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

LLM Inference Infrastructure: Cost and Latency Optimization

Designing Multi-Server MCP Architectures for AI Agents

Custom AutoML Monitoring in ML.NET Solutions

Scaling TSP Solutions with Cloud Compute and Python

DeepSeek AI: Understanding Limitations, Risks, and Controversies

MLOps Orchestration with SageMaker Pipelines and Governance

Multi-Provider AI Integration: Model Routing with OpenRouter

SageMaker Security, Governance, and Cost Management Foundations

Deploying Machine Learning Models to Production with SageMaker

Practical Model Compression and Knowledge Distillation

Building Custom AutoML Monitors for ML.NET Pipelines

Building Single-Server MCP Architectures for AI Agents

Machine Learning Model Deployment and Production Monitoring

Designing Low-Latency LLMs for Code Generation

Data Pipelines for LLMOps: Document Ingestion and Chunking

Back-of-the-Envelope Resource Estimation for Model Deployment

Embedding Models and Zero-Downtime ML System Design

MCP Logging and Debugging for Reliable AI Agents

LLMOps Foundations: Managing Large Language Models in Production

Generative AI Model Adaptation and Resilient Training with SageMaker

LLMOps Foundations: Building with Agents and Multimodal Models

Secure LLM Operations: Compliance and Safety in LLMOps

Evaluating Generative AI Models with Bedrock

ML Model Monitoring and Drift Detection