MLOps and LLMOps

In this session, we will cover the following topics:

• Course Overview and Structure: Understand the key objectives and outline of the course, including how the sessions are designed to balance theory, practical implementation, and industry use cases. Learn the expectations and deliverables for successful completion.
• Core Concepts in MLOps: Explore the foundational principles of MLOps, focusing on automating machine learning workflows, reproducibility, and managing models in production environments.
• MLOps vs. LLMOps: Examine the unique challenges of working with large language models in production compared to traditional machine learning models. Gain insights into why LLMOps is emerging as a specialized subfield, addressing issues like model size, latency, and inference scalability.

In this session, we will cover the following topics:

• Kubeflow Pipeline Setup: Learn to design and implement end-to-end machine learning workflows using Kubeflow, including defining components, orchestrating tasks, and monitoring pipeline execution.
• Airflow Pipeline Setup: Understand how to create and schedule ML pipelines with Apache Airflow, focusing on DAG creation, task dependencies, and integrating ML tasks for efficient workflow automation.

In this session, we will cover the following topics:

Part 1: Presentation on AI Trends and Enterprise Applications

• Explore the latest advancements in AI and their relevance to large-scale enterprise use cases, highlighting innovations in model architectures, deployment strategies, and integration into business workflows.

Part 2: MLflow in the MLOps Ecosystem

• Discuss MLflow’s role in the MLOps lifecycle, focusing on how it streamlines data preprocessing, model training, deployment, and monitoring within enterprise-scale workflows.
• Understand its capabilities for experiment tracking, reproducibility, and model versioning, ensuring efficient collaboration and scalability.

In this session, we will cover the following topics:

• Assignment Solutions: Detailed walkthrough of the solutions for the first two assignments, addressing key concepts, common challenges, and best practices.
• MLflow Pipeline Discussion: Explore the design and implementation of an MLflow pipeline, focusing on its components for tracking experiments, managing model lifecycle, and integrating with deployment workflows.

In this session, we will cover the following topics:

• Weights & Biases Pipeline Setup: Step-by-step guide to setting up and configuring Weights & Biases for experiment tracking and model management in MLOps workflows.
• Neptune Pipeline Setup: Overview of integrating Neptune for tracking machine learning experiments, visualizing metrics, and collaborating on model development.
• Alternate Approach for Airflow Pipeline Setup: Explore a different method for configuring and optimizing Airflow pipelines for scalable ML workflows.

In this lab session, we will cover the following topics:

• Comet ML Setup and Integration: A hands-on guide to setting up Comet ML for experiment tracking, including configuration and integration into your existing MLOps workflows.
• Optimizing ML Experiments with Comet ML: Learn how to use Comet ML’s features to monitor, visualize, and optimize your machine learning experiments for better performance and reproducibility.

• Key Concepts of Distributed Computing: An overview of the fundamental principles behind distributed computing, focusing on scalability and resource optimization.
• Introduction to Ray: Introduction to Ray, a framework for distributed computing that simplifies scaling machine learning workflows.
• Ray Tasks and Actors: Learn how Ray tasks and actors work for parallel computing and resource management in distributed environments.
• Asynchronous Processing and Task Scheduling: Explore how asynchronous processing and task scheduling enhance computational efficiency and throughput.
• Use Case: Named Entity Recognition (NER): A practical demonstration of using Ray in a Named Entity Recognition (NER) task, showcasing its scalability and performance improvements.

In this lab session, we will cover the following topics:

• Task Overview: Participants will be provided with a zip file containing six books by Leo Tolstoy, where each chapter is an individual text file.
• Named Entity Recognition (NER): We will perform NER on the chapters to identify people, places, and organizations.
• Data Processing: Participants will preprocess the text data, applying NER models to extract and classify entities.
• CSV Compilation: The recognized entities (people, places, organizations) will be compiled into a structured CSV file for easy analysis and reference.

In this session, we will cover the following topics:

1. Distributed Computing Challenges & Ray Framework:
   • Discussion on the challenges in distributed computing.
   • Overview of the Ray framework, focusing on task distribution and memory management.
   • Introduction to Ray Data and its optimization for machine learning datasets.
2. Comparison with Spark and Real-World Use Cases:
   • Comparison of Ray Data with Spark, highlighting its advantages in GPU utilization and better integration with machine learning tools.
   • Exploration of Ray Data’s Pandas-like API for data shuffling efficiency.
   • Examination of Ray Data’s adoption in real-world companies like ByteDance and Spotify.

In this lab session, we will cover the following:

• MLflow Integration:
  • Integration of MLflow for model training, hyperparameter tuning, and serving models using the California Housing dataset.
• MLflow Setup Demo:
  • Demonstration of MLflow setup, model registry, and the differences between local and cloud-based model saving.
• Parallel Processing and Troubleshooting:
  • Discuss techniques like text chunking, clustering, and address system configuration issues to improve parallel processing efficiency.

In this lab session, we will cover the following topics:

• Text Classification: Techniques and best practices for training text classification models.
• Distributing Training with Ray Train: Utilizing Ray Train for distributed training to accelerate model training processes.
• Using Hugging Face Trainer: Simplifying model training using Hugging Face Trainer, integrating it with Ray for better scalability.

• vLLM for Scalable LLM Deployment: Learn how vLLM uses page attention to optimize memory and GPU usage, enabling high-throughput, multi-user support.
• Operating Modes: Explore batch mode for Python scripts and server mode for external client interaction via endpoints.
• Hardware Considerations: Understand the hardware needs for larger models, ideal for production, and compare vLLM with Ollama for smaller setups.

In the final session, we cover the following topics:

• Course Recap: A brief review of key concepts covered throughout the course.
• MLOps Overview: A comprehensive understanding of MLOps practices and their importance in managing the machine learning lifecycle.
• Emerging Trends and Concepts: Explore the latest advancements in MLOps and LLMOps, including new tools, techniques, and frameworks.
• Challenges in MLOps and LLMOps: Discuss the common hurdles faced when implementing MLOps and LLMOps, such as scaling, model monitoring, and data management.
• Practical Considerations in MLOps: Focus on real-world challenges and best practices for deploying, managing, and maintaining ML models in production.