For many companies, Digital Transformation enables customers to automate tasks, streamline decision processes, and implement use cases that were unachievable 10 years ago. Digital Transformation is creating opportunities to achieve value with AI. Yet AI continues to be complex.

One of the most complex tasks for getting AI workloads into production is providing data scientists with the correct set of tools for the model development process. Enterprise customers require a data science platform that streamlines the model development process by automating tasks while allowing teams to collaborate efficiently.

Customers are struggling to choose a data science platform that meets their requirements. These requirements are based on the need of data scientists plus the tools for last-mile execution. The data science platform must be open and flexible to enable the use cases of today while not locking out the use cases of tomorrow.

Customers tell us that they require:

• Flexible and open resources—Open-source libraries, user functions, and data science notebooks integration
• Streamlined model management for accelerated time-to-market—The ability to monitor and recalibrate hundreds of models, model-testing capabilities, and testing models side by side
• Effective collaboration tools—The ability for different skill levels to work together on the same workflows and projects, project archiving and management, and governance
• Last-mile delivery—The ability to create APIs or containers to be packaged in applications, faster deployment into production code, and metrics engagement to measure model impact