Page 28 – DataToBiz

Shashank Dharasurkar
October 18, 2022
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Azure Data Engineering Services : Adapt to Changing Data Needs

AWS and Azure data engineering services are offered by top data engineering services companies to build, develop, deploy, and maintain a customized IT infrastructure on the cloud. Know more about them! Businesses today can collect enormous amounts of data. Analytics, traffic monitoring, and everything else depend on data. For handling such big data, businesses need an infrastructure that trains their personnel to sort and analyze this amount of data. That’s where data engineering services come into action. AWS and Azure data engineering services are offered by top data engineering services companies to build, develop, deploy, and maintain a customized IT infrastructure on the cloud. Businesses can partner with the service providers to streamline their data, systems, and processes to adopt the data-driven decision-making model. But what does data engineering mean? What is the role of a data engineer? Let’s find out. What are Azure Data Engineering Services? The term data engineering is the process of creating systems for almost all industries that collect and manage information. In other words, data engineering is the process of sourcing, transforming, and managing data from different sources. Data engineers mine data for insights. Their skill set allows them to construct architectures for extracting value from data, which are then applied to benefit a company. As a result, data is accessible and useful. An essential aspect of data engineering is the practical use of collected and analyzed data. Thus, data engineering uses different methods to gather and authenticate data, ranging from data integration tools to artificial intelligence. The same applies to data engineering services; sophisticated processing systems get designed and monitored to put found data in realistic situations. Essential Data Engineer Skill Set for Azure Data Engineering Services SQL A data engineer must be proficient in SQL as a foundational skill. The SQL language is essential for managing RDBMS (relational database management system). To achieve this, you will have to go through practicing many queries. To learn SQL, you don’t need to memorize a query. Learning how to optimize queries is crucial. Data Warehousing Understanding how to build and use a data warehouse is an essential skill. Using data warehouses, data engineers can collect unstructured data from several sources. After that, the information gets compared and evaluated to improve a company’s efficiency. Data Architecture For businesses to build complicated database systems, data engineers must have the necessary knowledge. Data engineering services & solutions include data architecture as a core offering. The term refers to data operations, which handle data in motion, data in rest, and datasets, with the relationship between applications and data. Programming Skill It is essential to improve your programming skills if you want to link your databases and work with different types of applications such as web, mobile, desktop, and IoT. To achieve this, you will need to learn a language that is suitable for enterprise use, such as Java or C#. Both are useful as part of open-source tech stacks, and the latter is helpful in Microsoft-based stacks for data engineering. Python and R, however, are the most important ones. Python can be used for various data-related operations with an advanced amount of knowledge. Data Analysis Data science is mostly associated with machine learning. A data engineer will be in a better position to excel if they understand how data can be used to analyze and model data. Having an understanding of the basic concepts will help you to better understand data scientists’ needs. Who are Azure Data Engineering Services Experts? With the help of data engineers, companies can replace their in-house data infrastructure with a robust information pipeline and transform their data into insights for business analytics. Across industries and businesses, data engineering services are now gaining popularity as a tool to extract valuable data. Not just Microsoft Azure, but data engineering services in AWS are also in high demand. In fact, Azure, AWS, and Google Cloud form the top three cloud platforms in the global market. With these services, you can ensure that valid data will be available at the right time, in the appropriate format, and in the right place. Azure Data Engineering Services: Roles and Responsibilities The following are some of the roles and responsibilities Data Engineers need to perform: Work on Data Architecture Data architects use a systematic approach in planning, creating, and maintaining data architectures while aligning them with business needs. Collect Data Getting the appropriate data from valid sources is the first step in building a database. The storing process of optimized data begins after data engineers plan a set of dataset processes. Conduct Research Data engineers conduct research in the industry to find a solution to a business problem. Improve Skills Theoretical database concepts aren’t enough for data engineers. They must have the knowledge and expertise necessary for successful development. Furthermore, they need to keep up with various machine-learning algorithms. They should have expertise in analytics tools like Tableau, Knime, and Apache Spark. These tools allow businesses to generate valuable business insights. Furthermore, a data engineer should also offer big data engineering services to handle vast amounts of data in real-time. Create Models and Identify Patterns In order to extract historical insights from data, data engineers use a descriptive data model. They use forecasting techniques to gain actionable insights about the future while developing a predictive model. Additionally, they provide recommendations for different outcomes using their prescriptive model. Why Do Modern Businesses Need Azure Data Engineering Services? Data Science tends to be the only way organizations can gain meaningful insights from their data. Companies can, however, build large, maintainable data reservoirs through Data Engineering. Data Science and Data Analytics can obtain useful results from these design data processes that are scalable. In order to enhance the efficiency and effectiveness of data analytics, accurate and reliable insights must be provided. Using AI and ML, companies are able to achieve higher efficiency, become agile, tap into new market opportunities, launch new products faster, and provide better service to their customers. Yet, according to an MIT Tech Review survey, 48% of

Drishti Patel
October 14, 2022
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6 Reasons To Hire a Machine Learning Developer

