Discover how successful retail organizations navigate the complex journey from proof-of-concept to production-ready MLOps infrastructure. This comprehensive guide explores essential strategies for scaling machine learning operations, covering everything from standardized pipeline architecture to advanced model management. Learn practical solutions for handling model proliferation, managing multiple environments, and implementing robust governance frameworks. Whether you're dealing with a growing model fleet or planning for future scaling challenges, this post provides actionable insights for building sustainable, enterprise-grade MLOps systems in retail.
This blog post discusses the integration of ZenML and BentoML in machine learning workflows, highlighting their synergy that simplifies and streamlines model deployment. ZenML is an open-source MLOps framework designed to create portable, production-ready pipelines, while BentoML is an open-source framework for machine learning model serving. When combined, these tools allow data scientists and ML engineers to streamline their workflows, focusing on building better models rather than managing deployment infrastructure. The combination offers several advantages, including simplified model packaging, local and container-based deployment, automatic versioning and tracking, cloud readiness, standardized deployment workflow, and framework-agnostic serving.
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.
We compare ZenML with Apache Airflow, the popular data engineering pipeline tool. For machine learning workflows, using Airflow with ZenML will give you a more comprehensive solution.
Context windows in large language models are getting super big, which makes you wonder if Retrieval-Augmented Generation (RAG) systems will still be useful. But even with unlimited context windows, RAG systems are likely here to stay because they're simple, efficient, flexible, and easy to understand.
I explain why data labeling and annotation should be seen as a key part of any machine learning workflow, and how you probably don't want to label data only at the beginning of your process.
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