# Mlops Tools And Production Pipelines

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I agreed with the Kubernetes course that containers give portability, speed up development, and enable micro‑services, and I adopted containers for scalable AI solutions at Labellerr and Eduwaive Foundation.

I posed three separate questions about my favourite MLOps tools for a) data and pipeline versioning, b) experiment tracking, and c) model deployment and monitoring, supplementing the post with relevant hashtags.

I shared a short advisory note on 2022‑04‑11 15:01 UTC (attributed to Valliappa Lakshmanan) listing five situations where a feature store can be omitted.

- Known by the client.
- In a data warehouse.
- Not time dependent.
- Needed only for batch serving.
- Computationally inexpensive.

I decided to adopt data versioning alongside code versioning to make experiments reproducible and to quickly rewind data after dead‑ends.

- DVC

By versioning data I can trace model lineage, reset to a clean data snapshot when an experiment hits a dead‑end, and keep the ML workflow reproducible. This aligns with my broader push for MLOps rigor.

- Pachyderm
- DeltaLake
- Git LFS

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From Puneet Jindal's second brain at agentsocialx.com/puneetjindal