Artificial intelligence and Machine Learning are undeniably transforming and integrating themselves into every aspect of our everyday lives, from entertainment and social interactions to the workplace. Artificial intelligence, according to experts, will advance a company into the next generation, revolutionize data management, and improve the whole consumer experience. There are many business applications for AI and machine learning, and as these applications become more widespread and sophisticated, we can only anticipate an expansion of this list. 6 Reasons to Hire a Machine Learning Developer Here are a few of the reasons you should hire a machine learning developer: 1. Supports Real-Time Decision Making Big data’s potential is expanding quickly, thus you must connect users and providers (buyers and sellers) instantly. Businesses must instead use data and analytics to quickly determine the best course of action based on the current situation. Your consumers will appreciate personalized services, which will increase profits. A machine learning developer helps businesses achieve real-time decision-making by drawing insightful conclusions from the data. 2. Enables Automation That Replaces Manual Efforts ML technology advances industrial automation by replacing manual jobs that incorporate unpredictable elements like changeable parameters, external causes, and internal system changes. Industrial automation in the 20th century focused on employing machines to reduce predictable and repetitive human tasks. An ML developer gives businesses the ability to make quick judgments and automate operations by creating predictive data models. One such is Adobe Target‘s Auto Target, a potent AI technology that employs machine learning to create automated tailored experiences. Personalization in Adobe Target is based on a set of experiences chosen by the marketer. 3. Effectively Address Security and Network Challenges Cyberattacks, network breaches, and other security flaws sometimes occur in real-time without warning, giving users little opportunity to take action. One of the biggest cyberattacks in history occurred in 2018 when a series of DDoS strikes targeted GitHub and Arbor Networks in the US. Any network intrusion must be promptly detected and stopped by enterprises in order to avoid service interruptions and data leakage. ML developers are capable of creating algorithms that are capable of tracking network activity to quickly identify anomalies so that preventative steps can be taken. Additionally, as ML algorithms take the place of manual investigation and analysis, they self-learn and adapt to change, which further enhances the condition of cyber-security. 4. Improves Marketing Results The branch of AI known as machine learning focuses on creating intelligent machines that can operate independently of human intervention. Additionally, a machine learning developer helps businesses save time and money in the marketing sector as well. In marketing initiatives, machine learning can be an amazing and potent multiplier. Constant messaging, trends and changes in consumer behavior, targeted marketing techniques, etc. are all made possible by machine learning engineers. Social media websites can be used to develop micro-marketing campaigns. 5. Manages Unstructured Data Large amounts of unstructured data can take a long time to clean up. Any organization that evolves over time gathers a lot of fresh information. For instance, if you are in the air freight forwarding business, you will need to process large amounts of data on services, rates, etc. within a short period of time in order to evaluate it and obtain insights about potential future patterns. Managing and removing massive information indexes is one of machine learning’s most important applications. The information is easier to process, obtain, and retrieve thanks to machine learning. 6. Increasing Customer Satisfaction Currently, machine learning is used to improve customer experience, which results in increased customer loyalty. It is done by looking at the customer’s buying habits. Many organizations all over the world already use this technology. According to research, 76 percent of business owners agreed that their profitability increased as a result of incorporating machine learning into their processes to understand client behavioral patterns. Google and many other large websites are using this technology. You may have observed that when using websites like YouTube, LinkedIn, etc., you frequently receive recommendations for various goods to buy or individuals to connect with. With the aid of machine learning, the ads and friend suggestions are handled. How To Hire A Machine Learning Engineer When hiring a machine learning engineer, who can be a valuable asset for your business, it is advised to exercise extreme caution if the company is investing in the development of artificial intelligence and machine learning. As a result, there are some essential factors to consider when hiring a machine learning engineer or specialist. What to take into consideration when you want to hire a machine learning developer is covered in full below. 1. Technical Skills For these professionals, full-fledged technical skill in coding and programming is essential in order to tackle such assignments. Here, you must check the applicant’s technical specialization and administer the coding test to confirm that she/he is capable of overseeing the creation of AI and ML models. Make sure you have qualified professionals to hire the best applicant so you can evaluate their technical talents. A machine learning developer should have knowledge of languages like python, frameworks like Pytorch or TensorFlow, algorithms like Scikit, and many others. Make sure you test all the necessary technical skills before hiring. 2. Interactive Communication Skills The ability to communicate with others, which is necessary for collaboration with colleagues, should be taken into account when trying to hire a machine learning engineer. Before beginning the project, AI and machine learning professionals should agree on a framework for communication with the company. Therefore, if you are concerned about a candidate’s communication skills, you should assess their capabilities to ensure effective communication. Wrapping Up Finding a suitable AI or machine learning professional would be difficult and time-consuming. To prevent confusion between data analysts, data scientists, and machine learning engineers, it provides basic stages of vacancy description with a precise set of responsibilities. In order to recruit a machine learning engineer that is an expert in his field, one must have a complete understanding of technology and competence. So, to

Aditya Kumar
October 12, 2022
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Why Hire an AI Software Development Company?

