| 1. Basic Inference - First Levels of Deployment (Practical).mp4 | 132.7 MB | ||
| 1. Course Structure How to get the Most out of this Course.mp4 | 119.1 MB | ||
| 1. Efficiency through Batching and Dynamic Batches.mp4 | 105.9 MB | ||
| 1. Ensuring Model Correctness Evaluation Techniques.mp4 | 48.5 MB | ||
| 1. Fundamentals of ML Model Management and ML-Ops.mp4 | 59.5 MB | ||
| 1. Introduction & Welcome.mp4 | 74.4 MB | ||
| 1. The Broader Context of AI A Wider Perspective.mp4 | 74.2 MB | ||
| 1.1 GitHub Level 1 Deployment.html | 204.8 B | ||
| 1.2 GitHub Level 2 Deployment.html | 204.8 B | ||
| 1.3 level1.py | 921.6 B | ||
| 1.4 level2.py | 921.6 B | ||
| 1.5 utils.py | 409.6 B | ||
| 2. Entering Optimisations - Advanced Levels of Deployment (Practical).mp4 | 91.4 MB | ||
| 2. Environment Setup Prepare and Use the Resource of this Course Right.mp4 | 63.6 MB | ||
| 2. Hands-on Application of Batching Techniques (Practical).mp4 | 110.3 MB | ||
| 2. Measuring Performance Key Metrics for Large AI Projects.mp4 | 64.5 MB | ||
| 2. Overview of Effective ML-Ops Frameworks.mp4 | 49 MB | ||
| 2. Performance Optimization Exploring Key Dimensions.mp4 | 56.6 MB | ||
| 2.1 5.2_batching_and_dynamic_batching.ipynb | 8.4 KB | ||
| 2.1 GitHub Level 3 Deployment.html | 204.8 B | ||
| 2.2 5.2_batching_and_dynamic_batching.py | 3.6 KB | ||
| 2.2 GitHub Level 4 Deployment.html | 204.8 B | ||
| 2.3 Jupyter Notebook Batching & Dynamic Batching.html | 204.8 B | ||
| 2.3 level3.py | 921.6 B | ||
| 2.4 Python Source Batching & Dynamic Batching.html | 204.8 B | ||
| 2.4 level4.py | 819.2 B | ||
| 3. Balancing Speed and Accuracy Best Practices.mp4 | 76.4 MB | ||
| 3. Evaluating Deployment Strategies for Cost & Efficiency.mp4 | 53.7 MB | ||
| 3. Setting Up Data Access in Distributed Environments (Practical).mp4 | 157.3 MB | ||
| 3. Setting up ML-Ops Framework Introduction to MLflow (Practical).mp4 | 103.3 MB | ||
| 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 | ||
| 3.1 GitHub Level 5 Deployment.html | 204.8 B | ||
| 3.1 MLflow Setup Readme.html | 204.8 B | ||
| 3.2 5.3_the_role_of_sorting_batches.py | 2.5 KB | ||
| 3.3 Jupyter Notebook Batch Sorting Optimizations.html | 204.8 B | ||
| 3.4 Python Source Batch Sorting Optimizations.html | 204.8 B | ||
| 4. Distributing Data Across a Cluster with RabbitMQ (Practical).mp4 | 101.1 MB | ||
| 4. Getting Started with MLflow A Practical Approach (Practical).mp4 | 89 MB | ||
| 4. Leveraging Quantization for Model Efficiency (Practical).mp4 | 142.9 MB | ||
| 4. Real-World Benchmarks for Success Case Studies and Insights.mp4 | 134.3 MB | ||
| 4.1 4.5_getting_started.ipynb | 10.1 KB | ||
| 4.1 5.4_understanding_quantization.ipynb | 8 KB | ||
| 4.1 GitHub Level 5 Deployment.html | 204.8 B | ||
| 4.2 5.4_understanding_quantization.py | 2.5 KB | ||
| 4.2 Colab Getting Started with MLflow.html | 102.4 B | ||
| 4.2 produce_prompts.py | 512 B | ||
| 4.3 Jupyter Notebook MLflow Getting Started.html | 204.8 B | ||
| 4.3 Jupyter Notebook Quantization for Model Efficiency.html | 204.8 B | ||
| 4.3 rabbit.py | 1.2 KB | ||
| 4.4 Python Source Quantization for Model Efficiency.html | 204.8 B | ||
| 5. Foundations of Distributed Computing with Ray (Practical).mp4 | 81 MB | ||
| 5. Inference Strategies Parallelism, Flash Attention, GPTQ & AVQ,.mp4 | 139 MB | ||
| 5. Training Models with MLflow A Hands-On Guide (Practical).mp4 | 171 MB | ||
| 5.1 4.6_training_loop.ipynb | 11 KB | ||
| 5.1 GitHub Level 5 Deployment.html | 204.8 B | ||
| 5.2 Colab MLflow Training Loop.html | 102.4 B | ||
| 5.3 Jupyter Notebook MLflow Training Loop.html | 204.8 B | ||
| 6. MLflow for Model Inference Techniques and Practices (Practical).mp4 | 150.9 MB | ||
| 6. Next-Gen Scaling LoRa, Paged Attention, ZeRO.mp4 | 120.3 MB | ||
| 6. Scaling Large Language Models on a Cluster (Practical).mp4 | 149.6 MB | ||
| 6.1 4.7_mlflow_inference.ipynb | 10.9 KB | ||
| 6.1 consume_results.py | 204.8 B | ||
| 6.2 Colab Inference with MLflow.html | 102.4 B | ||
| 6.2 GitHub Level 5 Deployment.html | 204.8 B | ||
| 6.3 Jupyter Notebook MLflow Inference & Serving.html | 204.8 B | ||
| 6.3 ray_batch_job.py | 921.6 B | ||
| 7. Advanced Techniques in MLflow Extending Functionality (Practical).mp4 | 74.2 MB | ||
| 7.1 4.8_mlflow_authentication.py | 409.6 B | ||
| 7.2 GitHub MLflow Authentication.html | 204.8 B | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 72 total files | |||
Deploy AI Smarter: LLM Scalability, ML-Ops & Cost Efficiency
https://DevCourseWeb.com
Published 4/2024
Created by The Fuzzy Scientist
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 29 Lectures ( 4h 41m ) | Size: 2.84 GB
Deployment, Generative AI, LLMs, GPT4, ML-Ops, LoRa, AVQ, Ray, RabbitMQ, Flash Paged Attention
What you'll learn:
Learn to set-up, configure and deploy large language models with precision, ensuring smooth operation in production environments.
Gain practical skills in ML-Ops with MLflow for effective model management and deployment.
Conduct cost-benefit analyses and apply strategic planning for economical AI project management.
Implement the latest LLM optimization and scaling techniques to enhance model performance.
Requirements:
Learners should only have a basic understanding of machine learning and proficiency in Python. All the other concepts are though inside the course.
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