture, you can start training the model. This process can take several days or even weeks, depending on the size of your dataset and the power of your computer. Evaluate the model Once the model is trained, you need to evaluate its performance on a held-out test dataset. This will help you to determine how well the model can generalize to new data. Deploy the model Once you are satisfied with the performance of the model, you can deploy it to production. This may involve making the model available as a web service or integrating it into an existing software application. However, building a Large Language Model from scratch is a resource-intensive endeavor, you can contact LLM consulting companies for guidance while developing the LLM Model. Using a Large Language Model Leveraging Large Language Models (LLMs) opens the door to a multitude of practical applications across various industries. Here, we will explore the diverse array of applications in which LLMs have found utility, along with real-world examples of their implementation. Information Retrieval Large language models play a pivotal role in information retrieval systems, akin to popular search engines like Google and Bing. These platforms utilize LLMs to fetch and synthesize information in response to user queries. By

Kavika Roy
January 03, 2024
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Behind the Scenes: A Look into Our AI Product Development Process

Discover how AI provides a seamless roadmap to overcomes the challenges in AI product development process, paving the way for innovation and exceptional possibilities? Well, let’s talk about that today!! The usage of AI in the product development process catalyzes innovation and enhances operational efficiency. It makes it easy to design, develop, and customize products to fulfill the customer’s needs. From transforming product development methodologies and outputs through personalized product recommendations and quality control, the scope of AI in product development is vast. In this blog, we’ll uncover the secrets behind our successful AI product development process. Let’s jump right in. Sneak-peak in the tools used for AI product development: AI Development: Tensor Flow, Amazon Sage Maker, Tableau, Keras, Microsoft Cognitive Toolkit, Cloud AutoML Vision, Apache MXNet Deployment: Azure, AWS, Google Cloud Platform, Nvidia Data Storage: Amazon Redshift, Amazon Athena, SAP, Azure Synapse Analytics, Azure Data Lake Storage, MongoDB, Microsoft SQL Server, Google Big Query, MySQL, Hadoop, Azure Integration Services Data Processing: Microsoft SQL Server, Talend, Azure Data Factory, Python, Spark, Kafka, Airflow, AWS Glue, and Databricks. Steps in AI Product Development Our AI product development team follows a six-step plan to create AI products. Our experts ensure a smooth transition as the output of one pipeline depends on the output of the other. Now, I’ll explain the steps in detail: Kicking-off the Project: The first phase of the AI product development roadmap involves defining the project scope and specifying what our experts will work on. Further, they choose the tools and technology stack and delve deep into user stories and use cases to find out how users will interact with the product. Next, the AI development experts create the system architecture blueprint along with the UI/UX design. Minimum Viable Product (MVP): Minimum viable product refers to a product version with sufficient features to attract early adopters and validate the product idea in the early stages. The AI developers prioritize the most important features and create a basic version of the product. Next, they build and improve the product in small steps known as iterative development, and ask the audience to use the product and share their feedback. Data Acquisition and Preparation: This third stage of the AI product development process includes gathering data and planning how this data will be used to build the product. The developers verify the accuracy and reliability of the data by cleaning and organizing it. Further, they add tags (if necessary) to categorize information and protect it from unauthorized access. Model Development and Training: Choose the right model and build the AI product that fulfills the specified needs. Transfer learning considerations, train the AI product, and test it to assess whether it meets the purpose it is designed for. Integration and Development: Integration and development is the fourth stage of AI product development. It includes API development, mobile apps, and web apps with a scalable architecture. It is important to ensure that the AI product works smoothly on various operating systems and devices. Quality assurance is also essential to verify that the product works efficiently and is free of bugs. Deployment and Maintenance: Deployment and maintenance are the best practices for AI product development to launch your AI product into the market. Once the product is launched, find out how the AI product is performing and take note of how you can make your product better. After you have made changes, provide the new versions of the AI product and maintain it to ensure smooth functioning. Benefits of AI Product Development Process Rich Computation AI enables the development teams to analyze huge amounts of data, detect patterns, and make predictions with machine learning techniques and algorithms. This makes it easy to build efficient AI products. Demographic Analysis With AI, developers can analyze data quickly and find new ideas. For example, you can use AI to understand customer preferences and recommend new features for a smartphone that people may like. Psychometric Analysis AI makes it easy to track and analyze data from different sources such as social media, search history, and physiological metrics. With a focus on ethical considerations, AI can be used to analyze data patterns and predict the probability of adoption of new products. Less Time AI significantly reduces the time needed for processes such as prototyping, testing, and development. The developers can create quick simulations, test them, and make improvements if needed. Informed Decision-making AI can quickly analyze large datasets and create multiple probabilities, thereby facilitating businesses to explore all aspects during decision-making. High Security: AI-powered authentication systems such as biometric scanning and CAPTCHA track security breaches, virus attacks, and malware. Also, the developers can use advanced tools to carry out investigative processes to trace vulnerabilities and prevent risks. Conclusion: Deploying AI into product development is the key strategy to optimize development processes and enhance productivity. AI product development companies help you to innovate, design, and launch AI products that remain competitive in the market. It is mandatory to abide by ethical considerations to ensure the sustainable growth of AI and implement effective strategies to redefine the future of AI product development. Originally Published on Medium

Kavika Roy
January 03, 2024
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How AI Can Help You Build Your Dream App!

