PickAClass · AI, Tech & Engineering · Generative AI & Deep Learning

MLOps for Generative AI

Focus on the operational challenges of deploying, monitoring, and maintaining large-scale generative models. Learn about model versioning, performance tracking, cost management, and building robust production pipelines.

MLOps for Generative AI

MLOps and LLMOps: Deploying and Scaling AI Models

MLOps with Vertex AI: Feature Management Foundations

Feature Management in MLOps with Vertex AI

Managing Features in Vertex AI: An MLOps Foundation

MLOps with Vertex AI: Managing Features in Production

Model Evaluation and MLOps with Vertex AI

MLOps Foundations: Model Evaluation with Vertex AI

Vertex AI Model Evaluation: MLOps Fundamentals

Model Armor Essentials: Securing AI and LLM Deployments

Securing AI Deployments with Model Armor

Model Context Protocol: Building Production-Ready MCP Integrations

MLOps Foundations: Operationalizing Scalable Machine Learning Models

Designing Reusable AI and Machine Learning Pipelines

Optimizing AI Inference Speed and Accuracy

Machine Learning Codebase Optimization and Management

Optimizing ML Workflows: Version, Reproduce, and Track Models

Optimizing Foundation Models for Practical Applications

AI Deployment: Orchestration, Evaluation, and Monitoring

MLOps and Generative AI Deployment in Azure

AI Agents and MLOps: Building Production-Ready AI Systems

Building Modular Monoliths with Modern .NET

LLMOps Foundations: Deploy and Manage Large Language Models

Hands-On Network Simulation with Cisco Modeling Labs (CML2)

Architecting Context-Driven AI: MCP for Leaders