Enterprise MLOps platforms: What’s next?
Keynote Outline
Inflexible infrastructure, wasted work, and operationalisation pitfalls have all been key obstacles preventing organisations from adopting a model-driven strategy. MLOps platforms have been rapidly gaining popularity as more and more businesses realise how critical it is to have a reliable platform for accelerating research and operationalisation. But a state of the art
MLOps platform should be more than just a tool for spinning up JupyterLab in the cloud. In this talk we try to peek into the future and answer the “What’s next?” question in the context of MLOps. We’ll talk about the role of hybrid and multi-cloud architectures, GPU use for model inference, automatic cost control, integration with open source platforms like Feast and MLflow and why it matters, transitioning towards distributed compute options like Ray and OpenMPI, and more.
Key takeaways
What are the key elements of a state-of-the-art MLOps platform and where is the space headed
How hybrid and multi-cloud solutions help the growing requirements around data
sovereignty
Keynote Speaker Bio
Dr. Kjell Carlsson is the head of data science strategy and evangelism at Domino Data Lab where he advises organizations on how to drive business outcomes with artificial intelligence (AI) and data science. Previously, he covered AI, ML, and data science as a Principal Analyst at Forrester Research where he wrote reports on topics ranging from computer vision, MLOps, AutoML, and conversation intelligence to augmented intelligence, next-generation AI technologies, and data science best practices. He has spoken in countless keynotes, panels, and webinars, and is frequently quoted in the media. Carlsson received his Ph.D. in Business Economics from the Harvard Business School.