We see artificial intelligence everywhere! It has a key role in app development too, be it speeding up the process or enhancing user experience. AI can indeed help you build your dream app or other AI/ML-based products and release it into the market. Let’s discuss how AI app development can be done. Artificial intelligence is no longer a distant technology. It is a part of our everyday lives and used by many people in one way or another. The advent of generative AI has led to major developments where almost anyone can create art, music, and apps without making heavy investments. While the results and ethical issues are debatable, we cannot ignore how AI has been integrated into our lives. According to Grand View Research, the global artificial intelligence market is estimated to grow by a CAGR (compound annual growth rate) of 37.3% and reach $1,811.8 billion by 2030. ChatGPT is the fastest-growing AI application, with over 180.5 million global users, of which 100 million are active weekly. This includes 92% of the Forbes 500 companies. Many individuals, entrepreneurs, and business organizations want to build an app with AI to suit their specific requirements. While this was expensive a few years ago, AI app development is now a cost-effective solution for building and deploying a customized application. In this blog, we’ll understand the role of artificial intelligence in building apps and see how you can build your dream app using AI. Role of AI in Apps Development AI app development is versatile and user-friendly. It helps streamline the ideation to implementation journey by reducing the risk of errors and speeding up lengthy processes. From automating code generation to debugging and reasoning, AI can be used in many ways. Machine learning and natural language processing are used to train the app to read and understand user input and provide relevant output. We live in a competitive world where businesses cannot afford to wait for months and years to release an application. AI for app development is a quick and effective solution to overcome this problem and take new apps to customers in less time. It’s no surprise that the demand for AI product/ app development companies have increased in recent times. Though you can use generative AI or ChatGPT to build your app, partnering with an experienced company will give long-lasting results. You can also hire their maintenance and support services to ensure customer satisfaction and higher ROI. Here are a few ways to use AI product development tools: How to Use AI for App Development Developing an application requires a lot of groundwork, coding, testing, documentation, etc., which leaves the teams on a tight schedule to get everything done in time. However, an AI product development team can streamline the tasks effectively and stay one step ahead. How, you ask? Find out below. Project Management Building and launching an app is a project, isn’t it? The success of your app also depends on how well you manage and organize the entire process. Artificial intelligence can help with planning, monitoring, measuring, collaborating, testing, and a lot more. It speeds up the process and increases the efficiency, accuracy, and performance of the app development team. For example, AI can automate recurring tasks, send alerts and reminders, schedule meetings, manage milestones, and support strategic data-based decision-making. AI also promotes innovation while reducing the risk of error. It makes collaboration easier, thus minimizing communication issues between team members. Automated Code Generation An app requires several pages of code. While all of it doesn’t have to be manually created from scratch (sections of code are taken from open-source and edited), the rest of the work falls on the programmer. With AI app development, most of the code can now be generated automatically. The programmer provides the input parameters for the system to write the code on its own. AI-generated code already adheres to the coding practices and brings more consistency to the project. While AI may not be 100% accurate with intricate logic commands, that part can be handled by the programmer. The boring sections can be auto-generated. It saves time and ensures human expertise. A win-win situation for everyone, isn’t it? Github Copilot is an example of an AI-powered code generation tool. Predictive Analytics Predictive analytics combines statistics and modeling techniques to predict an outcome for the given input. App developers run analytics on historical and present data to get insights about potential bugs and glitches that may arise during the project. From predicting how many lines of code will be written to calculating the resources required, predictive analytics helps app developers in many ways. Predictive analytics is a part of advanced analytics run using AI and ML models. Large datasets are analyzed to make data-driven decisions about project timelines, expenses, risks, opportunities, marketing, etc. Enhance User Experience How can you forget user experience? Keeping the end user happy with the app is the primary goal. While many want to create the best app for users, the execution is far from easy. AI can help here. AI product development companies use machine learning algorithms to analyze data and generate reports about user preferences. This gives them the metrics or indicators that help in adding features to the app. They are also useful for measuring the app’s performance during testing and debugging. Building apps backed by proper research will increase user experience, bring higher engagement, and establish the brand image. Natural Language Processing NLP is used to make a machine under human language in the proper context. It plays a vital role in bridging the gap between human and machine communication. NLP makes apps more intuitive and user-friendly. In fact, it is considered a reliable tool to create user-centric applications. Moreover, NLP-powered tools convert descriptions and feedback into actionable insights for the app-development team. It also automatically translates user requirements into an understandable format, making it easy for developers to customize the application for a wide range of audiences. An example of NLP in app development is a chatbot that responds

Yashica Chopra
January 01, 2024
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5 Real-World Examples of AI Products in Action- From Start to Finish

