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MLOps
Understanding MLOps
Basics of MLOps
History of MLOps
MLOps Essential for ML Pipelines
Breaking Down the ML Lifecycle
MLOps and DevOps: Differences
Role of MLOps in the business
MLOps Tools
Overview of MLOps Tools
Types of MLOps Tools
MLOps Tools
Grasping Tool Applications
Choosing the right MLOps Tools
Mastering tool implementation
MLOps Environment Setup
Preparing local environment
Cloud setup for MLOps
Grasping Virtual Environments
Studying Docker and Kubernetes
Setting up CI/CD pipelines
Security and permissions
Grasping Machine Learning
Basics of Machine Learning
ML Algorithm Types
Feature engineering & selection
Model Selection and Training
Data preprocessing in ML
Validation & Metrics
Merging ML with MLOps
Grasping the integration process
Checking Model compatibility
Model versioning
A/B Testing and canary releases
Learning rollback approaches
Monitoring and logging
CI/CD in MLOps
Learning CI/CD systems in MLOps
Exploring CI/CD in MLOps
Version control systems in MLOps
Testing in MLOps
Deploying using CI/CD pipelines
Monitoring deployment
Data versioning in MLOps
Valuing data versioning
Data versioning tools (e.g., DVC)
Data versioning in projects
Data versioning best practices
Strategies for large datasets
Dealing with sensitive data
MLOps practices
MLOps workflow overview
Customizing MLOps workflows
Frequent mistakes and fixes
Review MLOps standards
ML model life cycle management
Case Studies
Enhancing MLOps efficiency
Boosting MLOps efficiency tips
Customizing MLOps Processes
Scaling MLOps Organization-wide
Enhancing Reusability in MLOps
Resource Management Insights
Cost Optimization Strategies
People & Culture in MLOps
Role of Team Culture
Defining Team Roles
Best Practices for Team Structure
Communication in MLOps Teams
Ensuring Transparency in MLOps
MLOps Management Perspective
MLOps Tracking & Logging
Monitoring & Logging Basics
Impact of Observation in MLOps
Exploring Monitoring Tools
Setting Up Monitoring & Alerts
Log Review for MLOps
Feedback in MLOps workflow
Future of MLOps
Studying new MLOps trends
Evaluation of current trends
AI and ML effects on MLOps
MLOps success stories
Predictions for the future of MLOps
Insights from leaders in MLOps
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For detailed explanations and theory, visit the
Complete MLOps
.