MLOps World: Machine Learning in Production Courses

MLOps Platform Architecture for End-to-End ML Pipelines

Explore Avast's MLOps journey, tooling, and cultural shift for enhanced ML productivity. Learn strategies for model tracking, storage, orchestration, and E2E deployments in large-scale ML pipelines.

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

Keeping Up With ML Models in Production: Mitigating Performance Drift

Learn strategies to mitigate ML model performance drift in production environments, incorporating non-technical collaborators and integrating ML into agile frameworks for long-term success.

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

Multi-armed Bandits in Production at Stitch Fix

Explore scaling challenges and solutions for multi-armed bandits in online experimentation, including a new method for efficient, deterministic Thompson sampling at Stitch Fix.

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

Breaking the Monolithic ML Pipeline with a Feature Store

Explore how a Feature Store decomposes ML pipelines, separating feature and model processes for improved efficiency and team collaboration in machine learning operations.

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

Taking Aim at Bias in AI - Workshop Sessions

Explore strategies to identify and mitigate bias in AI systems, enhancing fairness and ethical decision-making in machine learning applications.

• YouTube
• 2 hours
• On-Demand
• Free Video

Building Pipelines with Kubeflow - Hands-on Workshop

Discover Kubeflow pipelines: create environments, build and upload pipelines, and set up retraining schedules in this hands-on workshop for MLOps practitioners.

• YouTube
• 3 hours 43 minutes
• On-Demand
• Free Video

HoloClean and Kamino - Structured Learning for Private Data Generation

Explore structured learning for private data generation using HoloClean and Kamino frameworks. Learn to preserve data correlations while ensuring privacy in synthetic data instances.

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

MLOps with Orchestration: Accelerating Deployment of AI - Workshop Sessions

Accelerate AI deployment with MLOps orchestration. Learn to integrate tools, streamline processes, and handle complex use cases for efficient ML application development and continuous delivery in production environments.

• YouTube
• 1 hour 49 minutes
• On-Demand
• Free Video

Next-Gen AI Processors and Why We Need to Think Differently About Software 2.0

Explore next-gen AI processors, computational challenges in machine learning, and innovative approaches to efficient processing beyond traditional GPU solutions.

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

How Not to Let Your Data and Model Drift Away Silently

Discover strategies to prevent data and model drift, ensuring ML models maintain performance and value over time in production environments.

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

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