MLOps World: Machine Learning in Production Courses

Challenges for ML Operations in a Fast Growing Company

Explore ML operations challenges in rapidly growing companies. Learn strategies for scalable platforms, efficient model training, and balancing distributed vs. non-distributed applications. Gain insights on developer ergonomics and best practices.

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

Closing the Production Gap with MLOps

Explore MLOps strategies to accelerate deployment, monitor models, ensure governance, and manage the model lifecycle, including automated retraining and challenger models in production.

• YouTube
• 1 hour 25 minutes
• Self-Paced
• Free Video

The Rapid Evolution of the Canonical Stack for Machine Learning

Explore the evolution of AI/ML tech stacks and the emergence of a canonical stack for machine learning, democratizing AI for companies of all sizes.

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

MLOps at Acerta - Automating the Machine Learning Lifecycle for Manufacturing

Automate the machine learning lifecycle for manufacturing, enhancing efficiency, scalability, and reproducibility. Learn to build a robust MLOps platform for production-grade systems.

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

Iterative Development Workflows for Building AI Applications

Explore iterative development workflows for AI applications using Snorkel framework. Learn guided error analysis, quality improvement techniques, and collaboration with domain experts.

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

MLOps with PyCaret - Practical Demo for Data Science Workflows

Supercharge your data science team's productivity with PyCaret, an open-source ML library for efficient model preparation and deployment in Python.

• YouTube
• 1 hour 11 minutes
• Self-Paced
• Free Video

The AI Captain: A Study of Machine Learning at the Edge

Explore the Mayflower project's AI breakthroughs and edge computing applications in maritime technology. Gain insights into cutting-edge AI implementation for autonomous navigation.

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

PANAMA: In-Network Aggregation for Shared Machine Learning Clusters

Explore PANAMA, a novel in-network aggregation framework for distributed ML training, enhancing performance and resource sharing in shared clusters.

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

From 12 Months to 30 Days: Accelerating AI Deployment Through MLOps

Discover Ecolab's MLOps journey: Learn how they built a cloud architecture enabling rapid AI service deployment every 30-90 days to proactively address risks.

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

ModelOps: Designing and Building a Model Life Cycle for AI Operationalization

Learn to design and build a model life cycle for operationalizing AI models, incorporating industry best practices and addressing key considerations for successful implementation.

• YouTube
• 1 hour 26 minutes
• Self-Paced
• Free Video

Productionize with Kubeflow Orchestration and Feast in GCP - Workshop

Develop a machine learning framework using Kubeflow, Feast, and Kafka in GCP. Learn to manage data features, serve endpoints, and monitor model performance with Grafana for real-time and batch predictions.

• YouTube
• 1 hour 48 minutes
• Self-Paced
• Free Video

Systematic Approaches and Creativity - Building DoorDash's ML Platform During the Pandemic

Explore DoorDash's ML platform development journey, focusing on collaboration, guardrails, principled approaches, and architecture powering billions of daily predictions for diverse use cases.

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

Deploying an End-to-End ML Pipeline with AWS SageMaker - What Amazon Didn't Tell You

Navigate the challenges of setting up an ML pipeline on AWS SageMaker, from GroundTruth to Endpoints, with insights on overcoming unexpected hurdles in the AWS ecosystem.

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

Training Models on the Cloud without Changing Code - GRID.AI

Discover how to effortlessly train models on the cloud, automate engineering tasks, and focus on machine learning with Grid AI's innovative approach to research workflows.

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

Fixing Data Quality at Scale with Data Observability

Learn to fix data quality issues at scale using data observability techniques. Discover how to proactively address errors in product dashboards, ML models, and datasets for more reliable and efficient data operations.

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