AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT

Based on the outlined curriculum, you have a comprehensive plan for learning machine learning with Amazon SageMaker and other related technologies. Here's a breakdown of how you can approach this curriculum step by step:

Section 1 (Days 1-5): Data Annotation and Labeling with Amazon SageMaker GroundTruth

1. Understanding Image and Text Classification: Start by understanding the basics of image and text classification, including the types of tasks (image classification, object detection, pixel-level semantic segmentation) and data labeling requirements.

2. Amazon SageMaker GroundTruth: Learn how to use Amazon SageMaker GroundTruth for creating high-quality training datasets with minimum effort. Explore the different workforces available for data labeling, such as public Mechanical Turks, private labelers, and AWS curated third-party vendors.

3. Success Stories: Study cases where companies have successfully leveraged data to improve their business outcomes, including revenue maximization, cost reduction, and process optimization.

4. Data Sources and Types: Learn about different data sources and types, and understand the importance of good versus bad data for training machine learning models.

5. Json Lines (JSONL) and Manifest Files: Get familiar with JSON Lines format for label files and manifest files used in SageMaker to organize and track your data.

6. Define an Image Classification Labeling Job: Follow a detailed tutorial to set up an image classification labeling job in SageMaker, including defining the task, uploading images, and configuring the workforce for labeling.

7. Auto-Labeling Workflow: Understand how auto-labeling works in SageMaker GroundTruth and learn about the difference between GroundTruth and GroundTruth Plus.

8. Object Detection and Semantic Segmentation: Learn how to define a labeling job that requires bounding boxes for object detection and pixel-level annotation for semantic segmentation.

9. Label Text Data: Finally, understand how to use SageMaker GroundTruth to label text data, which is essential for natural language processing (NLP) tasks.

Section 2 (Days 6-10): Python and Data Science Essentials with Boto3

1. Python for Machine Learning: Ensure you have a solid understanding of Python as it's the primary language used in machine learning and data science on AWS.

2. Boto3 SDK: Learn how to use Boto3, the AWS-specific toolkit for Python, to interact with AWS services like SageMaker and Lambda.

Section 3 (Days 11-15): Data Exploration and Preparation with Amazon SageMaker

1. Data Exploration: Dive into data exploration techniques and tools available in the Python ecosystem, such as pandas and scikit-learn.

2. Data Preparation: Learn how to prepare your data for machine learning by cleaning, transforming, and feature engineering using SageMaker.

Section 4 (Days 16-20): Building Machine Learning Models with Amazon SageMaker

1. Scikit-Learn and XGBoost: Start building models using scikit-learn and XGBoost, two powerful libraries for machine learning in Python.

2. SageMaker SDK: Learn how to use the SageMaker SDK to create a complete machine learning workflow, from data preprocessing to model training and deployment.

Section 5 (Days 21-24): Model Training and Evaluation

1. Model Training: Train various machine learning models using SageMaker, focusing on both regression and classification tasks.

2. Model Evaluation: Learn how to evaluate your models using appropriate metrics for each task, such as accuracy, precision, recall, F1-score, ROC AUC, etc.

Section 6 (Days 25-28): Model Optimization and Automation with AutoGluon, SageMaker Autopilot, and SageMaker Canvas

1. AutoGluon: Use the AutoGluon library to automate the prototyping of machine learning models, allowing you to try multiple algorithms and find the best one quickly.

2. SageMaker Autopilot: Explore SageMaker Autopilot for its automated machine learning capabilities, which help in model development from code to trained model with minimal effort.

3. SageMaker Canvas: Use SageMaker Canvas to visualize and improve your ML workflows, making them more robust and efficient.

Section 7 (Days 29-30): AWS Lambda for Machine Learning Workflow Automation

1. Lambda Basics: Understand the basics of AWS Lambda, including its anatomy and how it can be used to run code without provisioning or managing servers.

2. Machine Learning with Lambda: Learn how to invoke machine learning models using AWS Lambda by setting up a Lambda function that processes events or API calls and returns model inferences.

3. Lambda Workflow Automation: Explore how to automate your machine learning workflows using AWS Step Functions and SageMaker Pipelines for end-to-end automation of the data science process.

This curriculum provides a comprehensive roadmap for leveraging Amazon SageMaker and associated AWS services to build, train, and deploy machine learning models. It also touches upon the integration of AI with code through generative AI models like ChatGPT, showcasing how AI can assist in programming tasks.