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.
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.
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.
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.
Machine Learning Optimizations and Strategy for Post-Covid Era
Explore ML risk mitigation techniques for post-pandemic model deployment, monitoring, and recalibration across industries, with focus on ethical practices and automated AI/ML solutions.
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.
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.
Complete ML Lifecycle with MLflow - Learn Its Four Components
Explore MLflow's four components to simplify the machine learning lifecycle, from experimentation to production, enhancing reproducibility and accelerating development.
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.
Lessons Learned: The Journey to Real-Time Machine Learning at Instacart
Explore Instacart's transition to real-time machine learning, focusing on infrastructure, use cases, challenges, and key lessons for implementing ML in a dynamic marketplace.
Avoid ML OOps with ML Ops - A Modular Approach to Scaling End-to-End ML Platforms
Discover a step-by-step approach to building a scalable ML infrastructure. Learn to identify key areas for improvement, implement modular components, and enhance your end-to-end ML lifecycle for efficient, automated operations.
Evolution of ML Training and Serving Infrastructure at Pinterest Ads
Explore Pinterest Ads' ML infrastructure evolution, from logistic regression to transformer models, and learn key challenges and lessons in revolutionizing ad capabilities.
Quality Assurance in Machine Learning: Enhancing Trust and Reliability
Explore quality assurance in machine learning, its importance, and strategies for enhancing AI trustworthiness through lessons from other disciplines and best practices in MLOps and data science.
Learning from Extremes: What Fraud-Fighting at Scale Can Teach Us About MLOps Across Domains
Explore how anti-fraud MLOps practices can simplify architectures, enhance observability, and boost agility across various ML domains.
Low-latency Model Inference in Finance - A Close Look at Seldon V2
Explore low-latency model inference in finance using Seldon V2, focusing on high resilience and rapid service response for critical business applications.
Synthetic Data: Generative AI for Enhanced Data Quality in the Era of Foundational Models
Explore synthetic data's transformative potential in AI, focusing on data quality, augmentation, bias, and privacy for foundational language models.
Supercharging Recommender Systems - Unleashing the Power of Distributed Model Training
Explore distributed model training techniques to enhance recommender systems, boosting performance and scalability for production environments.
Customizable RAG Workflows with Your Own Data
Build customizable RAG workflows using OSS tools and your own data to optimize GenAI applications for corporate environments. Learn best practices and advanced techniques.
Applying Responsible AI with the Open-Source LangTest Library
Explore open-source tools for building safe, fair AI models. Learn to address robustness, bias, and data leakage challenges using the LangTest library in MLOps workflows.
Wanted: A Silver Bullet MLOps Solution for Enterprise
Explore key learnings from a 4-year MLOps journey in pharmaceutical research, addressing challenges in tool selection, user diversity, and scalable solutions for operationalizing ML models.
Running Multiple Models on the Same GPU on Spot Instances
Optimize ML inference costs through GPU fractionalization and Spot instances. Increase GenAI application throughput while reducing expenses.