Udemy - Deploy AI Smarter - LLM Scalability, ML-Ops and Cost Efficiency

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 1. Introduction
• 1. Introduction & Welcome.mp4 (74.4 MB)
2. Getting Started
• 1. Course Structure How to get the Most out of this Course.mp4 (119.1 MB)
• 2. Environment Setup Prepare and Use the Resource of this Course Right.mp4 (63.6 MB)
3. Pre-Deployment Strategies
• 1. Ensuring Model Correctness Evaluation Techniques.mp4 (48.5 MB)
• 2. Performance Optimization Exploring Key Dimensions.mp4 (56.6 MB)
• 3. Balancing Speed and Accuracy Best Practices.mp4 (76.4 MB)
4. Advanced Model Management with ML-Ops
• 1. Fundamentals of ML Model Management and ML-Ops.mp4 (59.5 MB)
• 2. Overview of Effective ML-Ops Frameworks.mp4 (49.0 MB)
• 3. Setting up ML-Ops Framework Introduction to MLflow (Practical).mp4 (103.3 MB)
• 3.1 MLflow Setup Readme.html (0.2 KB)
• 4. Getting Started with MLflow A Practical Approach (Practical).mp4 (89.0 MB)
• 4.1 4.5_getting_started.ipynb (10.1 KB)
• 4.2 Colab Getting Started with MLflow.html (0.1 KB)
• 4.3 Jupyter Notebook MLflow Getting Started.html (0.2 KB)
• 5. Training Models with MLflow A Hands-On Guide (Practical).mp4 (171.0 MB)
• 5.1 4.6_training_loop.ipynb (11.0 KB)
• 5.2 Colab MLflow Training Loop.html (0.1 KB)
• 5.3 Jupyter Notebook MLflow Training Loop.html (0.2 KB)
• 6. MLflow for Model Inference Techniques and Practices (Practical).mp4 (150.9 MB)
• 6.1 4.7_mlflow_inference.ipynb (10.9 KB)
• 6.2 Colab Inference with MLflow.html (0.1 KB)
• 6.3 Jupyter Notebook MLflow Inference & Serving.html (0.2 KB)
• 7. Advanced Techniques in MLflow Extending Functionality (Practical).mp4 (74.2 MB)
• 7.1 4.8_mlflow_authentication.py (0.4 KB)
• 7.2 GitHub MLflow Authentication.html (0.2 KB)
5. Advanced Model Deployment Techniques
• 1. Efficiency through Batching and Dynamic Batches.mp4 (105.9 MB)
• 2. Hands-on Application of Batching Techniques (Practical).mp4 (110.3 MB)
• 2.1 5.2_batching_and_dynamic_batching.ipynb (8.4 KB)
• 2.2 5.2_batching_and_dynamic_batching.py (3.6 KB)
• 2.3 Jupyter Notebook Batching & Dynamic Batching.html (0.2 KB)
• 2.4 Python Source Batching & Dynamic Batching.html (0.2 KB)
• 3. The Role of Sorting in Model Deployment (Practical).mp4 (119.9 MB)
• 3.1 5.3_the_role_of_sorting_batches.ipynb (8.5 KB)
• 3.2 5.3_the_role_of_sorting_batches.py (2.5 KB)
• 3.3 Jupyter Notebook Batch Sorting Optimizations.html (0.2 KB)
• 3.4 Python Source Batch Sorting Optimizations.html (0.2 KB)
• 4. Leveraging Quantization for Model Efficiency (Practical).mp4 (142.9 MB)
• 4.1 5.4_understanding_quantization.ipynb (8.0 KB)
• 4.2 5.4_understanding_quantization.py (2.5 KB)
• 4.3 Jupyter Notebook Quantization for Model Efficiency.html (0.2 KB)
• 4.4 Python Source Quantization for Model Efficiency.html (0.2 KB)
• 5. Inference Strategies Parallelism, Flash Attention, GPTQ & AVQ,.mp4 (139.0 MB)
• 6. Next-Gen Scaling LoRa, Paged Attention, ZeRO.mp4 (120.3 MB)
6. The Economics of Machine Learning Inference
• 1. The Broader Context of AI A Wider Perspective.mp4 (74.2 MB)
• 2. Measuring Performance Key Metrics for Large AI Projects.mp4 (64.5 MB)
• 3. Evaluating Deployment Strategies for Cost & Efficiency.mp4 (53.7 MB)
• 4. Real-World Benchmarks for Success Case Studies and Insights.mp4 (134.3 MB)
7. Effective Cluster Management for Large Scale ML Deployments
• 1. Basic Inference - First Levels of Deployment (Practical).mp4 (132.7 MB)
• 1.1 GitHub Level 1 Deployment.html (0.2 KB)
• 1.2 GitHub Level 2 Deployment.html (0.2 KB)
• 1.3 level1.py (0.9 KB)
• 1.4 level2.py (0.9 KB)
• 1.5 utils.py (0.4 KB)
• 2. Entering Optimisations - Advanced Levels of Deployment (Practical).mp4 (91.4 MB)
• 2.1 GitHub Level 3 Deployment.html (0.2 KB)
• 2.2 GitHub Level 4 Deployment.html (0.2 KB)
• 2.3 level3.py (0.9 KB)
• 2.4 level4.py (0.8 KB)
• 3. Setting Up Data Access in Distributed Environments (Practical).mp4 (157.3 MB)
• 3.1 GitHub Level 5 Deployment.html (0.2 KB)
• 4. Distributing Data Across a Cluster with RabbitMQ (Practical).mp4 (101.1 MB)
• 4.1 GitHub Level 5 Deployment.html (0.2 KB)
• 4.2 produce_prompts.py (0.5 KB)
• 4.3 rabbit.py (1.2 KB)
• 5. Foundations of Distributed Computing with Ray (Practical).mp4 (81.0 MB)
• 5.1 GitHub Level 5 Deployment.html (0.2 KB)
• 6. Scaling Large Language Models on a Cluster (Practical).mp4 (149.6 MB)
• 6.1 consume_results.py (0.2 KB)
• 6.2 GitHub Level 5 Deployment.html (0.2 KB)
• 6.3 ray_batch_job.py (0.9 KB)
• Bonus Resources.txt (0.4 KB)