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| 37 - 1. Hands-on AutoGen.mp4 |
31.58Мб |
| 38 - 2. Hands-on AutoGen.mp4 |
87.94Мб |
| 39 - 3. Hands-on IBM Bee Framework.mp4 |
126.56Мб |
| 40 - 4. Hands-on LangGraph.mp4 |
117.23Мб |
| 41 - 5. Hands-on CrewAI.mp4 |
97.05Мб |
| 42 - 6. Hands-on AutoGPT.mp4 |
45.26Мб |
| 44 - 2. Overview of MLOps and its Importance.mp4 |
3.07Мб |
| 45 - 3. Evolution of Machine Learning Operations.mp4 |
2.81Мб |
| 46 - 4. Key Concepts in MLOps - Versioning, Automation, and Monitoring.mp4 |
3.96Мб |
| 47 - 5. MLOps vs. DevOps - Similarities and Differences.mp4 |
3.25Мб |
| 48 - 6. Hands-on - Set up a basic MLOps Project Structure (Git, Docker, Model Pipeline.mp4 |
182.35Мб |
| 49 - 7. Introduction to Data Science to Production Pipeline Section.mp4 |
1.35Мб |
| 50 - 8. Overview of the ML Workflow - Data Preparation to Deployment.mp4 |
7.54Мб |
| 51 - 9. Experimentation vs. Production.mp4 |
5.45Мб |
| 52 - 10. Challenges in Deploying ML Models.mp4 |
2.21Мб |
| 53 - 11. Hands-on - Build an end-to-end pipeline for an ML model.mp4 |
250.10Мб |
| 55 - 13. Introduction to Cloud Platforms (AWS, GCP, Azure).mp4 |
12.66Мб |
| 56 - 14. Containerization with Docker.mp4 |
3.57Мб |
| 57 - 15. Kubernetes for Orchestrating ML Workloads.mp4 |
3.48Мб |
| 58 - 16. Setting up Local MLOps Environments.mp4 |
3.74Мб |
| 59 - 17. Hands-on - Containerize simple ML model & deploy it locally using Kubernetes.mp4 |
442.55Мб |
| 60 - Congratulations and Best of Luck.mp4 |
16.83Мб |
| Bonus Resources.txt |
70б |
| Get Bonus Downloads Here.url |
180б |