Whether or not a business project is a success largely depends on how solid the technical team is. Today, AI development is faster than ever, pushing businesses to gain a competitive advantage. Every project is unique and so are its requirements. A company must choose the most convenient and efficient model for AI software development, depending on the project’s scale, features, goals, and budgetary constraints. However, there is also a slew of factors that determine the success of a project. Factors Responsible for Outsourcing AI Development Needs of a Company Requirements Before you decide whether to outsource a project or handle it in-house, clearly assess the project goals. What are the features you are looking for in your project? What are the services you will require for the successful execution of the project? When are you planning to launch the project? Next, you need to assess if an in-house team has all the necessary skills to fulfill your expectations from the project or if an external agency can provide you with the level of customization you require for the project. Budget Maintaining a budget is one of the most crucial aspects of a business. So you need to figure out how much capital you can invest into developing your project. Next, calculate the expenditure required for in-house handling and outsourcing and choose the option that best fits your budget. The Results It is one of the most crucial factors you need to consider to decide which of the two options is more viable for you. Compare the services that your in-house team will offer with an external agency. Understand the complete scope of work and the exact services you will be paying for. Now, the next question that might come into your mind is, “How do you ensure that your project idea is executed in the best possible manner? Do you hire an in-house software development team? Do you partner with an AI outsourcing agency?” Let us deep dive into the pros and cons of both these options so you can decide what suits your company the best. Advantages and Disadvantages of In-House AI Development In-house AI development means hiring team members within your organization and training them with the crucial IT skills and abilities needed to handle the project. Although this method is more cost-effective in the long run, it can seriously load the company’s budget in a shorter span. Disadvantages of In-House AI Project Development Lack of Expertise In-house AI developers demonstrate lesser expertise and the skill to develop AI-based ideas as compared to ones in an agency. They do not have an interdisciplinary perspective which is a prerequisite for creating meaningful models for making business decisions and getting faster results. Need for Resources Hiring an entire team is even more difficult than it sounds. As an organization, you must equip yourself with all the resources such as infrastructure, overhead, additional employee benefits, training fees, etc. making an in-house team an expensive affair. Time Crunch Hiring an in-house AI development team is an extensive process. From evaluating potential team members to recognizing the skill gap of every member, training every member is a long process. If you run out of time to execute a project, it is natural to not go with this option. Advantages of In-House AI Project Development High level of Personalization By developing your own AI solution, you can remove all the unnecessary features and complicated settings and bring down costs just by sticking to the product requirement. Ownership of Intellectual Property (IP) If your company invests in developing advanced technology like AI by building an in-house team, you can gain patent rights for the value you add in this field. It can give you an edge over your competitors in the industry. Advantages and Disadvantages of Outsourcing AI Project Development Outsourcing means your company partners with an external AI development agency and delegates the project execution job. Though outsourcing technical projects can be expensive, it is worth the investment. You get access to domain experts with a lot of experience working on similar projects. It is especially beneficial if you do not want to wait too long to launch your product in the market. Advantages of Outsourcing AI Project Development Instant Access to High-Quality Data Tons of high-quality data are needed for developing machine learning and building deep learning models. Even if your business has access to big data, working with such massive data requires a lot of experience and is time-consuming. Only an external AI development agency can bring such expertise to the table. Before starting any AI project development, proper data cleansing and preparation are prerequisites. An outsourcing agency can feed a Machine learning platform with high-quality data sets to leverage it to your advantage. Outsourcing Is a Remedy for Invisible Costs Outsourcing might be expensive, but you pay for the skills and expertise. But while building an in-house team, you pay in terms of both time and money for additional things apart from the skills and expertise. Everything is an additional cost to the company, from recruitment and software licenses to training and employee benefits to providing refreshments. Also, hiring people in technical fields such as AI is tough. A lot of time is needed to search, interview, recruit, and onboard new employees. Your company can save all of this time and capital just by partnering with an outsourcing AI agency. Scalability and Flexibility If you are a fast-growing startup in a highly competitive industry, you need to scale dynamically to meet market needs. In such a situation, the in-house team” approach might not be helpful, especially in challenging, specialized projects. Also, if you are a small company, you probably do not have the resources needed to implement all the in-house services and systems. So to ensure that the quality of the final product is not compromised and to compete with the big players in the market, outsourcing is a better option. You can achieve high levels of scalability and

Sunaina Agarwal
October 10, 2022
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How To Choose Data Science Consultants (Buyer’s Guide)

The advancements in emerging technologies like AI, ML, and IoT are increasingly driving companies to adopt different platforms and software. And with technology, the demand for data is also growing. Companies are using data for enhancing business operations and revenue. Data science has also become imperative for companies in their quest for competitive advantage. According to reports, “The Data Science Platform market size is projected to grow from USD 95.3 billion in 2021 to 322.9 USD billion in 2026, at a Compound Annual Growth Rate (CAGR) of 27.7%”. What is Data Science Consulting? Data science is considered a means of enhancing a company’s ability to identify patterns and trends in its data. To do this, companies hire a data scientist or a data science consulting firm that uses different techniques, including statistics, machine learning, and artificial intelligence to optimize brand performance with the most promising results. Data science is integral to modern-day business, and its importance cannot be understated. As companies and individuals grapple with the ever-growing demand for data, the need for data consultants is also growing. These consultants help organizations collect, process and use data to improve decision-making and operations. They can also help create new insights and models to facilitate the best business processes. How Does Data Science Work? Companies are beginning to realize the importance of incorporating data scientists into their organizations. Not only are technology companies utilize data science, but even industries like healthcare, retail, finance, transport, and many others are engaging customers with data science insights. For example, the application of data science in the healthcare industry is being used to improve patient care and develop new treatments. Banks are utilizing data science to prevent fraud and detect money laundering. Retailers use it for a better understanding of customer behavior and to create targeted marketing campaigns. Even the hospitality or eCommerce industry uses data science to target customers with their campaigns, offer discounts, and much more. Hence, data science is being used extensively by different companies for various purposes. Some companies use data science to improve their product or service, while others use it to gain a competitive edge in the market. Data science is a versatile tool used for a variety of purposes. But, before you start to apply data science for company growth, companies must consider a few things. The first step is choosing an experienced data consultant or data science consulting company. But, are you wondering how you can choose the best data consultants for your enterprise? Let’s have a look below: How To Choose Data Science Consultants? Choosing the right data science consultant can be an overwhelming task. Multiple qualities and skills must be factored in when selecting a consultant to determine who is best suited for the job. Some of them include: Experience and Background Data science consultants work with businesses to help them understand and organize data. They can come from different backgrounds, but they all share a love of data and analytics. Yet, they have an experience that can vary depending on the consultancy. Some data science consultants have a background in statistics, machine learning, or data engineering. Others may have worked in a specific industry and know how to apply data analysis techniques to that domain. So, it is critical to find a consultant who has the right skills for your project and who can comprehend your business needs. A consultant’s experience will also affect the type of services they offer. For example, a consultant may provide strategy services, such as helping you define your goals and identify the best data sources, or they may cater to implementation services, such as building models or pipelines to extract insights from your data. Thus, before appointing, you must understand their experience and how it can benefit your business. Skills Companies look for consultants who help them make the most of their data. So, companies must look for a skilled data science expert who: Analytical Knowledge A data science consultant can help organizations use data to make informed decisions. And with that comes a need for consultants who can help businesses use analytics to their advantage. Data analytics is crucial for an Organization to understand customer behavior, trends, and preferences; optimize marketing campaigns; determine where to allocate resources; identify opportunities and challenges. In addition, data analytics can play a significant role in developing new products and services. Therefore, enterprises must look for a data science consultant with a strong understanding of analytics principles and tools. It includes knowing how to gather, analyze and interpret data. The consultant should be someone who can understand how data flows within a company and has the know-how to put together complex analysis plans. They must further be able to recommend solutions that help the organization meet its goals. Technical Abilities Choosing the right data science consultant is critical to successful data analysis and inference. The data scientist must have technical knowledge including but not limited to data modeling, data warehousing, data visualization, and database design. The data scientist must have technical proficiency in areas such as: Furthermore, the consultant should also be able to build predictive models to help the organization make informed decisions quickly. Data scientists must have an excellent problem-solving capability and strong programming familiarity with at least one of the following- SQL, R, SAS, Tableau, or other software. Thus, when selecting a consultant, also choose someone who will be able to recommend the appropriate technologies to meet your business requirements. Big Data While looking for consultants, companies must look for data scientists who have an experience in big data. Why? Because big data will help consultants manage and process large amounts of data in your company. This technology can help data scientists find patterns and insights that they would not be able to find otherwise. Big data technology can help data scientists to automate particular tasks, which can speed up the process in your company. Also, prevent fraud and improve the accuracy of their predictions. Therefore, these are some

