MLOps World: Machine Learning in Production Courses

LLM Economics - The Cost of Leveraging Large Language Models

Explore the economic realities of deploying LLMs in production, comparing costs of RAG vs fine-tuning and open-source vs commercial models.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

What Does it Mean to be an AI Engineer in the World of Generative AI

Explore the evolving role of AI Engineers in the era of Generative AI, covering technical aspects and business strategies for both engineers and decision-makers.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

How to Design and Build Resilient Machine Learning Systems

Explore strategies for building resilient ML systems that gracefully handle real-world challenges. Learn to detect, mitigate, and overcome production-specific risks across various domains.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

LLM Evaluation Framework for Crafting Delightful Content from Messy Inputs

Explore an evaluation framework for LLM outputs, transforming diverse textual inputs into delightful content. Learn metrics for assessing quality, relevance, and information preservation.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

Bringing AI to Existing Databases Without Complex MLOps

Integrate AI seamlessly with existing databases, bypassing complex MLOps processes. Learn efficient techniques for enhancing data infrastructure with artificial intelligence.

• YouTube
• 41 minutes
• Self-Paced
• Free Video

Apache Airflow: Where Data Engineers and ML Engineers Meet

Explore production-quality Generative AI pipelines using Apache Airflow. Learn to orchestrate workflows, integrate data engineering, and implement MLOps best practices for robust AI applications.

• YouTube
• 47 minutes
• Self-Paced
• Free Video

Beyond the Model Zoo - Optimizing Foundation Models for Your Application

Optimize foundation models for specific applications, exploring techniques beyond pre-trained models to enhance performance and efficiency in machine learning projects.

• YouTube
• 31 minutes
• Self-Paced
• Free Video

Defining Open-Source AI: Challenges and Opportunities in the Evolving Landscape

Explore the complexities of defining Open-Source AI, balancing innovation with core open-source principles in the evolving AI landscape. Engage in shaping a definition that reflects technological progress.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

Reproducibility and Data Version Control for LangChain and LLM/OpenAI Models

Explore reproducibility and data version control for LangChain and LLM/OpenAI models using lakeFS. Learn to manage large datasets efficiently and achieve reproducibility in LLM applications.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

Finetuning a Large Language Model on a Custom Dataset

Discover parameter-efficient finetuning techniques and optimizations for large language models using custom datasets, enhancing model performance at scale.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Deploying Generative AI Models - Best Practices and Interactive Example

Explore best practices for deploying generative AI models at scale, with an interactive example using UbiOps for serverless and cloud-agnostic implementation.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

Learn Your Codebase: Fine-tuning CodeLlama with Flyte to Learn Flyte

Discover how to fine-tune LLMs on specific codebases using Flyte, an open-source orchestration platform. Explore multi-node, multi-gpu distributed training for efficient model adaptation with limited resources.

• YouTube
• 1 hour 27 minutes
• Self-Paced
• Free Video

LLMs in Practice - A Guide to Recent Techniques and Trends

Explore recent LLM techniques and trends through hands-on examples. Gain insights into fine-tuning, vector databases, and practical applications for developers and executives.

• YouTube
• 2 hours 3 minutes
• Self-Paced
• Free Video

Spend Less Time Troubleshooting ML Production Issues

Learn strategies to quickly identify and resolve ML production issues, reducing troubleshooting time and improving model performance for stakeholders.

• YouTube
• 47 minutes
• Self-Paced
• Free Video

Introduction to Building ML Microservices - A Hands-On Approach with Music Industry Examples

Build ML microservices using MLServer and Seldon Core. Learn to create inference graphs for A/B testing, shadow deployment, and model monitoring with music industry examples.

• YouTube
• 3 hours 19 minutes
• Self-Paced
• Free Video