MLOps

Illustrative workflow showing GitHub triggering GitHub Actions, which then uses CML to generate a model and report, visualized as a pull request with performance metrics

Automating the CI/CD Pipeline for ML: A Practical Workflow with CML and GitHub Actions

Automating the CI/CD Pipeline for ML: A Practical Workflow with CML and GitHub Actions I. Introduction: Why CI/CD for Machine Learning Matters  In the rapidly evolving field of machine learning (ML), the adage ‘In ML, models are only as good as the last time they were tested ‘holds. This highlights the crucial role of CI/CD in ML, […]

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Illustrative scale comparing a "Managed Platform (e.g., SageMaker)" with a "Self-Hosted Stack," showing a smaller resource block and fewer people for the managed platform, balanced against more resources and people for the self-hosted stack

Is a Managed Platform (e.g., SageMaker) Ever Cheaper? A Break-Even Analysis for Startups

Is a Managed Platform (e.g., SageMaker) Ever Cheaper? A Break-Even Analysis for Startups I. Introduction: The Build vs Buy Dilemma in MLOps 🛠️💰 Many early-stage startups fall into the trap of thinking that open source = free. While it’s true that tools like MLflow, Kubeflow, or Seldon Core don’t have licensing fees, the reality is

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Illustrative iceberg depicting "Free Open Source MLOps Tools" above the water, and various AWS services with dollar signs below, representing hidden infrastructure costs

The True Cost of “Free”: Analyzing Hidden Infrastructure Costs of a Self-Hosted MLOps Stack on AWS

The True Cost of “Free”: Analyzing Hidden Infrastructure Costs of a Self-Hosted MLOps Stack on AWS I. Introduction: Why “Free” Self-Hosted MLOps Isn’t Free 💸 When startups hear the phrase “open-source MLOps”, the first thought is often “free”. Unfortunately, the cost realities of self-hosted MLOps hit hard once the first cloud bill arrives. Running a

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