Model Management & Experiment Tracking for Quality Management 

This course equips quality professionals with the knowledge and skills to effectively manage the machine learning lifecycle within quality control processes. You'll delve into the crucial stages of model training, serving, validation, and experiment tracking. This empowers you to build, deploy, and monitor machine learning models for quality control tasks, ensuring their reliability, performance, and continuous improvement. 

• Understand the importance and challenges of model management and experiment management in the Healthcare & Life Sciences industries

• Implement version control and organization strategies for machine learning models and datasets

• Design and conduct experiments to optimize model hyperparameters and evaluate model performance

• Apply techniques for model reproducibility, scalability, and deployment in production environments

• Develop model monitoring and maintenance strategies to ensure long-term reliability and performance

1. The core stages of model development:
   
   

• Introducing the core stages of model development for quality control (training, serving, validation) and highlighting the significance of model management.

• Key aspects of data preparation for model training in quality control tasks, exploring techniques for data versioning and managing data quality.

• Model Selection and Hyperparameter Tuning: Understanding different machine learning algorithms suitable for quality control tasks and exploring strategies for selecting and optimizing model hyperparameters for optimal performance.

• Case Study 1: Training a Model for Defect Detection in X-ray Images: Analyzing a real-world scenario of training a deep learning model to identify defects in X-ray images of manufactured parts, emphasizing data preparation and hyperparameter tuning.

2.  Model Serving:
   
   

• Model Serving and Scalability: Delving into the concept of model serving, exploring techniques for packaging and containerizing models for deployment in production environments.

• Understanding different deployment strategies for serving machine learning models in quality control workflows, considering factors like real-time response times and scalability for high data volume.

• Model Validation and Monitoring: Introducing key metrics for evaluating model performance in quality control tasks (e.g., accuracy, precision, recall), exploring techniques for drift detection, and understanding the importance of continuous monitoring.

• Case Study 2: Monitoring a Model for Customer Sentiment Analysis: Analyzing a real-world scenario of deploying a model to analyze customer reviews for quality-related concerns and showcasing techniques for monitoring model performance over time.

• Experiment Tracking for Reproducibility: Highlighting the importance of experiment tracking for managing and comparing different training runs, facilitating model iteration, and ensuring reproducibility for quality control tasks.

• Hands-on Session 1: Utilizing a cloud platform or model serving framework (e.g., TensorFlow Serving), participants explore deploying a simple pre-trained model for a quality control task.

• Proficiency in programming with Python and familiarity with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)

• Understanding of basic machine learning concepts and algorithms

• Knowledge of version control systems (e.g., Git) and containerization technologies (e.g., Docker) is beneficial but not required