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Deploy AI Smarter LLM Scalability ML Ops and Cost Efficiency DevCourseWeb

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Files:
  1. Get Bonus Downloads Here.url 182 bytes
  2. 1. Introduction & Welcome.mp4 74.4 MB
  3. 1. Course Structure How to get the Most out of this Course.mp4 119.1 MB
  4. 2. Environment Setup Prepare and Use the Resource of this Course Right.mp4 63.6 MB
  5. 1. Ensuring Model Correctness Evaluation Techniques.mp4 48.5 MB
  6. 2. Performance Optimization Exploring Key Dimensions.mp4 56.6 MB
  7. 3. Balancing Speed and Accuracy Best Practices.mp4 76.4 MB
  8. 1. Fundamentals of ML Model Management and ML-Ops.mp4 59.5 MB
  9. 2. Overview of Effective ML-Ops Frameworks.mp4 49.0 MB
  10. 3. Setting up ML-Ops Framework Introduction to MLflow (Practical).mp4 103.3 MB
  11. 3.1 MLflow Setup Readme.html 190 bytes
  12. 4. Getting Started with MLflow A Practical Approach (Practical).mp4 89.0 MB
  13. 4.1 4.5_getting_started.ipynb 10.1 KB
  14. 4.2 Colab Getting Started with MLflow.html 143 bytes
  15. 4.3 Jupyter Notebook MLflow Getting Started.html 189 bytes
  16. 5. Training Models with MLflow A Hands-On Guide (Practical).mp4 171.0 MB
  17. 5.1 4.6_training_loop.ipynb 11.0 KB
  18. 5.2 Colab MLflow Training Loop.html 143 bytes
  19. 5.3 Jupyter Notebook MLflow Training Loop.html 187 bytes
  20. 6. MLflow for Model Inference Techniques and Practices (Practical).mp4 150.9 MB
  21. 6.1 4.7_mlflow_inference.ipynb 10.9 KB
  22. 6.2 Colab Inference with MLflow.html 143 bytes
  23. 6.3 Jupyter Notebook MLflow Inference & Serving.html 190 bytes
  24. 7. Advanced Techniques in MLflow Extending Functionality (Practical).mp4 74.2 MB
  25. 7.1 4.8_mlflow_authentication.py 386 bytes
  26. 7.2 GitHub MLflow Authentication.html 192 bytes
  27. 1. Efficiency through Batching and Dynamic Batches.mp4 105.9 MB
  28. 2. Hands-on Application of Batching Techniques (Practical).mp4 110.3 MB
  29. 2.1 5.2_batching_and_dynamic_batching.ipynb 8.4 KB
  30. 2.2 5.2_batching_and_dynamic_batching.py 3.6 KB
  31. 2.3 Jupyter Notebook Batching & Dynamic Batching.html 227 bytes
  32. 2.4 Python Source Batching & Dynamic Batching.html 224 bytes
  33. 3. The Role of Sorting in Model Deployment (Practical).mp4 119.9 MB
  34. 3.1 5.3_the_role_of_sorting_batches.ipynb 8.5 KB
  35. 3.2 5.3_the_role_of_sorting_batches.py 2.5 KB
  36. 3.3 Jupyter Notebook Batch Sorting Optimizations.html 225 bytes
  37. 3.4 Python Source Batch Sorting Optimizations.html 222 bytes
  38. 4. Leveraging Quantization for Model Efficiency (Practical).mp4 142.9 MB
  39. 4.1 5.4_understanding_quantization.ipynb 8.0 KB
  40. 4.2 5.4_understanding_quantization.py 2.5 KB
  41. 4.3 Jupyter Notebook Quantization for Model Efficiency.html 224 bytes
  42. 4.4 Python Source Quantization for Model Efficiency.html 221 bytes
  43. 5. Inference Strategies Parallelism, Flash Attention, GPTQ & AVQ,.mp4 139.0 MB
  44. 6. Next-Gen Scaling LoRa, Paged Attention, ZeRO.mp4 120.3 MB
  45. 1. The Broader Context of AI A Wider Perspective.mp4 74.2 MB
  46. 2. Measuring Performance Key Metrics for Large AI Projects.mp4 64.5 MB
  47. 3. Evaluating Deployment Strategies for Cost & Efficiency.mp4 53.7 MB
  48. 4. Real-World Benchmarks for Success Case Studies and Insights.mp4 134.3 MB
  49. 1. Basic Inference - First Levels of Deployment (Practical).mp4 132.7 MB
  50. 1.1 GitHub Level 1 Deployment.html 207 bytes
  51. 1.2 GitHub Level 2 Deployment.html 207 bytes
  52. 1.3 level1.py 921 bytes
  53. 1.4 level2.py 921 bytes
  54. 1.5 utils.py 428 bytes
  55. 2. Entering Optimisations - Advanced Levels of Deployment (Practical).mp4 91.4 MB
  56. 2.1 GitHub Level 3 Deployment.html 207 bytes
  57. 2.2 GitHub Level 4 Deployment.html 207 bytes
  58. 2.3 level3.py 931 bytes
  59. 2.4 level4.py 784 bytes
  60. 3. Setting Up Data Access in Distributed Environments (Practical).mp4 157.3 MB
  61. 3.1 GitHub Level 5 Deployment.html 205 bytes
  62. 4. Distributing Data Across a Cluster with RabbitMQ (Practical).mp4 101.1 MB
  63. 4.1 GitHub Level 5 Deployment.html 205 bytes
  64. 4.2 produce_prompts.py 533 bytes
  65. 4.3 rabbit.py 1.2 KB
  66. 5. Foundations of Distributed Computing with Ray (Practical).mp4 81.0 MB
  67. 5.1 GitHub Level 5 Deployment.html 205 bytes
  68. 6. Scaling Large Language Models on a Cluster (Practical).mp4 149.6 MB
  69. 6.1 consume_results.py 165 bytes
  70. 6.2 GitHub Level 5 Deployment.html 205 bytes
  71. 6.3 ray_batch_job.py 943 bytes
  72. Bonus Resources.txt 386 bytes

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