Data science and machine learning are continuing to evolve as core capabilities across many industries.

But high-quality data science output is only half the story.

As the data science profession matures from “back office support” to leading from the front, there is an increasing need for more integrated systems that plug into business operations.

To get the most out of these capabilities, organisations must move beyond just building robust models, and establish operational processes that can produce, implement and maintain machine learning systems at scale.

Enter MLOps.

To understand the fundamentals and best practices of MLOps, I recently spoke to Shalini Kurapati who is CEO of Clearbox.ai.

Clearbox AI is the data-centric MLOps company that enables trustworthy and human-centred AI.

Their AI Control Room automatically produces synthetic data and insights to solve the issues related to data quality, data access and sharing, and privacy aspects that block AI adoption in companies.

In this episode of Leaders of Analytics, we cover:

• What MLOps is and why we need it to succeed with advanced data science solutions
• How to get beyond the proof-of-concept-to-production gap and get models into operation
• The importance of data-centric AI in building MLOps best practices
• The most common AI pitfalls to avoid
• How Human Centred Design principles can be used to build AI for good, and much more.

Check out Clearbox here: https://clearbox.ai/

Connect with Shalini here: https://www.linkedin.com/in/shalini-kurapati-phd-she-her-06516324/