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.
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.
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.
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.
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.
Beyond the Model Zoo - Optimizing Foundation Models for Your Application
Optimize foundation models for specific applications, exploring techniques beyond pre-trained models to enhance performance and efficiency in machine learning projects.
Deploying Generative AI Models - Best Practices and Interactive Example
Explore best practices for deploying generative AI models at scale, with an interactive example using UbiOps for serverless and cloud-agnostic implementation.
Deploy High Scale ML Models Without the Hustle
Learn to build and operate production-grade ML systems, from model training to deployment and monitoring, using the Qwak platform for efficient, high-scale solutions.
Deploying Model Artifacts in Hard Mode
Automate and streamline complex model deployment processes across multiple products. Learn strategies for overcoming documentation gaps, ambiguous terminology, and legacy systems in a collaborative remote environment.
Best Practices for Deploying MLOps Solutions in Enterprise
Explore best practices for deploying MLOps solutions in enterprise environments. Learn from industry experts about common pitfalls, avoidance strategies, and early adoption techniques for model lifecycle management.
Intro to MLOps: Build, Deploy, and Retrain a Model in Production
Build, deploy, and retrain a production ML model using open-source tools. Learn robust MLOps pipeline techniques for quick model deployment and continuous improvement.
Automatic ML Model Containerization: Best Practices and Tools
Explore best practices for containerizing ML models for production inference. Learn about secure, multi-tenant image builds, avoiding lock-in, and tools like chassis.ml and Open Model Interface for seamless integration.
A Zero Downtime Setup for Models: How and Why
Discover techniques for achieving zero-downtime model updates and handling failures in production environments. Learn a robust setup to ensure continuous availability and reliability of deployed models.
Taking MLOps 0-60: How to Version Control, Unify Data and Manage Code Lifecycles
Explore MLOps fundamentals, focusing on version control, data unification, and code lifecycle management for reliable and adaptable machine learning systems.
MLOps is Just HPC in Disguise - A Real-World, No-Nonsense Guide to Upgrading Your Workflow
Upgrade your ML workflow with efficient tools and techniques for automating tasks, managing resources, and streamlining production deployment in this practical guide to MLOps.
Generalizing Diversity - Machine Learning Operationalization for Pharma Research
Explore ML operationalization in pharma research, addressing challenges of transitioning from one-off to repeated model usage in critical workflows. Learn strategies for leveraging MLOps platforms in enterprise settings.
MLOps with Dataiku - Considerations for Model Deployment and Monitoring
Explore key MLOps concepts, deployment readiness, and model monitoring strategies. Learn how Dataiku streamlines MLOps processes for efficient AI industrialization.
Implementing SecMLOps at Every Stage of the ML Pipeline
Implement SecMLOps at every stage of the ML pipeline to address security challenges, comply with data privacy laws, and ensure robust ML system architecture.
Deploying and Managing Machine Learning Models at Scale - A Hands-On Workshop
Deploy and manage ML models at scale. Hands-on workshop covers creation, deployment, monitoring, and management using real-life scenarios. Gain practical experience with Seldon Deploy, explainers, and outlier detection.