| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction | |||
| 1. Introduction.mp4 | 18.6 MB | ||
| 2 - Mlops using mlflow | |||
| 10. Serving models using mlflow and real time endpoints.mp4 | 55.4 MB | ||
| 2. Why mlflow exists.mp4 | 88.3 MB | ||
| 3 - LLMOps using mlflow | |||
| 11. Prompt registry in mlflow.mp4 | 93.1 MB | ||
| 12. Loading prompts from registry.mp4 | 61.7 MB | ||
| 13. Prompt Evaluation (Part-1).mp4 | 94.1 MB | ||
| 14. Prompt Evaluation (Part -2 ).mp4 | 113.5 MB | ||
| 15. Custom Scorers in Prompt Evaluation.mp4 | 68.8 MB | ||
| 16. AI Gateway in MLFLOW.mp4 | 119 MB | ||
| 17. Observability and Monitoring of GenAI applications using mlflow.mp4 | 138.2 MB | ||
| 18. Prompt Evaluation Project.mp4 | 364 MB | ||
| 4 - Databricks - Mlflow | |||
| 19. Mlflow on Databricks.mp4 | 524.2 MB | ||
| 20. Deploy huggingface model on Databricks.mp4 | 367.8 MB | ||
| 5 - Databricks AI functions | |||
| 21. Intro.mp4 | 11.5 MB | ||
| 22. Data Ingestion.mp4 | 58.5 MB | ||
| 23. AI Sentiment Classification.mp4 | 47.8 MB | ||
| 24. AI classification.mp4 | 43.1 MB | ||
| 25. AI Extraction.mp4 | 52.9 MB | ||
| 26. AI Fix Grammar.mp4 | 22.1 MB | ||
| 27. Generic AI Query Function.mp4 | 105 MB | ||
| 28. Structured Schema Extraction using AI Query.mp4 | 28.3 MB | ||
| 29. End to End Project - Creating Databricks Batch Job for Sentiment Prediction.mp4 | 100.4 MB | ||
| 3. Mlflow setup from scratch.mp4 | 49 MB | ||
| 4. Experiments and Runs.mp4 | 58.9 MB | ||
| 5. Backend store and Artifact store.mp4 | 73.5 MB | ||
| 6. Things we can log using mlflow.mp4 | 144.3 MB | ||
| 7. Manual and Auto logging in MLFLOW.mp4 | 67.3 MB | ||
| 8. Nested Runs in MLFLOW.mp4 | 41.5 MB | ||
| 9. MLFLOW Model Registry.mp4 | 55.6 MB |
MLflow for MLOps & LLMOps: Master MLflow with Databricks
https://WebToolTip.com
Published 4/2026
Created by Rahul Jha
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 29 Lectures ( 6h 28m ) | Size: 3 GB
Learn MLflow for experiment tracking, model registry, model deployment, prompt management, and Databricks AI Functions
What you'll learn
✓ Understand how MLflow works internally and how it fits into real MLOps workflows for experiment tracking, model lifecycle management, and deployment.
✓ Track machine learning experiments using MLflow by logging parameters, metrics, artifacts, and runs in a structured and reproducible way.
✓ Build and manage ML models using MLflow Model Registry including versioning, lineage tracking, and production model management.
✓ Deploy ML models as REST APIs using MLflow’s built-in model serving capabilities for real-time inference.
✓ Implement LLMOps workflows using MLflow including prompt registry, prompt versioning, evaluation, and prompt management.
✓ Integrate MLflow with Databricks to manage machine learning experiments and production ML pipelines.
✓ Use Databricks AI Functions to perform tasks like sentiment analysis, classification, text extraction, and schema extraction using SQL.
✓ Build an end-to-end ML workflow including experiment tracking, model logging, model registry, and deployment.
Requirements
● Basic understanding of Python programming
● Familiarity with machine learning concepts such as models, datasets, and training
● A computer capable of running Python and Jupyter / VS Code
● A free Databricks account (we will show how to set it up)
● Curiosity to understand how real MLOps and LLMOps systems work in production
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