AI products generate a lot of discussion. Despite the concerns, artificial intelligence is growing by leaps and bounds. Here, we’ll discuss some well-known real-world examples of AI products and their workings. Artificial intelligence covers a variety of concepts like machine learning, deep learning, natural language processing, computer vision, etc. AI has become integral to our lives in recent times. AI is versatile and can be used in a plethora of activities, operations, and industries. The global artificial intelligence market is over $136.6 billion, while the AI industry is expected to grow by 13x in the next seven years with a CAGR (compound annual growth rate) of 38.1%. Another report shows that 87% of global companies think AI gives them a competitive edge in the market. Be it in our personal or professional life, we cannot avoid using AI products in some form. It’s no surprise that SMBs, MSMEs, emerging businesses, and large enterprises are partnering with AI product development companies to build and deploy custom applications powered by artificial intelligence. This blog details real-world examples of AI products used by several people from around the globe. What is an Example of Artificial Intelligence in Action? Almost half the businesses (48%) use some form of artificial intelligence, machine learning, and data analytics in their establishments. AI is meant for personal (individual), team, organizational, and global use. A few common examples of artificial intelligence in our day-to-day lives are listed below. Text Editors Chatting, commenting, emailing, etc., are a part of our lives. The keypad apps in smartphones have built-in AI to auto-predict the sentences and emojis. Gmail also has an auto-predictor that suggests the next part of the sentence as you type. Bloggers, content writers, and authors rely on AI text editors like Grammarly, ProWritingAid, etc., to streamline content. From identifying typos and punctuation mistakes to making suggestions about better sentence structures, the software improves the quality of writing. Artificial intelligence algorithms are trained on high-quality language data to identify errors in the input content and rectify them. The AI product development team also trains to understand the grammar rules and differences between different types of English. Navigation and Maps Maps and GPS are some of the best real-world examples of AI products in action. Instead of using printed maps, we access the map and route through Google or Apple Maps. By connecting the phone to the car’s software, The GPS system will give directions in real-time. While GPS has been here for a while, the system has been enhanced by integrating it with artificial intelligence. Machine learning algorithms are now taught to understand traffic flow, remember and recognize house numbers, and identify the best route to your destination. Taking things a step further, AI and ML are essential in self-driven cars, where two or more cars constantly communicate with each other. Facial Recognition Facial recognition is another real-world AI product. The face lock security feature on smartphones is a perfect example of this. Many places, like airports, etc., use facial recognition to confirm your identity. It is a part of security and surveillance systems. The police use facial recognition software when checking leads and suspects to find the criminal. Smart cameras come with this software to enhance the quality of selfies. Many apps like Snapchat, TikTok, etc., use it to add filters and customize images/ videos. Facial recognition is combined with AR technology for users to get a real-time experience in trying makeup products, eyewear, jewelry, etc. Recommendation Algorithms Search algorithms are not limited to SEO. They are backed by artificial intelligence to provide personalized results based on your search history and preferences. For example, if you search for a handbag, you will notice relevant ads on your social media platforms and other websites. Platforms like Facebook, Instagram, X, etc., train their algorithms to provide content based on the posts you like, comment, and share. Over time, the algorithms learn the behavior and tailor the feed accordingly. This is useful as you can avoid seeing content you don’t like. However, it can also feed you misinformation and half-truths. Healthcare Real-world AI products are rampant in the healthcare industry, with many hospitals and pharma companies investing in advanced technology. Data shows that 38% of medical and healthcare providers use computer-aided diagnosis when treating patients. Be it better diagnosis, personalized treatment plans, early detection of diseases, remote patient care, or faster drug discovery, artificial intelligence has a prominent role in the healthcare sector. Doctors are using robotic arms for complex surgeries. Mixed reality (MR) devices are also used to access patient information and get remote assistance during operations. Chatbots and virtual therapists are more examples of AI in healthcare. Customer Service The customer service industry is quickly using chatbots to interact with customers. While the earlier chatbots had limited features, the ones powered by AI are more efficient in handling customer queries and complaints. Moreover, chatbots can interact with multiple users simultaneously and work 24*7 without tiring. This reduces the pressure on the representatives. Businesses from every industry use chatbots in today’s world to enhance customer service. From tracking information, taking orders, answering queries, and generating tickets, the chatbots can handle several tasks with ease. Electronic Payments Payment apps are well-known AI products in everyday life. Google Pay, PhonePe, Paytm, etc., are commonly used payment apps in India to make instant electronic payments by scanning a QR code. Many banks now use artificial intelligence algorithms for fraud detection to prevent scams and dubious transactions. AI is also used for identity management, account management, and security management in banks, insurance companies, and financial institutions. The credit card spending patterns can be analyzed using artificial intelligence to understand the customer’s LTV and increase limits or offer loans. What are the Real-World Examples of AI Products? Home Assistants Home assistants are the easiest example of AI products used in our day-to-day lives. Many of us have a home assistant device like Alexa, Siri, Cortana, or Google Assistant to play music, place an order, read out webpages, or provide answers to our questions by searching on the internet. They

Mihir Soni
December 29, 2023
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Power BI and Python Integration: Transforming Analytics

