Machine Learning Engineering

17 January 2025 - less than 1 min read time
Tags: Machine Learning AI MLOps

Machine Learning Engineering

Core ML Concepts

1. Machine Learning Fundamentals

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

2. Model Development

• Feature Engineering
• Model Selection
• Training Strategies
• Evaluation Metrics

3. Deep Learning

• Neural Networks
• CNN
• RNN/LSTM
• Transformers

MLOps

1. ML Infrastructure

• Model Training
• Model Serving
• Model Monitoring
• Resource Management

2. ML Pipeline

• Data Pipeline
• Training Pipeline
• Deployment Pipeline
• Monitoring Pipeline

3. Version Control

• Data Versioning
• Model Versioning
• Experiment Tracking
• Code Management

Model Deployment

1. Deployment Strategies

• Model Serving
• A/B Testing
• Canary Deployment
• Blue-Green Deployment

2. Scalability

• Load Balancing
• Auto-scaling
• Resource Optimization
• Performance Tuning

3. Monitoring

• Model Performance
• System Metrics
• Data Drift
• Concept Drift

Tools & Technologies

1. ML Frameworks

• TensorFlow
• PyTorch
• Scikit-learn
• XGBoost

2. MLOps Tools

• Kubeflow
• MLflow
• DVC
• Weight & Biases

3. Cloud Platforms

• AWS SageMaker
• Azure ML
• Google Vertex AI
• Custom Solutions

Best Practices

1. Development

• Code Quality
• Testing
• Documentation
• Reproducibility

2. Production

• Monitoring
• Logging
• Error Handling
• Performance

3. Team Collaboration

• Version Control
• Code Review
• Knowledge Sharing
• Documentation

Advanced Topics

1. AutoML

• Automated Feature Engineering
• Neural Architecture Search
• Hyperparameter Optimization
• Model Selection

2. Edge ML

• Model Optimization
• Edge Deployment
• Hardware Acceleration
• Power Efficiency

3. Responsible AI

• Fairness
• Transparency
• Privacy
• Security

Future Trends

1. Large Language Models

• Foundation Models
• Fine-tuning
• Prompt Engineering
• Efficient Training

2. AI Acceleration

• Hardware Acceleration
• Model Optimization
• Distributed Training
• Quantum ML

3. Emerging Applications

• Multimodal Learning
• Few-shot Learning
• Federated Learning
• Continual Learning