Machine Learning Operations (MLOps) is crucial in today's tech landscape, even with the rise of Large Language Models (LLMs). Implementing MLOps on AWS, leveraging services like SageMaker, ECR, S3, EC2, and EKS, can enhance productivity and streamline workflows. ZenML, an open-source MLOps framework, simplifies the integration and management of these services, enabling seamless transitions between AWS components. MLOps pipelines consist of Orchestrators, Artifact Stores, Container Registry, Model Deployers, and Step Operators. AWS offers a suite of managed services, such as ECR, S3, and EC2, but careful planning and configuration are required for a cohesive MLOps workflow.
Mohan and Alex discuss neurosymbolic AI and the implications of a shift towards that as a core paradigm for production AI systems. In particular, we discuss the practical consequences of such a shift, both in terms of team composition as well as infrastructure requirements.
ZenML recently added an integration with Evidently, an open-source tool that allows you to monitor your data for drift (among other things). This post showcases the integration alongside some of the other parts of Evidently that we like.
We discuss the role of MLOps in an organization, some deployment war stories from his career as well as what he considers to be 'best practices' in production machine learning.
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