Design Scalable ML Architectures to Deliver AI-based Systems
Why organizations struggle with their machine learning pipelines
The framework to build and deploy ML models in production
The implementation strategy for an architecture to deliver AI-based systems
As artificial intelligence (AI) and (ML) initiatives mature, enterprises face significant scalability and operational challenges while struggling to systematically productionalize their ML pipelines. ML architectures tend to be constrained by data gravity, compute requirements and model integration complexities. In this complimentary webinar, Gartner for Technical Professionals expert Soyeb Barot examines the framework to build and deploy ML models in production alongside operationalizing the machine learning development lifecycle (MLDLC). He also will set out the implementation strategy for an architecture to deliver AI-based systems.
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