Hands-on Workshop: Create Machine Learning Retraining Pipelines
Develop ML retraining pipelines using Kubernetes and Kubeflow. Master advanced techniques for efficient model updates and deployment in production environments.
Production ML for Mission-Critical Applications
Explore rigorous approaches for deploying ML in production, addressing scalability, testability, and performance analysis. Learn about TensorFlow Extended for large-scale applications.
How to MLEM Your Models to Production
Explore MLEM, an open-source tool for storing, accessing, packaging, and deploying machine learning models efficiently in various production scenarios.
Eliminating AI Risk: Building an AI Firewall to Prevent Model Failures
Explore root causes of AI model failures and learn a rigorous framework for eliminating AI risk, including building an AI firewall to prevent model errors.
Hands-on: A Beginner-Friendly Crash Course to Kubernetes
Découvrez les bases de Kubernetes et des applications Cloud Native. Explorez l'orchestration de conteneurs et le déploiement d'applications dans un environnement cloud moderne.
What Engineers Don't Get About Working with Data Scientists
Explore challenges and solutions in engineer-data scientist collaborations for operational ML. Gain insights on effective teamwork, process integration, and overcoming common hurdles in MLOps.
Supporting Sales Forecasting at Scale for Canada's Largest Grocery Store
Explore MLOps platform setup on Google's Vertex AI for sales forecasting at Canada's largest grocery chain. Learn to track ML lifecycle stages and expedite data science projects from exploration to production.
Building Production ML Monitoring from Scratch - Live Coding Session
Build a cloud-native ML monitoring stack using open-source tools. Create a live web dashboard to measure model drift, feature statistics, and performance metrics in production, with Python-based custom metrics integration.
UnionML: A Microframework for Building Machine Learning Applications
Explore UnionML, a microframework simplifying ML application development. Learn to streamline the process from research to production, addressing common challenges in the ML lifecycle.
How to Reduce ML Computing Costs: Building Efficient Multi-Cloud Infrastructure
Discover cost-effective ML infrastructure strategies, including hybrid cloud architectures, spot instances, and fractional GPUs. Learn to optimize computing costs by up to 83% across multiple environments.
Building Real-Time ML Features with a Feature Platform
Explore challenges in deploying ML pipelines and learn how feature stores and platforms solve data problems for production ML, enabling real-time processing and scaling.
Solving MLOps From First Principles - A Framework to Reduce Complexity
Develop a framework to tackle MLOps challenges, select appropriate tools, and overcome "MLOps Fatigue" using first principles thinking and practical problem-solving strategies.
MLOps for Personalized Reinforcement Learning in Mobile Gaming - Rovio's Approach
Explore Rovio's MLOps infrastructure for personalized reinforcement learning in mobile gaming. Learn how to leverage cloud services for individualized player experiences without traditional A/B testing.
The Critical Things You Have to Build to Transform Your Company to be ML Driven
Discover key components for transforming your company into an ML-driven organization. Learn strategies for overcoming challenges in the machine learning development lifecycle and deploying models effectively.
Transformative Power of Machine Learning in the Real World
Explore the transformative impact of machine learning across industries, focusing on real-world applications and their potential to revolutionize healthcare and beyond.
What Every Product Manager Delivering AI Solutions Should Know
Explore key insights for product managers in AI/ML, focusing on user adoption, team communication, and impactful solution development. Learn from industry experts' experiences and best practices.
Enhance Cost Efficiency in Domain Adaptation with PruneMe
Optimize LLMs through layer pruning, enabling cost-effective domain adaptation and model merging for improved performance in specific applications.
MLOps on Highly Sensitive Data - Enhancing Security and Compliance
Enhance security and compliance in MLOps for sensitive data using Kubernetes, blockchain tokenization, and confidential computing. Learn from a life sciences case study.
A Guide to Building a Continuous MLOps Stack with GCP and Superwise
Dive into MLOps CI/CD pipeline automation with GCP and Superwise. Learn to build, deploy, and monitor continuous ML pipelines, focusing on automations and production-first insights for issue detection and resolution.
A GitOps Approach to Machine Learning in Production
Explore GitOps principles for ML in production. Learn to apply GitOps to deployment, onboarding, monitoring, and model lifecycle stages for enhanced traceability and productivity.
It's All About the Data - Continuously Improve ML Models the Data-Centric Way
Explore data-centric AI practices to enhance ML model performance. Debug input data, analyze datasets, and identify post-deployment issues using open-source whylogs for continuous improvement.