MLOps World: Machine Learning in Production Courses

From Concept to Production: Building a Production-Ready ML Pipeline - Part 2

Develop a production-ready ML pipeline from concept to deployment using Python, Kubeflow, and Google Cloud Platform. Master data prep, model training, and orchestration techniques.

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

MLOps vs ModelOps - Differences, Importance, and Best Practices

Explore the differences between MLOps and ModelOps, focusing on continuous monitoring, automated remediation, and feedback loops for AI and analytical models in enterprise settings.

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

Security Audits for Machine Learning Attacks

Explore common machine learning security attacks and learn effective remediation strategies to protect your models from harmful outcomes and data exposure. Enhance your organization's security practices.

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

5 Governance Capabilities You Need in MLOps - Workshop Session

Explore 5 essential MLOps governance capabilities to effectively implement and control machine learning models in production environments. Gain insights on assessing organizational maturity and achieving full MLOps maturity.

• YouTube
• 1 hour 7 minutes
• Self-Paced
• Free Video

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

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