Unlock the potential of Power BI and python integration. Seamlessly integrate scripts, follow the step-by-step guide, and explore these best practices for transformative data analysis. Microsoft Power BI provides organizations with robust tools to transform raw data into actionable insights. With its intuitive interface and seamless integration capabilities, Power BI helps create compelling visualizations, dashboards, and reports, facilitating a comprehensive understanding of their data. Power BI’s core strength lies in its ability to streamline complex datasets into visually appealing and easy-to-understand representations. Whether it’s tracking KPIs, identifying trends, or exploring patterns, Power BI offers a versatile platform for data visualization. The significance of this tool extends beyond mere reporting; it catalyzes informed decision-making and strategic planning. As the demand for deeper insights and predictive analytics continues to grow, combining Power BI with ML emerges as a transformative union. Integrating machine learning algorithms into Power BI opens new dimensions for data analysis, enabling organizations to move beyond descriptive analytics to predictive and prescriptive analytics. Understanding Power BI with Python Integration The synergy between Power BI and Python has become a game-changer in advanced analytics, elevating data analysis to new heights. Power BI’s native integration with Python introduces a seamless collaboration between a powerful business intelligence tool and the versatility of a leading programming language. One of the pivotal features of this integration is the incorporation of Python scripts within Power BI, enabling the creation of robust machine learning (ML) models. The Python script function in Power BI allows users to develop ML models directly within their reports, leveraging Python’s extensive libraries for statistical analysis and machine learning. What distinguishes this integration is its independence from the Power BI desktop, ensuring that ML models remain autonomous entities capable of automatically updating and generating predictions on live data whenever the model is executed or the dataset is refreshed. How to Integrate Machine Learning in Power BI with Python The integration of ML into tools like Power BI has become a pivotal milestone in business intelligence. Here are the steps to integrate Machine Learning in Power BI with Python: Milestone 1: Integration of Python in Power BI Milestone 2: Running ML Model in Power BI Desktop Limitations: Milestone 3: Installation of Personal Gateway Milestone 4: Trying simple Python script and publishing on the PBI Service Milestone 5: Running Basic ML Queries Note: Benefits of Using Python for Machine Learning in Power BI The decision to harness Python for machine learning within Power BI has multiple benefits. Extensive Libraries and Ecosystem Python’s vast array of libraries, such as NumPy, Pandas, and Scikit-learn, equips Power BI users with a rich set of tools for data manipulation, analysis, and model development. This amalgamation of Power BI’s visualization capabilities with Python’s libraries creates a potent environment for crafting sophisticated ML models. Seamless Integration with Power BI The native integration of Python in Power BI is designed for simplicity and efficiency. Users can seamlessly embed Python scripts into their Power BI reports, fostering a cohesive workflow that integrates advanced analytics effortlessly. This synergy allows analysts and data scientists to work within a familiar environment while leveraging the diverse functionalities of Python. Real-Time Predictions and Updates A critical advantage lies in the ability to generate real-time predictions. The ML models, once integrated, operate independently, providing instant insights as soon as new data is fed into the system. This real-time responsiveness adds a layer of dynamism to data-driven decision-making processes. Best Practices and Tips for Integrating Machine Learning in Power BI Using Python Adopting best practices and leveraging key tips becomes critical for businesses targeting a seamless and effective integration of machine learning within Power BI using Python. Here are some of the best practices and tips: Optimizing Performance To enhance the performance of machine learning models in Power BI, consider optimizing your scripts and queries. Aim for efficiency in data processing, and minimize unnecessary computations. Additionally, choose the appropriate algorithms and techniques that align with the scale and nature of your data. Efficient Handling of Large Datasets When working with large datasets, implement strategies for efficient data handling. Utilize Power BI’s native features for data modeling and aggregation, and consider pre-processing steps to reduce the volume of data without compromising the integrity of your analysis. Version Control for Python Scripts Establish a version control system for your Python scripts within Power BI. This ensures traceability and facilitates collaboration among team members. Having a version history enables you to track changes, revert to previous states, and maintain a structured development environment. Data Security and Compliance Prioritize data security and compliance when integrating machine learning in Power BI. Ensure that sensitive information is handled appropriately, implement encryption protocols, and adhere to regulatory requirements. Regularly update security measures to mitigate potential vulnerabilities. Documentation and Knowledge Transfer Thoroughly document your machine learning models, scripts, and data sources within Power BI. This documentation is invaluable for knowledge transfer within teams, aiding in troubleshooting, and facilitating future enhancements or modifications. Clear documentation fosters collaboration and ensures the sustainability of your analytics solutions. Regular Model Monitoring and Maintenance Implement a robust monitoring system for your machine learning models post-integration. Regularly assess model performance, identify deviations, and update models as necessary. This proactive approach ensures that your models remain accurate and relevant in dynamic business environments. Testing and Validation Before deploying machine learning models in a production environment, conduct thorough testing and validation. Validate the accuracy of predictions against historical data, assess the model’s generalization to new data, and ensure that it meets the desired performance criteria. Rigorous testing minimizes the risk of inaccuracies in real-world applications. Wrapping up As we stand at the intersection of Power BI’s robust data visualization capabilities and the predictive prowess of Python-based machine learning, the future of data intelligence is both exciting and transformative. The road is marked by continuous evolution and innovation powered by artificial intelligence and machine learning. The demand for deeper insights and real-time predictions from machine learning companies and Power BI service providers will drive further enhancements

