Tag: MLOps
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MLflow Experiment Tracking, Model Registry and Versioning (Data Science Series, Part 24)
A model you cannot reproduce is a rumour. Here is how I set up MLflow tracking, a model registry and aliases on the churn project, including the errors you will hit on a local file store.
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Feature Stores and Training Serving Skew in Machine Learning (Data Science Series, Part 23)
A model that scores well offline and badly in production is usually not a modelling failure. It is two pieces of code computing the same feature differently. Here is how skew happens, what a feature store fixes, and when a shared library is the better answer.
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Serving Machine Learning Models: Batch, Real Time and Streaming (Data Science Series, Part 22)
Batch, real time and streaming are three different answers to the same question, and picking the wrong one costs money for months before anyone notices. Here is how I choose, with runnable FastAPI and batch scoring code for the churn project.
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From Notebook to Python Package: Structuring a Data Science Project Someone Else Can Run (Data Science Series, Part 21)
A working churn notebook is not a deliverable. Here is the src layout, pyproject.toml, path handling, test suite and artifact versioning that turn 175 lines of model code into something a colleague installs and runs in one command.
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The Data Science Lifecycle End to End, From Business Question to Retired Model (Data Science Series, Part 2)
Modelling was 11 percent of my last churn project. Here is the eight stage lifecycle that accounts for the other 89 percent, including the retirement stage almost nobody plans for.
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watsonx LLMOps on OpenShift, from Notebook to Promotion Gate (IBM Gen AI Series, Part 22)
How to run generative AI features in production on the IBM stack: watsonx.ai for models and governance, Red Hat OpenShift AI for pipelines and GPUs, and a promotion gate that refuses to ship a regression.
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Vertex AI Pipelines for LLMOps, from Notebook to Nightly Retrain (Google Cloud Gen AI Series, Part 28)
How I turn a Gemini tuning notebook into a Vertex AI pipeline that reruns nightly, gates on evals, registers versions, and does not quietly ship a worse model.
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LLMOps and CI/CD on Azure for GenAI Apps (Azure Gen AI Series, Part 28)
An Azure LLMOps pipeline looks like MLOps until the gate. Here is how to block a merge on an evaluation score, register the flow, and roll it out blue to green on a managed online endpoint, plus why new pipelines should skip Prompt Flow.
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Hugging Face Air-Gapped: Enterprise Hub, Offline Mirroring, and the On-Prem Build (Hugging Face Series, Part 16)
How to run Hugging Face models on a segmented or air-gapped network: mirror the artifacts to local storage, force offline mode at runtime, and use the Enterprise Hub for identity, governance, and the rate limits a proxy needs.
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Hugging Face Security and Governance: Gated Models, Malicious Weights, and Scanning Before Anything Enters Your Registry (Hugging Face Series, Part 15)
A model download is an unvetted artifact, and a pickle checkpoint can run code the moment you load it. Here is how to gate the Hugging Face Hub the way you already gate a container registry: safetensors over pickle, no blind trust_remote_code, scan before promote, pin for provenance.
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How to Run a Hugging Face Model: Inference Providers vs Endpoints vs Self-Host (Hugging Face Series, Part 11)
Three ways to serve a Hugging Face model: the serverless Inference Providers proxy, dedicated Inference Endpoints, or self-hosting TGI on your own GPUs. A build-vs-buy verdict for the infra team that owns the bill.
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Pushing Models and Datasets to the Hugging Face Hub: Private Repos, Versioning, and Model Cards (Hugging Face Series, Part 10)
Pushing to the Hugging Face Hub is artifact promotion. Here is how to create private repos, scope tokens and org RBAC, pin consumers to a commit, write a model card for provenance, and move large files without melting your bandwidth.
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Architecture & Design
About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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