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

Machine Learning Experiment Tracking and Reproducibility

Resilient AI Apps with Model Routing and Fallbacks

Structuring dbt Models: Folders, Tags, and Smart Selection

Resource Modeling in Rails with Active Model

Multi-Model AI Workflow Design with OpenRouter

Regression Analysis and AutoML with the H2O Framework

SageMaker Model Monitor for ML Quality and Drift Detection

Experiment Tracking and Model Lineage in SageMaker

Deploying Generative AI Models with SageMaker JumpStart and Neo

Introduction to Kubeflow: Managing Machine Learning Workflows

Machine Learning Model Failures: Prevention and Debugging

Command-Line Arguments in Python: Argparse for ML Pipelines

Designing Low-Latency Machine Learning Serving Systems

Foundation Models in Practice: Applications, Safety, and Selection

Machine Learning Model Evaluation and Performance Metrics

Multi-Task and Multi-Objective Machine Learning System Design

AI Model Testing and Troubleshooting for Continuous Maintenance

Model Context Protocol (MCP) and Communication Fundamentals

Integrating Bedrock and SageMaker for Scalable AI Workflows

Model Selection and Feasibility for AWS ML Projects

Python ML Library Packaging for Reliable Model Deployment

Managing Python and Machine Learning Configurations with OmegaConf

ML.NET AutoML: Configuring Custom Machine Learning Pipelines

Benchmarking AI Models: Evaluating Cost, Speed, and Quality