LearnFlat · 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

Foundation Models and Task-Specific AI Explained

Foundation Models on AWS: Principles and Applications

Building Resource Features for MCP AI Applications

Setup Python Virtual Environments for Machine Learning

Agentic AI System Reliability: Foundations for Scale

OpenAI Codex Integration: External Tools with MCP

ML.NET Setup: Your Local Development Environment

Rust for MLOps: Building High-Performance ML Pipelines

Practical Small Language Model Deployment

Introduction to Kubeflow for AI/ML

Introduction to AI Red Teaming: Securing Machine Learning Systems

LLMOps Fundamentals: Deploying and Managing Large Language Models

Rust for LLM Operations and High-Performance LLMOps

Python Foundations for MLOps and Workflow Automation

Open Source LLMOps and Data Engineering Fundamentals

Introduction to Open Source LLMOps

MLOps Foundations: Managing ML Lifecycles with MLflow and Hugging Face

Azure LLMOps: Deploy and Manage Large Language Models

DevOps, DataOps, and MLOps: Building End-to-End AI Pipelines

Designing AI Agents with AWS Bedrock

Introduction to Machine Learning Pipelines with Kubeflow

Apache Airflow Fundamentals: Build and Orchestrate Data Pipelines

MLOps Foundations: Deploying Machine Learning Models to Production

Environmental Data Analysis and Computing for Beginners