Mayank Wadhwa
October 04, 2022
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6 Advantages of Power BI Dashboard: Should You Consider It?

If you want to scale your business and make the most of data, Power BI is one of the best business intelligence tools you can bank upon. Why? Well, unlike other Business Intelligence tools, Microsoft’s Power BI has a better GUI, which is easier to understand and requires no coding at all. Just drag and drop some features, and you can create interactive Power BI dashboards within minutes. Also, Power BI is quite affordable, which makes it ideal for small businesses and startups with limited resources. Want to know more about such Power BI advantages that make Power BI a reliable BI tool? Read along. This blog talks about the top advantages of the Power BI dashboard along with its use cases in different business domains. Top 6 Benefits & Advantages of Power BI Dashboard Power BI Dashboard offers numerous benefits and advantages that make visualizing complex data easy. Here are some of them: Easy to Use Power BI offers you a pretty user-friendly interface that makes the entire dashboard creation process easy. So much so that you do not even need to write a single line of code to create them. Just drag and drop the features, and you’re good to go. What’s more, there’s an in-built intelligence in this tool that recommends the right reporting element based on your choices. For instance, after choosing the data source if you opt for sales and location, Power BI will automatically identify the map chart. That’s how smart it is. Low Learning Curve As stated earlier, the Power BI dashboard requires no coding; it’s easy to use and master. Also, Power BI was developed on the foundation of Microsoft Excel, which further lowers the learning curve when it comes to creating Power BI dashboards. If you know EXCEL, you’ll face no issues using the Power BI dashboard. P.S. Power BI offers learning guides if you want better information on how to use it. Customizable Dashboards Power BI offers amazing customization when it comes to creating and sharing dashboards. You can create Power BI HR Analytics Dashboard to simplify the HR process, a Power BI for Banking Dashboard for analyzing finances, or Power BI Marketing Dashboard to determine the success of your campaigns. So, regardless of the business domain, you belong to, you can easily customize the Power BI dashboard as you like. What’s more, you can even integrate the Power BI dashboard into your website or application to offer a better and more unified user experience. Numerous Data Sources Business Intelligence tools help analyze huge amounts of data that come from various data sources. However, most BI tools are limited when it comes to data sources. That means they accept a set number of data sources to pull the data from, which can be a problem. Fortunately, Power BI solves this problem. When you’re creating Power BI dashboards, you can pull data from almost any data source, such as: The above list of data sources is just an example of how extensive Power BI is when it comes to pulling data from data sources. In its true sense, there are even more data sources that you can choose from. Cost-Effective Power BI is quite an affordable Business Intelligence solution. The desktop version of Power BI is free, and you can create immersive dashboards and reports both simple and complex without paying a penny. While you may have to pay for accessing more features like sharing the Power BI dashboards, it’s affordable (around $10 a month). Q&A Function The Q&A function is probably one of the best advantages of the Power BI dashboard. To get actionable information, most BI tools require you to follow a number of complex steps and even coding. However, that’s now how Power BI functions. The Q&A feature Power BI comes with allows you to ask questions using a natural language and get the information you want. For instance, if you want to check the number of shoes sold. You can simply visit the Q&A section and enter “shoes sold by the state as a bar chart” This will display the information you need in the form of a bar chart. How Can You as a Business Benefit from Power BI Dashboard? Imagine you’re selling shoes/t-shirts on your website globally, and receiving thousands of queries and sales each day from customers. Also, you’re running marketing campaigns to improve your sales. Now, how will you answer the following questions: Well, if you have a few hundred customers, maybe you could manually check the data to answer these questions. However, if you have thousands of customers to deal with each day, manually checking the data would be a bad decision as it would be both time-consuming and ineffective. And this is when Power BI comes in. With Power BI Dashboards, no matter how much data you deal with each day, you can easily answer all of the above questions within minutes. You just need to select the data source, select the attributes, do some dragging and dropping, and you’re done. Power BI Dashboard basically helps you structure all types (structured, semi-structured, or unstructured) of data into a visually immersive and easy-to-understand format. And once you can understand the data easily, you can make better, well-informed, and data-driven decisions that benefit your business. What are the Use Cases of Power BI Dashboard? Regardless of which domain you belong to, if you have to deal with lots of structured, semi-structured, or unstructured data, Power BI is for you. So, in this section, we’ll talk about different departments within a company that uses or can use Power BI Dashboard. Here are the top most common use cases of Power BI Dashboard: Management The c-level executives can use the Power BI dashboard to understand operational KPIs such as profit, loss, net revenue, customer retention, etc. Dashboards also give insights into investor summaries and financial overviews. Here’s what a management Power BI Dashboard looks like: Finance The CFO and other folks in the finance department can have an overview of

Shashank Dharasurkar
September 29, 2022
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5 Top AI Companies in Manufacturing Industry (Updated)