Yashica Chopra
December 28, 2023
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9 Business Intelligence Challenges with Solutions

Business intelligence empowers an enterprise to make data-driven decisions in real time. It has several advantages and will be a part of major businesses in the future. However, you should overcome business intelligence challenges to get the desired results. We’ll discuss the issues and ways to convert the disadvantages to benefits. Running a business requires hard work and smart work. It is important to find a balance between legacy systems and advanced applications. Similarly, the transition from traditional decision-making methods to data-driven decisions is filled with challenges. While the past generations made decisions based on observations and intuition, the current businesses have to rely upon something more tangible. Business intelligence is where the tools/ applications use their ‘intuition’ in analyzing data and generating insights. The digital age demands the latest technology, like business intelligence and artificial intelligence. BI is one of the in-demand technologies in the global market. Reports show that the global BI market will touch $33.3 billion by 2025. However, this is easier said than done. Adopting BI in a business requires planning and expertise. You should hire business intelligence companies to implement digital transformation in your organization. The Covid-19 pandemic has increased the adoption rate. Another study says that one-third of large enterprises will use decision intelligence by 2023. Yet, the road to BI adoption is not smooth. Enterprises have to overcome a few challenges to become successful. But what are some potential barriers to business intelligence system success? How can a business solve the issues effectively and use BI for day-to-day decision-making? Let’s find out. What are the Characteristics of Business Intelligence? Business intelligence provides actionable insights to make decisions based on data analytics. It is a combination of strategies and technologies that collect, process, and analyze data (historical and real-time) to provide reports in graphical/ visual formats. BI has the following characteristics: Where is Business Intelligence Used? Business intelligence helps high-level executives, mid-level managers, team leaders, etc., to make decisions based on data-driven insights. Business intelligence is used in many areas and plays a role in every department in the enterprise. So how does business intelligence help companies? Here’s how: Data Mining Data mining is the technique of identifying patterns in large data sets. The data collected by the organizations is cleaned, formatted, and transformed to prepare it for analytics. Marketing From identifying prospective leads to creating a promotional campaign, the insights provided by BI help the sales teams plan and implement their marketing strategies. Competition Understanding competition is vital to succeeding in today’s competitive scenario. BI tools can provide insights to analyze competitors’ strategies, campaigns, products, decisions, etc. Finance Businesses have to make major investment decisions. Should you invest in a new product or redesign an existing one? How to optimize resources and increase returns? BI provides answers to such questions. Risk Management Risks are an integral part of the industry. That said, you should be proactive and have a risk management strategy to avoid losses. BI helps with fraud detection, market trend prediction, and much more. Customer Relationship Customers are vital for a business to survive. Understand what your customers want and where to find new customers. Know what brings to your business and how to inspire brand loyalty. What is the Business Intelligence Cycle? The BI cycle can have different stages based on the organization. However, the major stages of business intelligence are as follows: What are the Benefits of Business Intelligence? Why do organizations need business intelligence in today’s world? Should small businesses also invest in BI tools? How can BI help an organization achieve its goals? The following are some benefits of BI/ reasons to use business intelligence in your enterprise. Increase Productivity Automation, digitization, and collaboration streamline business processes to increase productivity. Higher ROI BI helps you make the right decisions and reduce the risk of losses. You get better returns when your increase operational efficiency and customer satisfaction. Identify Market Trends Understand the market conditions to identify trends and make the most of opportunities. Optimize Resources Don’t waste business resources or invest additional finances to generate resources. BI analyzes inefficient areas and provides solutions to overcome wastage. Manage Inventory Know how much inventory to maintain, when to move the stock, and how to manage logistics using BI insights. Decision Making Business intelligence can be used for everyday and long-term decision-making in the enterprise. Make decisions based on actionable insights. What are the Challenges of Business Intelligence? Business intelligence has several benefits to offer. Nevertheless, it has its share of drawbacks or disadvantages. Digital transformation fails because businesses don’t properly deal with the issues when implementing data-driven models. Let’s look at the disadvantages of Business Intelligence and find out how to overcome the challenges. Data Breach Collecting data is easy, but you need to be careful about securing the data and preventing cyberattacks. A data breach can compromise your business position in the market. Multiple Data Sources With so much data available from external sources, you have to choose the most relevant sources for your business. Data Quality Raw data is of poor quality. Using this will result in wrong or faulty insights that can affect your business decisions. Recurring Costs BI systems have to be maintained and constantly upgraded. This increases the costs for a business. Employee Resistance Digital transformation is not always appreciated by employees. It can be hard to integrate advanced systems with business processes if employees are unwilling to use them. Self-Servicing Analytics Self-servicing analytics requires technical knowledge. Employees should be trained to use the dashboards. Incorrect KPIs Finalizing wrong KPIs can lead to incorrect assessments, thus providing a false report of the business position. No BI Strategy You require a specific BI strategy for your business. There may be a difference between what works for your competitors and what works for you. Mobile BI Though mobile intelligence is becoming common, it is still not easy to set up the systems for it. How to Overcome the Challenges of Big Data and Business Now that you know the

