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

Introduction to AIOps: Modernizing IT Operations with AI

Intel AI Deployment and Optimization Recipes

Intel AI Technologies for Efficient Model Deployment

MLOps Foundations with MLflow and Hugging Face

Connecting AI with Model Context Protocol (MCP)

Machine Learning Model Performance and Maintenance

MLOps Foundations: Deploying and Monitoring ML in Production

Building AI Agents with Model Context Protocol (MCP)

Modernizing Applications with Generative AI on AWS

Building AI Integrations with Model Context Protocol

Open Source LLMOps: Building and Deploying AI Applications

Model Context Protocol (MCP) for Beginners: Connect AI to Real-World Data

Rust for LLMOps: Deploying Large Language Models

Practical Workflow Automation with Model Context Protocol (MCP)

Building Machine Learning Pipelines with Python

Optimizing AI Agent Investments and Performance on Azure

Machine Learning Model Deployment to Batch Endpoints

Managing the Semantic Model Development Lifecycle with Git and Pipelines

Deploying Language Models in the AI Foundry Catalog

Introduction to AI Agent Tooling, MCP, and Execution Environments

Responsible AI for Language Models in Azure Databricks

Deploying Models to Managed Online Endpoints for Real-Time Inference

GitHub Actions Environments for Secure CI/CD and MLOps

Threat Modeling: Designing Effective Data-Flow Diagrams