MLOps World: Machine Learning in Production Courses

Data and Machine Learning at League of Legends

Explore unique data challenges in video games, focusing on model deployment and evolving ML stacks in League of Legends since 2009.

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

GenAI: Lessons Learned from Failed Implementations

Explore real-world examples of failed GenAI implementations in large US enterprises, uncovering key lessons and unique challenges beyond common concerns.

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

How Many Labelled Examples Do You Need for a BERT-sized Model to Beat GPT4 on Predictive Tasks?

Explore the effectiveness of in-context learning vs. labelled data for predictive tasks, comparing BERT-sized models to GPT-4 in text categorization and entity recognition.

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

Supercharging Search with Language Models (LLMs)

Explore how Instacart revolutionized search using Language Models, enhancing user experience with personalized recommendations and improved results.

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

From Analytics to AI: How Generative AI Can Unlock Data Insights and Transform Decision-Making

Explore the transition from traditional reporting to AI-enabled teams and learn a framework for leveraging AI to drive business impact.

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

Data Versioning in Generative AI: A Pathway to Cost-Effective ML

Explore data versioning strategies for Generative AI, focusing on managing large-scale unstructured data and optimizing resource usage in model scoring and API interactions.

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

Introduction to LangChain and Retrieval Augmented Generation (RAG)

Explore LangChain's core concepts and build LLM-based applications using Retrieval Augmented Generation for document-based question answering.

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

Quick Deploy Model Serving in Ranking Systems

Discover how to streamline model deployment in ranking systems using ElasticSearch plugins and MLeap bundles for efficient, error-free updates and seamless integration with existing infrastructure.

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

Scaling AI in Production with PyTorch

Learn to scale AI in production using PyTorch, covering resilient distributed platforms, TorchServe, and strategic partner initiatives for a robust ecosystem.

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

Developing a Data-Centric NLP Machine Learning Pipeline for Text Classification

Explore the development of a text classification pipeline using BERT, focusing on creating a robust training dataset and taxonomy for improved ML results in a real-time system.

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

Is Your Machine Learning Model Trustworthy? Evaluating AI Quality and Reliability

Explore AI quality pillars, their importance, and evaluation methods to ensure trustworthy ML models. Learn automated techniques for assessing model performance, robustness, and explainability.

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

Model Monitoring: What, Why, and How

Learn crucial strategies for monitoring ML models in production, ensuring high performance and mitigating risks of defective models across industries.

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

The Critical Missing Component in the Production ML Stack - Data Logging for AI Applications

Explore AI observability tools for ML systems, focusing on logging libraries that enable testing, monitoring, and debugging of AI applications and data pipelines. Learn best practices to enhance MLOps.

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

Connecting Data Scientists to Production with the Tempo Python SDK

Prepare and test machine learning models locally, then deploy them to production using Tempo Python SDK. Bridge the gap between data scientists and DevOps for faster, more reliable model deployment.

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

Production Machine Learning Monitoring: Outliers, Drift, Explainers and Statistical Performance

Dive deep into advanced ML model monitoring techniques, including outlier detection, concept drift, explainability, and performance metrics for robust production deployments.

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