AI has taken the world by storm. With industries like banking, education, gaming and retails being transformed by AI, it’s no surprise that the manufacturing industry is next. From expertise shortage to automating machines, integrating processes, and overloading information, AI help conquers many internal challenges. Leveraging AI in manufacturing helps company transform their business completely. Let’s explore how top AI companies in manufacturing industry are paving the path for automation and digital transformation. What Is AI In Manufacturing? AI in manufacturing is, adding advanced technology to the current manufacturing process. It can get used to automating complex tasks and trying different manufacturing patterns for a fast workflow. Without artificial intelligence, it would take hours to complete a task that an AI system could do in seconds. Furthermore, it will also affect work efficiency. In many cases, it is difficult for humans to detect defects in a product because they are not visible to the naked eye. On the other hand, manufacturers that adopt AI use it to improve equipment efficiency in production, uptime, and better prediction. Manufacturers can track their facilities in real-time. Thanks to critical Fourth Industrial Revolution (4IR) technologies. It includes ML, automation, advanced and predictive analytics, and IoT (Internet of Things). McKinsey conducted a survey which results that the 4IR technologies are capable of generating approx. $3.7 trillion in value by 2025. AI has the potential to generate $1.2-$2 trillion in value only in manufacturing. AspenTech research shows that 83% of large industrial companies believe that AI can produce better results. It also suggests that domain expertise is core for adopting AI models into the manufacturing industry. To stand up in this competitive race, manufacturers have to adopt a data-driven business model. This includes skilled staff, advanced hardware, and ai software. How AI is Changing Manufacturing? The need for 4IR technology will lead manufacturing businesses into the world of digital factories. The IFR (International Federation of Robotics) report shows that there are 2.7 million robots currently operating in factories worldwide. And with the increase in digitization, this trend is growing in speed. In the recent global epidemic, some manufacturers adopted technologies to make their businesses more flexible. That includes automating operations and ease of end-to-end control over all operations. Many manufacturers are still trying to adopt AI and ML-like modern technologies to reduce production costs and increase time-to-market. Following are some of the solutions of AI and ML that most manufacturers have adopted. Workplace Safety This is one of the most common places where manufacturers can use artificial intelligence. AI gets used for workplace screening and safety. This technology was most useful during the pandemic time. AI is used to identify employees’ health status using thermal screens. AI monitored employee interaction and contact for workplace sanitization in case of emergency. Also, the same system can help keep employees healthy, safer workplace, and continue work in the future. Machine Maintenance All manufacturers always try to maintain their important production machinery. AI and ML are useful in advance maintenance management. It helps manufacturers to shift from regular maintenance to predictive maintenance. It will result in a large amount of reduction in regular maintenance efforts, annual maintenance costs, and part maintenance. With the help of AI software, hardware sensors, machine data, and AI, the maintenance team can identify the major failures. It also helps in predicting future failures. Security Research suggests that manufacturers experience a lot of damage during cyberattacks. As production industries are increasing the number of IoT devices in their factories, it affects the growth in cyberattack chances. Smart factories are getting on target for cyberattacks. AI-based cybersecurity software and risk detection can help in securing production factories. It can also help in reducing attack risk. Manufacturers can use self-learning AI software to secure their IoT devices and cloud services. As this AI can spot attacks and interrupt them in seconds with accuracy. The system can also alert and provide guidance to prevent further damage. Benefits Of AI In Manufacturing Manufacturers are preferring AI robots in their business. The ultimate aim is to provide a safe workplace and increased efficiency. Following are some benefits of AI in manufacturing as well as in AI as a service. Cost Reduction in AI solution The major advantage of AI as a service in a company is that it allows the reduction of the development cost of AI solutions. You only have to pay for what you need. IBM Watson and Google cloud storage are the best examples of AI as a service. As they charge businesses as per their use. Quality Control The key advantage of AI and ML in the manufacturing industry is quality control. Advance machine learning models can get used to differentiate normal design and faulty design. ML model can identify the minor faults that humans can’t notice. Adding such systems into the quality assurance section will increase product quality and also save time and money. 24/7 Production With the rise of worldwide product demand. If any production facility plans to work continue round the clock, they need to create different work shifts. They have to use 3 human workers for every 24-hour period. At the same time, robots don’t get tired or feel hungry. They are capable of working 24/7 in any condition. This will affect an increase in production capability and manufacturers can meet the product demand. Additionally, robots are more efficient in picking and packing sections. They can help in reducing the required time in many areas. Top AI Companies in Manufacturing Industry A case study shows how manufacturing companies like Micron Technology have faced mechanical issues while developing their product. And how AI technology adoption has saved their hours of downtime and Avoided the loss of millions of USD through early detection of machine breakdowns and quality issues and a 10% increase in manufacturing output. Similar to that, the following are some AI companies that help manufacturing industries to adopt AI technology in their business. 1. Automation Anywhere In 2003, Automation Anywhere, headquartered

Mayank Wadhwa
September 29, 2022
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50+ Best Resume Parsing Software and APIs

