Model Management & Experience Tracking in IT Management

This course equips IT professionals with the knowledge and skills to effectively manage the Machine Learning (ML) lifecycle within IT operations. You'll explore the critical stages of model training, serving, validation, and experiment management. This empowers you to build robust and reliable AI systems that deliver value for IT management tasks. 

• Explain the different stages of the Machine Learning lifecycle, including model training, serving, validation, and monitoring.

• Understand the importance of model management for ensuring the reliability and performance of AI systems used in IT.

• Explore techniques for training and deploying Machine Learning models in production environments suitable for IT management tasks.

• Implement best practices for model validation and monitoring to identify potential issues and ensure model effectiveness over time.

• Utilize experiment management tools to track, compare, and optimize Machine Learning experiments for IT applications.

• Discuss the ethical considerations surrounding bias and fairness in AI models deployed within IT operations.

• Develop a plan for integrating model management and experiment tracking practices into existing IT workflows.

1. The Machine Learning Lifecycle: From Training to Production:

• Demystifying the ML Lifecycle: Understanding the different stages of the Machine Learning lifecycle, focusing on training, serving, validation, and monitoring for robust AI systems.

• Model Training for IT Management: Exploring techniques for training Machine Learning models on IT-related data, considering factors like data preparation and algorithm selection.

• Case Study 1: Training a model to predict IT infrastructure failures based on historical sensor data, highlighting the training process and considerations for real-world deployment.

• Interactive Workshop: Hands-on experience with training a simple Machine Learning model using a cloud platform (e.g., Google Cloud AI Platform) to understand the basic training process.

• Guest Speaker Session: Inviting an ML engineer to discuss real-world IT management applications of Machine Learning models and their deployment considerations.

2. Building Reliable AI Systems: Model Serving and Validation:

• Deploying Models for Action: Understanding the process of deploying trained models into production environments within IT operations to make predictions and automate tasks.

• Model Serving Infrastructure: Exploring different deployment options for IT-related models, considering factors like scalability, security, and integration with existing IT systems.

• Hands-on Session: Simulating model deployment using a cloud platform (e.g., AWS SageMaker) to experience the process of serving predictions from a trained model.

• The Importance of Model Validation: Understanding the role of model validation in ensuring the accuracy, fairness, and generalizability of deployed models for IT applications.

• Case Study 2: Implementing validation techniques to monitor the performance of a model used for IT service desk ticket classification, identifying potential biases and areas for improvement.

3. Experimentation & Continuous Improvement: Experiment Management:

• Tracking the Journey: Exploring the importance of experiment management for tracking, comparing, and optimizing Machine Learning experiments in IT.

• Experiment Management Tools: Introducing popular tools and frameworks used for experiment management, enabling efficient tracking of model performance and hyperparameter tuning.

• Hands-on Session: Utilizing an experiment management tool (e.g., Weights & Biases) to track and compare different Machine Learning experiments relevant to an IT management task.

• Ethical Considerations in AI: Discussing the ethical implications of deploying AI models in IT, focusing on potential biases and fairness concerns, and mitigation strategies.

• Course Wrap-up & Project Presentations: Teams develop a plan for integrating model management and experiment tracking practices into an IT management task involving Machine Learning. Their plan should identify the chosen model type, deployment strategy, validation techniques, and experiment management tools they would utilize, addressing ethical considerations.

• Resource Sharing: Discussing best practices and ongoing resources for staying up-to-date with advancements in model management and experiment tracking for IT-related AI applications.

• 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