It is a program that will allow you to respond to the needs of the future, develop strategies for analyzing data; prepare data for analysis; explore and visualize data; create models with data using Python programming language, distribute them to applications and software and process them.
This course explores how to use machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Participants will learn each stage of the pipeline from the instructor and then apply this knowledge to complete a project that can solve business problems. By the end of the course, all participants will be able to successfully build a machine learning model using Amazon SageMaker to solve their chosen business problem.
After completing this course, you will learn how to apply machine learning to a real-life business problem, including how to select the appropriate machine learning approach for a specific business problem, how to use a machine learning framework to solve a specific business problem, how to orchestrate, evaluate, deploy, and tune a machine learning model in Amazon SageMaker, and some of the best practices for designing scalable, cost-optimized, and secure machine learning pipelines in AWS.
- Module 1: Introduction to Machine Learning and ML Pipeline
Overview of machine learning, including use cases, types of machine learning and key concepts
Machine learning pipeline overview
Introduction and approach to sample projects
- Module 2: Introduction to Amazon SageMaker
Introduction to Amazon SageMaker
Amazon SageMaker and Jupyter notebooks
- Module 3: Problem Formulation
Overview of problem formulation and deciding whether machine learning is the right solution
Transforming a business problem into a machine learning problem
Amazon SageMaker Ground Truth
Amazon SageMaker Ground Truth
Problem Formulation Exercise and Review
Project work for Problem Formulation
- Module 4: Preprocessing
Overview of data collection and integration and data preprocessing and visualization techniques
- Module 5: Model Training
Choosing the right algorithm
Format and split your data for education
Loss functions and gradient descent to improve your model
Create an education job in Amazon SageMaker
- Module 6: Model Training
How are classification models evaluated?
How to evaluate regression models?
Practice model training and evaluation
Model Training and Evaluation
- Module 7: Feature Engineering and Model Tuning
Feature extraction, selection, creation and transformation
SageMaker hyperparameter optimization
- Module 8: Module Deployment
Deploy, infer and monitor models in Amazon SageMaker
Deploying edge machine learning
- Solution Architects
- Data Engineers
- Anyone interested in Artificial Intelligence and Machine Learning who wants to learn the applications of Machine Learning concepts with AWS can participate.
We recommend that they have the following prerequisites deemed necessary for this training
- Basic information about Python programming language
- Basic information about AWS Cloud infrastructure
- Basics of working in a Jupyter notebook environment
Plan this training institutionally!
This training can be planned in different durations and content specific to your organization. Please contact us for detailed rich content and planning to realize your training objectives.