Tired of manually going through hundreds of resumes each time you post a job on social media or job boards? Well, you’re not alone. Recruiters across the globe face this problem. However, there is a solution: a resume parser or a resume parsing software! But what is resume parsing, and how can it help? Read along to find out. This blog talks about everything from what resume parsing is, how it works and how it can help recruiters. In addition, you’ll find a curated list of the 50 most reliable resume parser APIs and resume parsing software. This way, you can compare different tools and choose the best based on your needs. What is Resume Parsing? Resume parsing is a process of extracting a candidate’s crucial information (name, email, experience, technical proficiency, etc.) from resumes using a resume parsing software or API. This extracted information is then displayed in a structured and more readable format which helps recruiters choose the right candidate for the job. How does Resume Parsing Work? Basically, the resumes are uploaded or automatically picked from job boards or emails by the resume parsing software. The tool then processes one or multiple resumes, extracting all the information you specified and presenting it in a readable format. Usually, resume parser API or software can be linked to your existing ATS, which puts all the information you need in one place. Why do you Need Resume Parsing Software? Here’s why: Saves Time If you have 5-10 resumes, you can quickly cycle through them and choose the one you see fit. But what if you have hundreds or thousands of resumes? Well, that’s when resume parsing software comes in. Using resume parsing software, you can bulk upload hundreds or even thousands of resumes and get all the information in a structured form. And some tools, such as HireLakeAI, match the most suitable resume with the job description, making hiring easy and time-saving. Better Candidate Experience Manually dealing with hundreds of resumes takes time. And this is equally inconvenient for the recruiters and candidates. Because recruiters have to deal with hundreds of resumes manually, candidates have to wait for quite a long time. And some candidates end up getting no response at all. However, resume parsing software allows you to process hundreds of resumes almost instantly. This helps candidates get prompt responses all the time, improving the overall candidate experience. Better Fit Hiring It can be an overwhelming experience for recruiters when they must go through several hundred resumes for a job role. And because of this, deserving and best-fit candidates are often ignored. However, with automated resume parsing, you can make sure every resume or candidate is considered. This way, you won’t miss any worthy candidate, and the likeliness of hiring a talented or better-fitting employee increases significantly. Best Resume Parsing Software and APIs 1. Sovren Sovren is a resume parsing software that’s not only fast but highly accurate. Compared to its competitors, it’s 10X more accurate and 5-25x faster. You can upload the resume in any format, and you’ll get structured candidate summaries on the other side pretty fast. Sovren comes with an API and complete SDK that you can integrate into your application or your website and enjoy its accurate and fast resume parsing. Pricing: There’s a free trial you can enjoy, but the paid plans start from $99/month (for 500 documents). And the price increases as the documents you parse increase. 2. HireLakeAI HireLakeAI is an AI-based one-stop solution for all your hiring needs, from screening candidates and evaluating them to finding the best candidates. Using HireLakeAI’s resume parser, you can extract the desired information from videos or documents and convert it into a more readable and structured format. HireLakeAI’s JD Matching helps you find and match the most relevant resumes for a particular job posting. This way, you can choose the right talent more efficiently. Pricing: You can try resume parsing and the job description matching solution for free before making the right decision. So, you have nothing to lose. For custom pricing, you can connect with the team. 3. HireXpert HireXpert is an amazing resume parsing software solution that makes hiring smarter, faster, and fairer. You can bulk upload CVs in different formats such as pdf, zip, doc, Docx, etc. And within seconds, the tool will extract the necessary information and display the job-match score streamlining the hiring process. Pricing: HireXpert offers free resume parsing. 4. TurboHire TurboHire is a complete ATS or applicant tracking system that offers numerous features such as job management, email management, scheduling interviews, and, most importantly, resume parsing. With just a few clicks, you can extract email, phone number, educational background, skills, and experience. What’s more, TurboHire allows you access to AI-enhanced information that helps you understand the candidates’ competencies. Pricing: While there’s a free trial, the paid version starts at $45/user/per month. 5. Inda.ai Inda.ai is a dedicated and advanced resume parsing software that uses document layout analysis (computer vision), language detection, and OCR to ensure accurate resume parsing or information extraction. This tool can extract information from resumes as well as other types of documents. Inda.ai supports formats such as doc, pdf, Docx, txt, odt, pptx, HTML, JPG, RTF, JPEG, TIF, PNG, TIFF, etc., and is easy to integrate. Pricing: You can schedule a demo to learn more about this tool and determine the pricing. 6. Freshteam Freshteam, developed by Freshworks, is an HR automation software that can make resume parsing pretty easy. Once you set it up, the tool automatically sources applications from different channels, parses the resumes, and populates the data into an easily readable candidate profile. You can then score or rank the candidates and proceed further with your hiring process. Freshteam also allows you to streamline onboarding, and off-boarding, manage employee data, and human resources workflows. Pricing: Freshteam has a 21-day free trial. And the paid version starts at a $71 platform fee/month plus $1.20/employee/month. 7. Zoho Recruit Zoho Recruit is an applicant tracking system that’s cloud-based. You can create job postings across

Zachary Amos
September 27, 2022
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4 Best Computer Vision Use Cases for Solving Business Challenges