Yashica Chopra
December 22, 2023
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Top 5 Companies Developing AI Products in Healthcare

AI can provide better outcomes to improve the efficiency of patient care delivery in the healthcare industry. It can help doctors, patients, and technicians in various ways. Here, we’ll discuss the top AI companies developing AI products in healthcare. Artificial intelligence in healthcare is a hot topic as more companies enter the market to streamline workflow and patient care. From accurate diagnosis to early detection of diseases and remote treatment, AI has a diverse role in the healthcare industry. Statistics show that globally the use of AI in the healthcare market is anticipated to reach $20.65 billion in 2023 and is expected to touch $187.95 billion by 2030. Another report shows that the global AI in drug discovery and pharma market is estimated to grow at a CAGR (compound annual growth rate) of 45.7% to reach $4 billion by 2027. Similarly, AI in the MRI market is expected to be around $9.8 billion by 2030. AI-driven healthcare products are becoming a norm in today’s world as more hospitals, pharma companies, and healthcare centers are adopting the latest technologies to provide quality treatment and improve the success rate. Let’s find out more about the role of AI products and product development companies in healthcare. Role of AI Products in Healthcare The role of artificial intelligence in healthcare is three-fold. It can improve population health management, streamline operations in hospitals, and strengthen innovation in discovering new drugs and treatment methods. Diagnostics AI applications and computer vision technology can help diagnose anomalies and diseases in the early stages. This allows the doctor to treat the condition before it becomes life-threatening. Patient Care Nurses and the hospital staff can provide better care to patients without working extended hours. This is possible by automating operational tasks and saving time to focus on the patients. They can also stay connected with remote patients and send timely alerts for medication, follow-up visits, etc. Prevention and Wellness AI products in healthcare can prevent the risk of diseases from spreading through timely vaccinations, medication, and quarantine. AI apps can interact with patients to help them navigate mental health issues. Clinical Decision Support AI tools can accelerate decision-making in allocating beds, scheduling doctor visits, discharging patients after treatments, etc. It reduces the risk of errors and miscommunication through automation. Top 5 AI Product Development Companies in Healthcare DataToBiz DataToBiz is among the leading AI product development service providers in the healthcare industry. With years of expertise, the company offers end-to-end AI development, deployment, integration, customization, and maintenance services to hospitals and healthcare centers. It provides comprehensive digital transformation solutions to digitize patient data and make data-driven decisions using real-time visualization reports. From building a data warehouse to customizing analytical dashboards and using computer vision technology for diagnostics, a hospital can revamp all its operations by partnering with the company. DataToBiz also offers generative AI services to build and deploy LLMs (large language models) for interactive medical chatbots to communicate with patients. The company has a presence in different continents like Asia, North and South America, Australia, Africa, and Europe. With a project satisfaction rate of over 97%, DataToBiz delivers powerful AI solutions tailored to the client’s specifications. Butterfly Network Butterfly Network is a service provider in AI product development in the healthcare sector. The company is transforming patient assessment with its innovative handheld device that can be connected to 20+ clinical apps. It can also be integrated with other medical systems to create the documentation for the ultrasound reports. It is connected to cloud systems to streamline billing, patient reports, etc. Over 145,000 customers are already using the device to improve patient assessment and care. The Butterfly Network device can be connected to a smartphone with a cable to access the information in real-time. The company helps with various healthcare departments like cardiology, nursing, gynecology, anesthesiology, emergency care, and veterinary care. From storage to analytics, fleet management, credentialing, quality assurance, and even education, the company takes care of it to support hospitals at every step. It has revolutionized medical imaging using advanced artificial intelligence technology. The company also provides long-term maintenance services to its clients. Caption Health Caption Health offers healthcare app development services and provides an AI-based tool for ultrasound diagnosis to detect early signs of diseases with great accuracy. Called Caption Care, the ultrasound device comes with CaptionAI technology to capture medical images in real-time and provide insightful diagnosis. The built-in software will automatically analyze the images to measure the key parameters like heart rate, etc. The company calls it a turnkey end-to-end echo program to offer cost-effective echocardiograms for identifying heart diseases in the initial stages to start treatment early and save more lives. Caption Health’s device helps with patient engagement by identifying them based on the echo reports, symptoms, risk factors, etc., to schedule doctor appointments. It can also be used to train healthcare trainees and aids with the management of patient data in a structured format. Moreover, the Caption Care device can be taken to the patient’s location for ultrasound while the doctor can watch the images in real-time from their clinic. The device can be used by patients and hospitals. CloudMedX CloudMedX is an AI product development agency that offers computing technologies and predictive analytics to streamline patient data. It aims to convert vast amounts of medical information into critical insights and unlock the full potential of patient data to make effective decisions. The company is a team of data scientists, engineers, innovators, and doctors from different countries who work together to simplify healthcare solutions and improve the quality of patient care. CloudMedX leverages machine learning and advanced analytics to empower hospitals and patients. It also works with natural language processing and LLMs to provide generative AI solutions to its clients. The company has an automation extraction engine to accelerate the data transformation pipeline. It can be hosted in multiple environments and customized for different healthcare centers. The company provides a range of solutions like Clinical Explorer, Patient Explorer, Chronic Conditions Explorer, Risk Adjustment Analyzer, etc. Enlitic Enlitic offers healthcare AI solutions with the power of deep

