MLOps is an emerging engineering discipline that combines ML, DevOps, and Data Engineering to provide automation and infrastructure to speed up the AI/ML development lifecycle and bring models to production faster. It is one of the widely discussed topics in the ML practitioner community.
In this track, we will explore the best practices and innovations the ML community is developing and creating. Key areas of focus include declarative ML systems, distributed model training, scalable and low latency model inference, and ML observability to protect the downsides and ROI.
From this track
Ray: The Next Generation Compute Runtime for ML Applications
Ray is an open source project that makes it simple to scale any compute-intensive Python workload. Industry leaders like Uber, Shopify, Spotify are building their next generation ML platforms on top of Ray.
Head of Open Source Engineering @anyscalecompute
Fabricator: End-to-End Declarative Feature Engineering Platform
At Doordash, the last year has seen a surge in applications of machine learning to various product verticals in our growing business. However, with this growth, our data scientists have had increasing bottlenecks in their development cycle because of our existing feature engineering process.
ML Platform Engineering Manager @DoorDash
An Open Source Infrastructure for PyTorch
In this talk we’ll go over tools and techniques to deploy PyTorch in production. The PyTorch organization maintains and supports open source tools for efficient inference like pytorch/serve, job management pytorch/torchx and streaming datasets like pytorch/data.
Applied AI Engineer @Meta
Metrics for MLOps Platforms
Many companies are investing heavily into their ML platforms, either building something in-house or working with vendors. How do we know that an ML platform is any good? How do we compare different platforms?
Co-founder @Claypot AI
Empower Your ML Models with Customers Voice
ML engineers use A/B testings to iterate ML models, however, there are limitations of A/B testing that might not give us all the answers, and A/B testing might limit innovation if not used correctly. I’ll share examples from my previous examples and lessons I learned from interviewing 10+ ML eng
Senior Data Scientist @Predibase and “The Data Scientist Show" Podcast Host