This lesson explores practical methods for acquiring data in AI/ML workflows. Learners will be introduced to techniques such as data collection through APIs, web scraping, open datasets, and internal enterprise systems. The lesson also highlights ethical and legal considerations when collecting data, including privacy and compliance. By understanding these methods, learners will be able to identify efficient and responsible ways to gather data for their projects.
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