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Last Updated on July 29, 2022
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AI & Machine Learning now power most product experiences even beyond those of the big technology companies. Today, your models must perform and function correctly to ultimately deliver business value. The cost of deploying a slow or bad model, or not detecting undesirable behavior quickly, could significantly impact customer experience and the business’ bottom line.
Stefan Krawczky has spent the last 15+ years working on this exact problem at companies like Stitch Fix, Nextdoor, and LinkedIn. He has successfully streamlined the model ‘productionalization’ process for hundreds of the best data scientists and machine learning engineers. He has also built and managed the infrastructure to create, deploy, and track tens of thousands of models using MLOps best practices.
Over the course of four 2-hr sessions with Stefan and other top ML professionals, you will:
- Learn to identify, avoid, and prevent common ML outages
- Review industry case studies to learn common approaches for scaling model inference
- Evaluate and extrapolate tactics from Stitch Fix’s production deployment strategy
- Discuss the best tooling for improving model observability
Plus, unlike other online platforms, these sessions on Sphere will give students the unique opportunity to work directly with Stefan on industry-specific scenarios to upskill their MLOps knowledge while networking with other top-tier ML professionals. Since the course is also fully accredited, most students can expense the course using their employee L&D budget.
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Image and article originally from machinelearningmastery.com. Read the original article here.