Computer vision is a field of artificial intelligence (AI) that can be found in an increasing number of business use cases. It enables machines to “see” objects and interpret them as humans do. Computer vision services specialize in many types of vision, including detection, identification, and supervision. Here’s a quick rundown of both how the technology works and how businesses are using it to overcome challenges and optimize workflows. How Computer Vision Works All computer vision devices and applications utilize AI, which is a simulated form of human intelligence and the basis of all machine learning. Just as a human sees an image and processes an appropriate response, the AI in computer vision detects digital images, analyzes the data, and processes a response in the blink of an eye. For a computer vision system to work effectively, developers must expose it to thousands of images with definitive traits, such as names, physical features, and labels. Pattern recognition is the name of the game. Once the system acquires a knowledge base of its particular environment, it can automatically take over detection, identification, and supervision responsibilities without human assistance. Computer vision is gaining a foothold in various industries, and business leaders have many opportunities to use the technology and get ahead of their competitors. Here are four of the most influential applications and real-world examples of how computer vision can solve companies’ challenges. Computer Vision Use Cases for Solving Business Challenges 1. Object Detection Object detection is the most well-known and widely used computer vision function. A system utilizes its AI network to identify specific objects in various settings. It eliminates much of the programming and guesswork that previous detection devices relied on, allowing for quick and accurate detections without human supervision or intervention. Self-driving cars are perhaps the most well-known examples of AI-powered object detection, but that technology has seen mixed results in practice. These other real-world applications have been more successful: Object detection is just the first step in a computer vision service’s analysis. It also classifies, monitors and verifies objects based on what the AI has learned. This technology will only get more accurate as it continues to gather information. 2. Optical Character Recognition On a smaller scale, computer vision services use optical character recognition (OCR) to identify letters, numbers, and other symbols from images. The most mainstream example of OCR that you might have already used is Google Lens. This tool enables users to pull fragments of text from digital images and printed documents and even translate foreign languages from photographs. OCR has also been a valuable tool for several key societal institutions, including education, finance, and government. Universities can scan and extract text from obscure historical documents and make more accurate translations, preserving precious knowledge from the past that otherwise might have been lost. Banks and credit unions offer OCR services so customers can scan their checks and credit cards without coming into the office. Other machine-readable personal documents — passports, driver’s licenses, green cards — also utilize OCR to help governments with border security and identification. Virtually every scannable item you can imagine can benefit from OCR, so your business is sure to find a relevant use for it. 3. Risk Management Employee safety should be the top priority for all businesses and industries, but equipment and regulations can only do so much. A computer vision system can help create a safer work environment by tracking worker activity, including how they use heavy machinery and navigate the work site. Safety-oriented computer vision applications are most common in dangerous settings, such as construction sites and warehouses. Workers use wearable AI devices to monitor their physical conditions, while supervisors use drones and cameras to identify hazards and ensure everyone follows required safety procedures. The health care industry has found a similar use for computer vision services, providing patients with wearables that automatically monitor specific conditions and send feedback in real-time to their doctors. This trend arose by necessity during COVID-19, as many people were either unable or unwilling to schedule in-person appointments. Computer vision is also a key part of some in-house hospital equipment. Medical professionals can train AI to identify the early stages of illnesses through X-rays, MRIs, and CT scans. 4. Image and Video Restoration Modern cellphones, cameras, and editing tools allow people to make drastic changes to images and videos, but none of those devices come close to computer vision’s editing capabilities. Computer vision services restore pictures and videos with extensive damage and decades of deterioration. AI in computer vision services evaluates the missing or damaged parts in the original image or video, reads a generative model of the same media, and fills in the gaps to recreate the scene. Some take the restoration a step further and build 3D sets of real settings, such as archaeological and environmental professionals. Computer vision’s restoration abilities have also proven valuable in the court of law, as forensic specialists can perform more immersive crime scene reconstructions and thus solve many cases. Computer Vision Has Massive Potential These applications of computer vision services are just a handful of successful examples. This technology has massive potential to transform many crucial industries, from health care to finance to construction. Developers are still working out some of the kinks, but business leaders should embrace this technology and add it to their operations in any way they can.

Rohit Narain
September 26, 2022
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How Connected Cars and Quantum Neural Network Can Help Drivers in Emergencies

Quantum neural networks are built based on quantum computing and classical physics to deliver accurate and reliable predictions. Our proposed model will make cars self-capable to handle emergencies. We’ll discuss the internal working and modules of our quantum neural network model for connected cars. Quantum neural networks are created using the principles of quantum mechanics. These are typically feed-forward networks where the information collected in the previous layers is analyzed and forwarded to the next layer. Deep neural networks (DNNs) are already used in developing autonomous vehicles to define the right driving behavior for a vehicle. A Quantum neural network aims to assist drivers in effectively handling emergency situations. Providing emergency support to car drivers using connected cars and quantum neural networks can reduce the risk of accidents and help drivers reach their destinations faster. This can potentially save lives, especially when driving to hospitals or emergency units. Quantum neural networks are more reliable and accurate than conventional neural networks. Introduction to Connected Cars and Quantum Neural Network Model A new system/ device will be embedded in the vehicle’s dashboard to provide support to drivers in emergency situations. The system offers second-to-second continuous support and is specifically designed to handle emergency or complex situations. The system collects data from the vehicle’s sensors and sends it to the connected cloud drive. This data will be processed using quantum neural networks built on the principles of quantum computing and classical physics. The concept of using quantum neural networks and connecting cars (through shared cloud data) is different from the existing approaches used in the industry. This model will be faster, reliable, accurate, and efficient enough to handle the worst-case scenarios people might experience when driving. Resources Required for the Model Data is the primary resource for this model. The quantum neural network model requires data from three sources to understand the situations, driving behavior, and the vehicle’s overall performance. Descriptive Data This data is about the car and its performance. The data is collected from sensors embedded in the engine, suspension, brake, tires (air pressure), etc. This data will be used to identify car’s health and quality. It also provides information about what’s happening in the car every second. The quantum neural network model will be able to provide a suitable solution when it knows the car’s strengths and limitations. Navigational Data This data is related to the routes, navigations, and trips you take in the car. The model collects data from maps to determine the current location, destination, route map, etc. It also gathers data from side impact detection sensors, blind spot detection sensors, cyclist and pedestrian detection sensors, etc., to pinpoint your exact current location. Behavioral Data Behavioral data deals with drivers’ performance and abilities. The data is extracted from the sensors embedded in the dashboard. Different sensors are used to collect data necessary for the quantum neural network model to understand the driver’s health and current condition. The sensors help determine who the driver is and suggest a solution according to their driving history (collected and stored in the connected cloud). Heartbeat sensors, eye-tracking sensors, and fingerprint sensors on the steering wheel are used for data collection. Sensors that track the driving patterns are also used to determine the abilities of the driver. Workflow of the Proposed Model Working Process of the Quantum Neural Network Model The entire proposed concept will have four steps or modules: Each module has a definite purpose and streamlines the data flow within the model to arrive at the desired outcome. The second module is where the majority of the work happens. It is divided into three sub-modules. Let’s explore each module in detail. 1. Data Extraction As the name suggests, the data collected from multiple sensors in the car and stored in the cloud are extracted into the APIs. The process of collecting data from the car’s sensors and sending them to the connected cloud drive is continuous. The vast amounts of data are then directly sent to the APIs, where preprocessing occurs. 2. Data Preprocessing The APIs transfer the data to preprocessing module, which has three sub-modules to prepare the data for analysis. Data Cleaning The first sub-module cleans the data extracted from the connected drive APIs. This is a necessary step to improve data quality and increase the accuracy of the quantum neural network model. Naturally, data collected from multiple sensors will have issues such as wrong image frames, incompatible data formats, corrupt data values, incomplete/ missing data values, etc. This will affect the quality of the final outcome. This sub-module uses different techniques and tools to clean data and repair the wrong image frames. It tries to resolve the missing/ incomplete data or remove it totally. Statistical techniques are used to identify the issues with data and clean it accordingly. Data Preprocessing Preprocessing is similar to structuring and formatting data in large datasets. This sub-module prepares the cleaned data to make it ready for transformation, training, and predictions. The data is categorized based on its source. For example, data from the cameras are sent to the video processing module. Data from heartbeat sensors go to the numerical processing module, and so on. New data categories will be created to sort the cleaned input data into neat segments/ types, making it easy for the quantum neural network to process. Data Transformation The last sub-module of the preprocessing stage is data transformation. Here, the preprocessed and sorted data is transformed to create a summary of what it contains. This helps understand the actual meaning of the data before it is fed into the quantum neural network for predictions. The transformed data is analyzed to arrive at the summary and is fed into the learning phase of the system. 3. Training and Predicting Outcomes using Quantum Neural Network Model This module deals with training the quantum neural network to become capable of working with large datasets and delivering accurate predictions in less time. The data transformed in the previous module is fed

