MLOps World: Machine Learning in Production Courses

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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• 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
• On-Demand
• Free Video

Marius: Machine Learning over Billion-Edge Graphs - 10x Faster and 5x Cheaper

Discover how Marius optimizes machine learning on billion-edge graphs, achieving 10x faster and 5x cheaper training through innovative data flow architecture and resource utilization.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Building Scalable ML Services for Rapid Development in Health and Wellness

Discover how to build and scale ML services for health and wellness products, reducing release time by 85% using open-source tools like DBT, AirFlow, and MLFlow.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Introduction to Drifter ML: A Testing Framework for Machine Learning Models

Explore Drifter-ML, a testing framework ensuring ML models meet training assumptions. Learn motivations and practical examples for model testing.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Advanced Model Deployment with Seldon Deploy - Workshop Session

Create, deploy, monitor, and manage a customer segmentation ML model using Seldon Deploy. Explore e-commerce data, train models, deploy artifacts, and update with explainers and outlier detectors.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

Diagnosing Failure Modes in ML Organizations - Lessons from Yelp

Diagnose pitfalls in ML organizations. Learn to improve project velocity, practitioner happiness, and ML adoption across business objectives. Gain insights from Yelp's decade-long ML journey.

• YouTube
• 36 minutes
• On-Demand
• Free Video