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Machine Learning
Machine Learning in Production
1. Introduction to MLOps
2. Project Scoping and System Design
3. Data Engineering for Production
4. Model Development for Production
5. Model Deployment
6. Monitoring, Logging, and Maintenance
7. MLOps Infrastructure and Tooling
8. Governance, Ethics, and Security
MLOps Infrastructure and Tooling
Cloud Platforms for MLOps
Amazon Web Services
SageMaker
Managed Training and Deployment
Model Monitoring Tools
SageMaker Pipelines
Additional AWS Services
S3 for Data Storage
Lambda for Serverless Computing
ECS and EKS for Containerization
Google Cloud Platform
Vertex AI
End-to-end ML Pipelines
Model Registry and Monitoring
AutoML Capabilities
Additional GCP Services
BigQuery for Data Warehousing
Cloud Functions for Serverless
GKE for Kubernetes
Microsoft Azure
Azure Machine Learning
Automated ML Pipelines
Model Management and Deployment
MLOps Capabilities
Additional Azure Services
Azure Data Factory
Azure Functions
Azure Kubernetes Service
Multi-cloud Strategies
Cloud Portability
Vendor Lock-in Avoidance
Cost Optimization
Open-Source MLOps Tools
Workflow Orchestration
Apache Airflow
DAG Design
Task Dependencies
Scheduling and Monitoring
Kubeflow
Kubernetes-native Workflows
Pipeline Components
Multi-step Workflows
Prefect
Modern Workflow Engine
Dynamic Workflows
Error Handling
Experiment Tracking
MLflow
Experiment Logging
Model Registry
Model Serving
Weights & Biases
Experiment Visualization
Hyperparameter Optimization
Collaboration Features
Neptune
Metadata Management
Model Monitoring
Team Collaboration
Model Serving
Seldon Core
Kubernetes-native Serving
Advanced Deployment Patterns
Explainability Integration
KServe
Serverless Inference
Multi-framework Support
Autoscaling
BentoML
Model Packaging
API Generation
Deployment Automation
Data Versioning
DVC
Git-like Data Versioning
Pipeline Tracking
Remote Storage Integration
Pachyderm
Data Lineage
Distributed Processing
Version Control
Feature Stores
Feast
Open Source Feature Store
Online and Offline Serving
Feature Registry
Tecton
Managed Feature Platform
Real-time Features
Feature Monitoring
Managed MLOps Platforms
Platform Evaluation Criteria
Feature Completeness
Integration Capabilities
Scalability
Cost Structure
Vendor Comparison
Databricks MLflow
DataRobot
H2O.ai
Domino Data Lab
Integration Considerations
Existing Tool Integration
Data Source Connectivity
Security and Compliance
Cost and Vendor Lock-in
Total Cost of Ownership
Migration Strategies
Exit Planning
Infrastructure as Code
Terraform for MLOps
Resource Provisioning
Environment Management
State Management
Kubernetes Operators
Custom Resource Definitions
Operator Development
Lifecycle Management
Helm Charts
Package Management
Configuration Management
Release Management
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6. Monitoring, Logging, and Maintenance
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8. Governance, Ethics, and Security