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.

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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.

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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.

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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.

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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