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Checked by:
Category:
Language:
English
Total Size:
2.8 GB
Info Hash:
FD13BC0C3FEF065728545D7C62A8DA2E5D4E7E69
Added By:
Added:
April 8, 2024, 9:38 p.m.
Stats:
|
(Last updated: May 4, 2025, 3:38 p.m.)
| File | Size |
|---|---|
| Get Bonus Downloads Here.url | 182 bytes |
| 1. Introduction & Welcome.mp4 | 74.4 MB |
| 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 |
| 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 |
| 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 | 190 bytes |
| 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 | 143 bytes |
| 4.3 Jupyter Notebook MLflow Getting Started.html | 189 bytes |
| 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 | 143 bytes |
| 5.3 Jupyter Notebook MLflow Training Loop.html | 187 bytes |
| 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 | 143 bytes |
| 6.3 Jupyter Notebook MLflow Inference & Serving.html | 190 bytes |
| 7. Advanced Techniques in MLflow Extending Functionality (Practical).mp4 | 74.2 MB |
| 7.1 4.8_mlflow_authentication.py | 386 bytes |
| 7.2 GitHub MLflow Authentication.html | 192 bytes |
| 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 | 227 bytes |
| 2.4 Python Source Batching & Dynamic Batching.html | 224 bytes |
| 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 | 225 bytes |
| 3.4 Python Source Batch Sorting Optimizations.html | 222 bytes |
| 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 | 224 bytes |
| 4.4 Python Source Quantization for Model Efficiency.html | 221 bytes |
| 5. Inference Strategies Parallelism, Flash Attention, GPTQ & AVQ,.mp4 | 139.0 MB |
| 6. Next-Gen Scaling LoRa, Paged Attention, ZeRO.mp4 | 120.3 MB |
| 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 |
| 1. Basic Inference - First Levels of Deployment (Practical).mp4 | 132.7 MB |
| 1.1 GitHub Level 1 Deployment.html | 207 bytes |
| 1.2 GitHub Level 2 Deployment.html | 207 bytes |
| 1.3 level1.py | 921 bytes |
| 1.4 level2.py | 921 bytes |
| 1.5 utils.py | 428 bytes |
| 2. Entering Optimisations - Advanced Levels of Deployment (Practical).mp4 | 91.4 MB |
| 2.1 GitHub Level 3 Deployment.html | 207 bytes |
| 2.2 GitHub Level 4 Deployment.html | 207 bytes |
| 2.3 level3.py | 931 bytes |
| 2.4 level4.py | 784 bytes |
| 3. Setting Up Data Access in Distributed Environments (Practical).mp4 | 157.3 MB |
| 3.1 GitHub Level 5 Deployment.html | 205 bytes |
| 4. Distributing Data Across a Cluster with RabbitMQ (Practical).mp4 | 101.1 MB |
| 4.1 GitHub Level 5 Deployment.html | 205 bytes |
| 4.2 produce_prompts.py | 533 bytes |
| 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 | 205 bytes |
| 6. Scaling Large Language Models on a Cluster (Practical).mp4 | 149.6 MB |
| 6.1 consume_results.py | 165 bytes |
| 6.2 GitHub Level 5 Deployment.html | 205 bytes |
| 6.3 ray_batch_job.py | 943 bytes |
| Bonus Resources.txt | 386 bytes |
Name
DL
Uploader
Size
S/L
Added
-
2.8 GB
[29
/
3]
2024-04-08
| Uploaded by FreeCourseWeb | Size 2.8 GB | Health [ 29 /3 ] | Added 2024-04-08 |
NOTE
SOURCE: Deploy AI Smarter LLM Scalability ML Ops and Cost Efficiency DevCourseWeb
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