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

Process Isolation in Elixir: Building Boundaries with GenServer

Logging and Debugging for Reliable Machine Learning Systems

Testing Machine Learning Pipelines with Fixtures and Mocks

Managing Elixir Supervisors: Custom Lifecycle Control in OTP

Reliable Machine Learning: Implementing Runtime Checks and Validation

Multi-Model AI Orchestration with OpenRouter

Designing High-Availability Real-Time Inference Endpoints in SageMaker

Saving and Loading Custom Machine Learning Models

Designing Production RAG Applications: Planning and Scoping

ML System Design: Selecting and Justifying Task Types for Interviews

Foundational AI Models with AWS Bedrock

LLMOps Fundamentals: Operating Large Language Models in AI Systems

AWS Ground Truth and Human-in-the-Loop Workflows for Machine Learning

AutoML in ML.NET: Automating Machine Learning for C# Developers

ML Experiment Tracking and Automated Report Generation

Integrating ONNX Models in ML.NET for Cross-Platform Deployment

ML Observability and Drift Detection with SageMaker

Connecting AI Tools Using Model Context Protocol (MCP)

ML Deployment Pipelines: Safe and Reliable Model Updates

ML Model Optimization: Quantization, Pruning, and Production Design

Designing Multi-Model AI Ecosystems for Scalable Systems

AWS Glue Data Quality for Automated ML Pipelines

Selecting AWS Managed AI Services for Machine Learning Solutions

OpenRouter Foundations: Unified Access to Modern AI Models