Manpreet Kaur
December 20, 2023
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Building AI Products From Scratch: A Go-to Guide to C-level Executives

This blog talks about the steps to help you in building AI products from scratch. With AI, you can create great products to optimize your processes, save extra bucks, and deliver exceptional customer experience. AI has become imperative for growth-driven businesses as it offers great ways to streamline business operations, boost efficiency, and fulfill market demands. It has made it easy to gather, analyze, and identify patterns in datasets, improving analytical functions. Further, organizations can take advantage of its benefits to automate processes, ensure optimal resource usage, and protect systems against fraudulent activities. According to Grand View Research, the global AI market reached a valuation of $62 billion in 2020 and is anticipated to have an annual growth rate of 40.2% from 2021 to 2028. Before building AI products, it is essential to understand the main components of AI. They include: Learning Procedures Collecting and processing data to convert it into meaningful information is the first and foremost step. Process datasets such as images and customer interactions to arrange them into meaningful formats to train AI algorithms to accomplish designated tasks. Reasoning Procedures Assess algorithms and find the most suitable one for a specific task. For instance, a decision-making AI system evaluates different algorithms and selects the most optimal results depending on analyzed data. Self-adjustment Procedures The AI product development team monitors and adjusts algorithms to deliver precise results. This helps to enhance the performance of AI systems over time and addresses emerging issues. How to begin building AI products? The important steps for the AI product development process are: Identify the problem Focus on the pain points and find out the value proposition that your AI solution will offer. Figure out the problem that your product will solve. Review your data Once pinpointing the issue, the next step involves selecting relevant data sources essential for AI model training. Ensure you have high-quality data to streamline the AI product development process. This is achieved through data cleaning and processing before using it to train the model. Select a viable platform This is one of the most important phases of AI product development. You can choose between cloud-based and in-house frameworks. Choosing the right platform is crucial for AI solutions to obtain great results. The decision between these two frameworks is one of the biggest AI product development challenges. Here’s help: Cloud frameworks AI cloud frameworks allow developers and data scientists to build, deploy, and control machine learning models in the cloud. They offer access to data processing, storage, training models, and deployments within a unified computing environment, making it easier to develop ML solutions. Cloud frameworks offer accessibility, and can be scaled horizontally and vertically to handle varying demands. They also offer inherent flexibility and can be integrated easily with other services. Some AI product development tools include Amazon Web Services, Google Cloud AI, IBM Cloud, and Microsoft Azure AI. In-house frameworks In-house AI frameworks refer to personalized platforms created by internal teams to cater to development and deployment needs. They fulfill unique business needs, data sets, and technology stacks to create value. Developing an in-house AI framework offers customization, data ownership, control, and privacy advantages. Some of the in-house frameworks include TensorFlow, Keras, Microsoft Cognitive Toolkit, and Tableau. Evaluate your needs and requirements when choosing an appropriate framework for your AI solution. Select a programming language You can choose from different programming languages such as R, Python, Java, and C++. Python and R offer extensive ML libraries. Create algorithms An algorithm is a set of instructions given to computers. To enable the AI model to learn from data, machine learning algorithms are necessary. The next step includes training the algorithm with the collected data. Optimize the algorithm to create AI products with high accuracy. Implement, Monitor, and Optimize This is the final step of the AI product development roadmap. Check if your AI product is feasible and self-sufficient. Assess the performance of the deployed AI models and ML algorithms. Observe and optimize them to AI product development benefits. Some of the important considerations include: What are the Challenges of Building AI products? Building and implementing AI products brings with it a few challenges. It is necessary to plan the implementation process meticulously and ensure the existing technology infrastructure is prepared for the challenges ahead. The effectiveness of AI algorithms depends on the quality and quantity of data provided. Therefore, businesses must ensure the quality of their datasets and should have sufficient information to train AI models. Inaccurate and insufficient information may lead to bad results, causing significant threats to business success. AI algorithms duplicate and augment prejudices present in the training data, resulting in discrimination against specific groups with serious consequences. Therefore, businesses must advocate ethical and legal standards to deploy AI products. Building and implementing AI products need a high level of technical expertise, organizations may need to train the workforce to improve technical skills internally or hire external staff. Though AI systems have improved data collection and analysis, there is potential scope for theft or misuse of information. Implementing security techniques is essential to protect the AI system against intrusions. Also, it is important to maintain trust in AI products, specifically for regulated industries such as healthcare and finance. The company must guarantee that the outcomes are transparent and reliable. Managing the hassles of AI product development is quite cumbersome. An AI Product development agency helps you to bring your AI product idea to life. The industry experts guide you through the step-by-step plan for your custom AI product design and development. They will help you at all stages of the AI product development lifecycle, ensuring the product vision aligns with your objectives. Fact checked by –Akansha Rani ~ Content Creator & Copy WriterSunaina Meena ~ Digital Marketing Specialist

Yashica Chopra
December 18, 2023
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10 Top Most Common Myths in Affiliate Marketing – You Might Get One of These!

