Enabling Broader AI and ML Analytics Adoption: Data Platforms, Tools, and Practices

Enabling the broader adoption of AI and ML analytics use cases is one of the top drivers behind the modernization of data infrastructure at companies right now. From gathering, transforming, and processing data to the development, testing, and monitoring of data pipelines and models, deriving greater business value from AI and ML initiatives requires the right mix of platforms, tools, and practices. 
 

To be successful, companies need a modular data architecture that can quickly accommodate new use cases. They also need the ability to rapidly integrate data, well-defined data governance and security processes, and standardized processes for building data pipelines. The use of automation, MLOps and AIOps methodologies, and cloud services that offer on-demand scalability are all key pillars for success as well.