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

Automated Model Deployment with GitHub Actions and MLOps

Governing AI Workloads with AI Foundry

Batch Predictions with Deployed Models in Fabric

LLMOps Foundations in Azure Databricks

OMOP Common Data Model Transformations for Healthcare Analytics in Fabric

AI Orchestration: From Local Models to Cloud Deployment

Automated Machine Learning Deployment and Evaluation

MLOps Foundations: Automating, Optimizing, and Monitoring ML Models

Automated Machine Learning Validation and Safe Model Promotion

Optimizing Language Models with Azure Machine Learning

Developing Reliable AI Agents with Model Context Protocol (MCP)

Deploying and Maintaining Production AI Systems

Deploying Open Models and LLMs

Detecting Real-Time Anomalies and Outliers in AI Systems

Deploying AI Models with Cloud Run: Serverless Inference Guide

Multimodal AI Systems: Fine-Tuning, Fusion, and MLOps

Feature Engineering and Model Evaluation for Production

MLOps and Feature Management with Vertex AI

MLOps with Vertex AI: Model Evaluation Foundations

Model Evaluation in MLOps with Vertex AI

Vertex AI Model Evaluation for MLOps

Vertex AI Model Evaluation and MLOps for Beginners

Model Context Protocol (MCP) Foundations: Integrate AI with Data and Tools

MLOps and Responsible AI Practices