Affiliate programs are a good way to earn extra money if you plan it well. You can generate good income by becoming an affiliate. Here, we’ll debunk the common misconceptions and myths in affiliate marketing and provide tips to get started. Affiliate marketing has been popular for a while now. Many bloggers, influencers, and website owners turn to affiliate programs to earn extra money. Statistics show that the global affiliate marketing industry is expected to touch $15.7 billion by 2024. Another report mentions that the affiliate program market is set to have a CAGR (compound annual growth rate) of 7.7% between 2023 and 2030. However, there are still many myths in affiliate marketing. While some think affiliate programs are a scam, others consider it an easy way to make some fast bucks. Both are wrong. Understanding and debunking myths in affiliate marketing is necessary to navigate the affiliate market and achieve your goals. Never get into a project without knowing its intricacies. In this blog, we’ll address the most common myths in affiliate marketing programs and discuss ways to become successful in this venture. 10 Top Most Common Myths in Affiliate Marketing Affiliate programs are a great side hustle for many website owners. Influencers combine social media promotions and affiliate programs to earn attractive commissions and monetize their content. However, many people are unsure of what it means to be an affiliate or how the programs work. It’s easy to make mistakes when you rely on misinformation. Let’s avoid these mistakes by debunking the top ten affiliate marketing myths. 1. Affiliate Marketing is No Longer Profitable Despite the statistics, some people like to believe that affiliate programs are not profitable and the entire concept is dead. They couldn’t be further from the truth. According to Statista, affiliate marketing in the US alone was $8.2 billion in 2022, with no signs of slowing down. Affiliate programs are from being dead or dormant. Many organizations from different sectors are offering partner programs to tie up with affiliates and build a strong network. For example, many companies offer various affiliate marketing programs with noteworthy commissions and other incentives. 2. Affiliate Marketing is Not Real Business The most popular myth in affiliate marketing you may have heard is that affiliate programs are not real business. A business doesn’t have to have a real store with a signboard in today’s world. Many modern businesses start online and are run from basements and study rooms at home. With eCommerce sales increasing every year, it makes full sense to become an affiliate and promote quality products on your website. With the right approach and planning, it is very much a real business and brings steady income over time. Don’t let anyone tell you otherwise! 3. Affiliate Marketing Requires Qualifications Some people think you need certain educational qualifications to be an affiliate. That’s never the case. Affiliate programs are not based on your degree. It doesn’t matter if you are a student or a multiple-degree holder. What’s important is your knowledge about the niche. This is also not a compulsion but an additional benefit. As long as you understand how the product/ services work and how your audience relates to them, your degree and experience aren’t a concern. Moreover, it’s personal experiences that matter. Your affiliate links will get more responses if you have used the products and can vouch for them on your platform. Nevertheless, don’t pretend to be something you are not. If you are an affiliate for medical or wellness products, don’t claim expertise in the field unless you do. 4. Affiliate Marketing is Easy Money One of the common myths in affiliate marketing is that it brings easy money. While some affiliates might have earned money quickly, it is not always the case. You are not likely to earn a thousand dollars or more each month during the initial stages unless you already have the necessary audience. In a majority of the cases, affiliate programs take time to show the results. You should plan well and implement your strategies. From selecting the best affiliate programs to building a relationship with your users, everything has to align with your end goal. Then, you will notice better results and gradually see an increase in your affiliate income. One way to speed up the process is to join big affiliate programs like the IT staff augmentation affiliate program by DataToBiz as they offer high payouts and recurring commissions. 5. Affiliate Marketing Doesn’t Require Planning No matter how easy affiliate marketing is, it requires proper planning and implementation. You should be willing to put in hours of your time and energy to see the results. It’s rare to add links to your website and earn from them right away. Plan every step with care. From choosing the niche to identifying the partner programs and creating relevant and useful content, everything has to be planned. Also, promote your website and optimize it for search engines to maintain a steady stream of visitors. 6. Affiliate Marketing is Not Good for SEO Another common myth about affiliate programs is that they aren’t good for your website SEO and you cannot survive Google’s algorithm changes. This leads to many affiliates asking if there is any risk in affiliate marketing. Well, not really. Affiliate marketing can be good for SEO if you do it right. Google is only concerned about websites with ‘thin’ content that do nothing else other than add affiliate links and ads. If your website has real content for users to read and enjoy, they will invariably spend more time on it. This increases your website rank and visibility. Placing affiliate links on such sites will be beneficial to everyone. Focus on content quality, user experience, and relevance. Make sure the affiliate links are clear. 7. People Dislike Affiliate Marketing Links This is debatable. People don’t like to click on any link for various reasons. Safety concerns about data privacy and cyber threats make them wary of clicking on hyperlinks when reading a post. However, that doesn’t mean they hate affiliate links. It depends on how you present the content to