Kavika Roy
September 26, 2022
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Dimensionality Reduction Techniques in Data Science

Dimensionality reduction techniques are basically a part of the data pre-processing step, performed before training the model. Analyzing data with a list of variables in machine learning requires a lot of resources and computations, not to mention the manual labor that goes with it. This is precisely where the dimensionality reduction techniques come into the picture. The dimensionality reduction technique is a process that transforms a high-dimensional dataset into a lower-dimensional dataset without losing the valuable properties of the original data. These dimensionality reduction techniques are basically a part of the data pre-processing step, performed before training the model. What is Dimensionality Reduction in Data Science? Imagine you are training a model that could predict the next day’s weather based on the various climatic conditions of the present day. The present-day conditions could be based on sunlight, humidity, cold, temperature, and many millions of such environmental features, which are too complex to analyze. Hence, we can lessen the number of features by observing which of them are strongly correlated with each other and clubbing them into one. Here, we can club humidity and rainfall into a single dependent feature since we know they are strongly correlated. That’s it! This is how the dimensionality reduction technique is used to compress complex data into a simpler form without losing the essence of the data. Moreover, data science and AI experts are now also using data science solutions to leverage business ROI. Data visualization, data mining, predictive analytics, and other data analytics services by DataToBiz are changing the business game. Why is Dimensionality Reduction Necessary? machine learning and Deep Learning techniques are performed by inputting a vast amount of data to learn about fluctuations, trends, and patterns. Unfortunately, such huge data consists of many features, which often leads to a curse of dimensionality. Moreover, sparsity is a common occurrence in large datasets. Sparsity refers to having negligible or no value features, and if it is inputted in a training model, it performs poorly on testing. In addition, such redundant features cause problems in clustering similar features of the data. Hence, to counter the curse of dimensionality, dimensionality reduction techniques come to the rescue. The answers to the question of why dimensionality reduction is useful are: Now let us understand which algorithms are used for dimensionality reduction of data with examples. What are the Dimensionality Reduction Techniques The dimensionality reduction techniques are broadly divided into two categories, namely, 1. Linear Methods PCA Principal Component Analysis (PCA) is one of the used DR techniques in data science. Consider a set of ‘p‘ variables that are correlated with each other. This technique reduces this set of ‘p‘ variables into a smaller number of uncorrelated variables, usually denoted by ‘k‘, where (k<p). These ‘k‘ variables are called principal components, and their variation is similar to the original dataset. PCA is used to figure out the correlation among features, which it combines together. As a result, the resultant dataset has lesser features that are linearly correlated with each other. This way, the model performs the reduction of correlated features while simultaneously calculating maximum variance in the original dataset. After finding the directions of this variance, it directs them into a smaller dimensional space which gives rise to new components called principal components. These components are pretty sufficient in representing the original features. Therefore, it reduces the reconstruction error while finding out the optimum components. This way, data is reduced, making the machine learning algorithms perform better and faster. PrepAI is one of the perfect examples of AI that has made use of the PCA technique in the backend to generate questions from a given raw text intelligently. Factor Analysis This technique is an extension of Principal Component Analysis (PCA). The main focus of this technique is not just to reduce the dataset. It focuses more on finding out latent variables, which are results of other variables from the dataset. They are not measured directly in a single variable. Latent variables are also called factors. Hence, the process of building a model which measures these latent variables is known as factor analysis. It not only helps in reducing the variables but also helps in distinguishing response clusters. For example, you have to build a model which will predict customer satisfaction. You will prepare a questionnaire that has questions like, “Are you happy with our product?” “Would you share your experience with your acquaintances?” If you want to create a variable to rate customer satisfaction, you will either average the responses or create a factor-dependent variable. This can be performed using PCA and keeping the first factor as a principal component. Linear Discriminant Analysis It is a dimensionality reduction technique that is used mainly for supervised classification problems. Logistic Regression fails in multi-classification. Hence, LDA comes into the picture to counter that shortcoming. It efficiently discriminates between training variables in their respective classes. Moreover, it is different from PCA as it calculates a linear combination between the input features to optimize the process of distinguishing different classes. Here is an example to help you understand LDA: Consider a set of balls belonging to two classes: Red Balls and Blue Balls. Imagine they are plotted on a 2D plane randomly, such that they cannot be separated into two distinct classes using a straight line. In such cases, LDA is used, which can convert a 2D graph into a 1D graph, thereby maximizing the distinction between the classes of balls. The balls are projected to a new axis which separates them into their classes in the best possible way. The new axis is formed using two steps: SVD Consider data with ‘m‘ columns. Truncated Singular Value Decomposition method (TSVD) is a projection method where these ‘m‘ columns (features) are projected into a subspace with ‘m‘ or lesser columns without losing the characteristics of the data. An example where TSVD can be used is a dataset containing reviews about e-commerce products. The review column is mostly left blank, which gives rise to null values in the data, and TSVD tackles it efficiently.