WalkSelf · 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

Evaluating AI Product Performance: A Guide for Product Managers

ML Model Deployment and Testing Patterns on AWS

Kubeflow Katib for Hyperparameter Tuning and Optimization

Foundation AI Models: Practical Applications and Core Concepts

Structured Outputs and Tool Calling in AI Workflows

SageMaker Inference Auto Scaling and Cost Optimization

Model Context Protocol: Connecting AI Agents to Data and Tools

PyTorch Model Deployment with ONNX and OpenVINO

Building Single-Server MCP Resources for AI Context

MLOps to LLMOps: Managing Large Language Models in Production

Model Context Protocol (MCP) Fundamentals for AI Agent Integration

Managing and Monitoring Foundation Models on AWS

Automating ML Training with SageMaker Autopilot, Debugger, and Experiments

Model Context Protocol for AWS Agentic AI Systems

ML Deployment: Continual Learning and Feedback Loops

Inference Optimization and Performance in AWS Bedrock

The 4D Framework for LLMOps: Build Reliable AI Systems

SageMaker Inference Patterns for AI Model Deployment

Safeguarding Foundation Models in AWS Agentic AI Systems

MLOps for AI Product Managers: Building Reliable AI Products

ML Monitoring in Production: Designing Scalable MLOps Pipelines

Integrating MCP with LlamaIndex for Agentic AI Tools

Mixture of Experts: Designing Scalable and Efficient AI Models

ML Monitoring, Maintenance, and Security on AWS