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This article explains the differences between observability, telemetry, and monitoring, and how they work together to help teams understand and improve their software systems. It also discusses the benefits of using OpenTelemetry, a standard for creating and collecting telemetry for software systems, and Honeycomb's observability platform.
• Continuous Integration (CI) and Continuous Deployment (CD) pipelines for Machine Learning (ML) applications • Importance of CI/CD in ML lifecycle • Designing CI/CD pipelines for ML models • Automating model training, deployment, and monitoring • Overview of tools and platforms used for CI/CD in ML
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