Train models, tune parameters, and deploy to production or the edge with MATLAB and Simulink Training by Tonex
Train models, tune parameters, and deploy to production or the edge with MATLAB and Simulink is a comprehensive training course by Tonex equips professionals with the essential skills and knowledge needed to develop, optimize, and deploy machine learning models effectively. Participants will gain hands-on experience in training models, fine-tuning parameters, and deploying them to production environments or edge devices. This course covers the complete lifecycle of model development, ensuring that your models perform optimally in real-world scenarios.
Join us in this comprehensive course to master the art of training, tuning, and deploying machine learning models for real-world applications. Enhance your skills and stay ahead in the rapidly evolving field of artificial intelligence and machine learning.
Learning Objectives:
By the end of this course, participants will be able to:
- Learn the fundamentals of machine learning and its applications.
- Train machine learning models using popular frameworks like TensorFlow and PyTorch.
- Fine-tune model parameters to achieve superior performance.
- Implement deployment strategies for both production and edge computing environments.
- Evaluate and optimize models for efficiency and accuracy.
- Troubleshoot common issues in model deployment and maintenance.
Audience:
This course is designed for:
- Data scientists and analysts
- Machine learning engineers
- Software developers
- IT professionals
- AI enthusiasts and researchers
- Anyone looking to deploy machine learning models effectively
Course Outline:
Introduction to Machine Learning
- Understanding machine learning concepts
- Applications of machine learning in various industries
- Supervised vs. unsupervised learning
- Data preprocessing and feature engineering
- Model evaluation and validation
- Ethics and bias in machine learning
Model Training
- Data collection and preparation
- Building and training models using TensorFlow
- Implementing models with PyTorch
- Hyperparameter tuning and optimization techniques
- Cross-validation and model selection
- Transfer learning and pre-trained models
Fine-Tuning Model Parameters
- Exploring optimization algorithms
- Gradient descent and its variants
- Batch size, learning rate, and regularization
- Monitoring model performance
- Early stopping and model checkpoints
- Ensembling and stacking models
Model Deployment Strategies
- Overview of deployment environments (cloud, on-premises, edge)
- Containerization with Docker
- Deploying models with Kubernetes
- Serverless computing for model deployment
- Edge computing and IoT device deployment
- Model versioning and management
Model Evaluation and Optimization
- Metrics for model evaluation (accuracy, precision, recall, F1-score)
- A/B testing and experimentation
- Monitoring deployed models for drift and concept shift
- Model retraining and continuous improvement
- Addressing bias and fairness in deployed models
- Security considerations in model deployment
Troubleshooting and Maintenance
- Common challenges in model deployment
- Debugging and error handling
- Scaling and resource management
- Performance optimization techniques
- Model maintenance best practices
- Compliance and regulatory considerations