DevCourseWeb com Udemy Deploy AI Smarter LLM Scalability ML Ops and Cost Efficiency

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/1. Introduction/

1. Introduction & Welcome.mp4

/2. Getting Started/

1. Course Structure How to get the Most out of this Course.mp4

2. Environment Setup Prepare and Use the Resource of this Course Right.mp4

/.../3. Pre-Deployment Strategies/

1. Ensuring Model Correctness Evaluation Techniques.mp4

2. Performance Optimization Exploring Key Dimensions.mp4

3. Balancing Speed and Accuracy Best Practices.mp4

/.../4. Advanced Model Management with ML-Ops/

1. Fundamentals of ML Model Management and ML-Ops.mp4

2. Overview of Effective ML-Ops Frameworks.mp4

3. Setting up ML-Ops Framework Introduction to MLflow (Practical).mp4

3.1 MLflow Setup Readme.html

4. Getting Started with MLflow A Practical Approach (Practical).mp4

4.1 4.5_getting_started.ipynb

4.2 Colab Getting Started with MLflow.html

4.3 Jupyter Notebook MLflow Getting Started.html

5. Training Models with MLflow A Hands-On Guide (Practical).mp4

5.1 4.6_training_loop.ipynb

5.2 Colab MLflow Training Loop.html

5.3 Jupyter Notebook MLflow Training Loop.html

6. MLflow for Model Inference Techniques and Practices (Practical).mp4

6.1 4.7_mlflow_inference.ipynb

6.2 Colab Inference with MLflow.html

6.3 Jupyter Notebook MLflow Inference & Serving.html

7. Advanced Techniques in MLflow Extending Functionality (Practical).mp4

7.1 4.8_mlflow_authentication.py

7.2 GitHub MLflow Authentication.html

/.../5. Advanced Model Deployment Techniques/

1. Efficiency through Batching and Dynamic Batches.mp4

2. Hands-on Application of Batching Techniques (Practical).mp4

2.1 5.2_batching_and_dynamic_batching.ipynb

2.2 5.2_batching_and_dynamic_batching.py

2.3 Jupyter Notebook Batching & Dynamic Batching.html

2.4 Python Source Batching & Dynamic Batching.html

3. The Role of Sorting in Model Deployment (Practical).mp4

3.1 5.3_the_role_of_sorting_batches.ipynb

3.2 5.3_the_role_of_sorting_batches.py

3.3 Jupyter Notebook Batch Sorting Optimizations.html

3.4 Python Source Batch Sorting Optimizations.html

4. Leveraging Quantization for Model Efficiency (Practical).mp4

4.1 5.4_understanding_quantization.ipynb

4.2 5.4_understanding_quantization.py

4.3 Jupyter Notebook Quantization for Model Efficiency.html

4.4 Python Source Quantization for Model Efficiency.html

5. Inference Strategies Parallelism, Flash Attention, GPTQ & AVQ,.mp4

6. Next-Gen Scaling LoRa, Paged Attention, ZeRO.mp4

/.../6. The Economics of Machine Learning Inference/

1. The Broader Context of AI A Wider Perspective.mp4

2. Measuring Performance Key Metrics for Large AI Projects.mp4

3. Evaluating Deployment Strategies for Cost & Efficiency.mp4

4. Real-World Benchmarks for Success Case Studies and Insights.mp4

/.../7. Effective Cluster Management for Large Scale ML Deployments/

1. Basic Inference - First Levels of Deployment (Practical).mp4

1.1 GitHub Level 1 Deployment.html

1.2 GitHub Level 2 Deployment.html

1.3 level1.py

1.4 level2.py

1.5 utils.py

2. Entering Optimisations - Advanced Levels of Deployment (Practical).mp4

2.1 GitHub Level 3 Deployment.html

2.2 GitHub Level 4 Deployment.html

2.3 level3.py

2.4 level4.py

3. Setting Up Data Access in Distributed Environments (Practical).mp4

3.1 GitHub Level 5 Deployment.html

4. Distributing Data Across a Cluster with RabbitMQ (Practical).mp4

4.1 GitHub Level 5 Deployment.html

4.2 produce_prompts.py

4.3 rabbit.py

5. Foundations of Distributed Computing with Ray (Practical).mp4

5.1 GitHub Level 5 Deployment.html

6. Scaling Large Language Models on a Cluster (Practical).mp4

6.1 consume_results.py

6.2 GitHub Level 5 Deployment.html

6.3 ray_batch_job.py

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Bonus Resources.txt

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