Experimenting with data and machine learning can prove useful, but productively collaborating with a team to hand off models from training to production is an entirely different process, and one that can lead to real business transformation. Assuming you employ more than just a single data scientist, it’s essential to keep your modeling pipelines free of conflicts or disruptions.
MLOps is a term that has come to mean “productionalization” of ML, which at first can sometimes seem to be a research exercise. Data scientists, DevOps, and software engineers often use and employ different skill sets, while MLOps infrastructure can keep your data team and your engineering team not only talking, but happily solving problems together with data. Machine learning is experimental, so carries significant risk. By implementing MLOps technology, tactics, and process, you begin to de-risk these projects step by step.
SigOpt collaborated with Valohai and Tecton to share what we’ve learned about how to practically implement MLOps. This ebook, which you can read here, explains how many large tech companies who are customers of the contributors – including SigOpt – scale their modeling pipelines with standardized tooling and processes. If you’re playing a game of catch-up, with multiple models in production, you’ll find it far more difficult to systematically scale if you haven’t implemented some of the following tools and techniques:
- Feature store
- Model registry
- Version control for models and data transformations
- Monitoring in production
- Model tuning
- Metrics tracking
- Artifact tracking
- Automatic re-training
Guidelines are helpful, but how did these enterprises actually implement them? We are following up on this eBook with a discussion from the authors on this very topic. Sign up here, then tune in to hear our Head of Research, Michael McCourt, join Kevin Stumpf, CTO of Tecton, and Eero Laaksonen, CEO of Valohai, share stories of how their customers broke down some of these critical barriers to standardizing and scaling machine learning.
The event is Tuesday, March 16 at 9am PT and you can sign up here.
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