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My learningMLOPS TutorialLesson 05
When MLflow Stops Scaling: Choosing Between W&B, Comet, and Neptune

Lesson 05

When MLflow Stops Scaling: Choosing Between W&B, Comet, and Neptune

As ML teams grow, experiment tracking and collaboration demands can outpace basic tooling. In this video, compare MLflow, Weights & Biases, Comet, and Neptune to understand where each platform excels. Learn the trade-offs in scalability, collaboration, visualization, governance, and cost so you can choose the right experiment management platform for your team's next stage of growth.

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Curriculum

22 lessons · 1h 36m

0/22 lessons done1h 36m left
  1. 0103:50

    From Notebook Chaos to Autonomous Retraining: The 4 Levels of MLOps Maturity

    03:50

  2. 0204:26

    The Reproducibility Receipt: Proof Your ML Results Can Be Trusted

    04:26

  3. 0304:07

    MLflow 3.x: One Registry for XGBoost, GPTs, and Everything Between

    04:07

  4. 0404:24

    Git for Data: DVC, lakeFS, and the End of “Works on My Laptop

    04:24

  5. 03:58

    When MLflow Stops Scaling: Choosing Between W&B, Comet, and Neptune

    03:58

  6. 0603:49

    Training-Serving Skew: The Silent Model Killer at 3 A.M

    03:49

  7. 0703:16

    feast vs tecton vs hopsworks — the feature store choice you'll live with for 5 years

    03:16

  8. 0805:18

    The Sub-10ms Inference Stack: How Redis & Kafka Deliver Features at Lightning Speed

    05:18

  9. 0903:59

    The Orchestrator Wars: Kubeflow vs Airflow vs Prefect vs Flyte

    03:59

  10. 1004:13

    CI/CD for Models: GitHub Actions That Train, Test, and Block Bad Deploys

    04:13

  11. 1105:35

    The Death of Nightly Retraining: Drift-Aware, Event-Driven ML Pipelines

    05:35

  12. 1203:30

    Batch vs Online vs Streaming Inference: The Choice That Shapes Your Cloud Bill

    03:30

  13. 1304:14

    The Serving Stack Showdown: BentoML vs KServe vs Seldon vs Triton

    04:14

  14. 1404:36

    Shadow Traffic, Canaries & Ramp-Ups: Deploy Models Without Breaking Production

    04:36

  15. 1504:28

    100 Models, One GPU, Zero Meltdowns: The Multi-Model Endpoint Pattern

    04:28

  16. 1603:25

    Three Flavors of Drift: Data, Concept & Prediction—and How to Fix Each One

    03:25

  17. 1705:31

    The Silent Failure Stack: Evidently vs Arize vs Fiddler vs WhyLabs

    05:31

  18. 1804:27

    Model Cards, Lineage Logs & the 2 A.M. Audit Call You Didn’t See Coming

    04:27

  19. 1904:27

    Prompts as Code: Versioning, Evals & A/B Testing the New Model Artifact

    04:27

  20. 2004:48

    RAG in Production: The Chunking, Embedding & Vector DB Decisions That Make or Break Recall

    04:48

  21. 2106:13

    Catching Hallucinations Live: The Guardrail Stack That Keeps LLMs Honest

    06:13

  22. 2203:08

    AgentOps & Trace Stores: Debugging the Agent That Fails on Step Seven

    03:08