In this expert session, learners gain insights into the importance of maintaining a consistent taxonomy in data systems. The discussion highlights how standardized naming conventions and categorization improve data usability, searchability, and model performance. Real-world examples demonstrate how inconsistent labeling can lead to confusion and errors in AI systems, making a strong case for structured data governance practices.
Class Sessions
1- Introduction to Data Management in AI/ML
2- Overview of data sources
3- Methods for Acquiring Data
4- Importance of Data Cleaning and Preprocessing
5- Hear from an Expert: The Value of Consistent Taxonomy
6- Introduction to RAG
7- Best Practices for Maintaining Efficient Data Sources for RAG
8- Hear from an Expert: Security Considerations When Working with Data
9- Summary: Data Management in AI/ML
10- Hear from an Expert: Industry Exemplar
11- Walkthrough: Setting up your environment in Microsoft Azure (Optional)
12- Selecting the right model deployment strategy in Microsoft Azure
13- Walkthrough: Justifying your choice of model selection (Optional)
14- Introduction to Machine Learning Models
15- Course syllabus: Foundations of AI and Machine Learning Infrastructure
16- The structure and role of data sources and pipelines explained
17- Supervised vs Unsupervised Learning Models
18- In-depth exploration of data sources and pipelines
19- Understanding Regression Models in Detail
20- Model development frameworks and their applications explained
21- Key considerations in selecting a model development framework
22- Understanding Classification Models in Detail
23- Clustering and Unsupervised Learning Techniques
24- Model Selection Strategies
25- Introduction to Scikit-learn
26- Introduction to TensorFlow and PyTorch
27- Model Training and Validation
28- Evaluating and Comparing Machine Learning Models
29- Introduction to Considerations when deploying platforms
30- Best Practices for Packaging and Containerizing Models
31- Tools and Frameworks for Model Deployment
32- Instructions: Preparing a Model for Deployment
33- Tools and Practices for Version Control (Git, DVC)
34- Implementing Version Control for Reproducibility
35- End-to-End Machine Learning Project Workflow
36- Case Study: Building a Recommendation System
37- Case Study: Spam Detection System
38- Real-World Challenges in Machine Learning
39- Criteria for Evaluating Deployment Platforms
40- Capstone Project: Build Your Own ML Solution
41- Real-world Case Studies of Successful AI/ML Deployments