-
Course Contents
-
- Module 0: Introduction
- Module 1: Introduction to Machine Learning and the ML Pipeline
- Module 2: Introduction to Amazon SageMaker
- Module 3: Problem Formulation
- Module 4: Preprocessing
- Module 5: Model Training
- Module 6: Model Evaluation
- Module 7: Feature Engineering and Model Tuning
- Module 8: Deployment
You have access to the labs for another 14 days after the course. This way you can repeat exercises or deepen them individually.
-
Target Group
-
This course is intended for:
- Developers
- Solutions Architects
- Data Engineers
- Anyone with little to no experience with ML and wants to learn about the ML pipeline using
-
Knowledge Prerequisites
-
We recommend that attendees of this course have:
- Basic knowledge of Python programming language
- Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
- Basic experience working in a Jupyter notebook environment
| Module 0: Introduction |
| Pre-assessment |
| Module 1: Introduction to Machine Learning and the ML Pipeline |
| Overview of machine learning, including use cases, types of machine learning, and key |
| concepts |
| Overview of the ML pipeline |
| Introduction to course projects and approach |
| Module 2: Introduction to Amazon SageMaker |
| Introduction to Amazon SageMaker |
| Demo: Amazon SageMaker and Jupyter notebooks |
| Hands-on: Amazon SageMaker and Jupyter notebooks |
| Module 3: Problem Formulation |
| Overview of problem formulation and deciding if ML is the right solution |
| Converting a business problem into an ML problem |
| Demo: Amazon SageMaker Ground Truth |
| Hands-on: Amazon SageMaker Ground Truth |
| Practice problem formulation |
| Formulate problems for projects |
| Checkpoint 1 and Answer Review |
| Module 4: Preprocessing |
| Overview of data collection and integration, and techniques for data preprocessing and |
| visualization |
| Practice preprocessing |
| Preprocess project data |
| Class discussion about projects |
| Checkpoint 2 and Answer Review |
| Module 5: Model Training |
| Choosing the right algorithm |
| Formatting and splitting your data for training |
| Loss functions and gradient descent for improving your model |
| Demo: Create a training job in Amazon SageMaker |
| Module 6: Model Evaluation |
| How to evaluate classification models |
| How to evaluate regression models |
| Practice model training and evaluation |
| Train and evaluate project models |
| Initial project presentations |
| Checkpoint 3 and Answer Review |
| Module 7: Feature Engineering and Model Tuning |
| Feature extraction, selection, creation, and transformation |
| Hyperparameter tuning |
| Demo: SageMaker hyperparameter optimization |
| Practice feature engineering and model tuning |
| Apply feature engineering and model tuning to projects |
| Final project presentations |
| Module 8: Deployment |
| How to deploy, inference, and monitor your model on Amazon SageMaker |
| Deploying ML at the edge |
| Demo: Creating an Amazon SageMaker endpoint |
| Post-assessment |
| Course wrap-up |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.-
Course Contents
-
- Module 0: Introduction
- Module 1: Introduction to Machine Learning and the ML Pipeline
- Module 2: Introduction to Amazon SageMaker
- Module 3: Problem Formulation
- Module 4: Preprocessing
- Module 5: Model Training
- Module 6: Model Evaluation
- Module 7: Feature Engineering and Model Tuning
- Module 8: Deployment
You have access to the labs for another 14 days after the course. This way you can repeat exercises or deepen them individually.
-
Target Group
-
This course is intended for:
- Developers
- Solutions Architects
- Data Engineers
- Anyone with little to no experience with ML and wants to learn about the ML pipeline using
-
Knowledge Prerequisites
-
We recommend that attendees of this course have:
- Basic knowledge of Python programming language
- Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
- Basic experience working in a Jupyter notebook environment
| Module 0: Introduction |
| Pre-assessment |
| Module 1: Introduction to Machine Learning and the ML Pipeline |
| Overview of machine learning, including use cases, types of machine learning, and key |
| concepts |
| Overview of the ML pipeline |
| Introduction to course projects and approach |
| Module 2: Introduction to Amazon SageMaker |
| Introduction to Amazon SageMaker |
| Demo: Amazon SageMaker and Jupyter notebooks |
| Hands-on: Amazon SageMaker and Jupyter notebooks |
| Module 3: Problem Formulation |
| Overview of problem formulation and deciding if ML is the right solution |
| Converting a business problem into an ML problem |
| Demo: Amazon SageMaker Ground Truth |
| Hands-on: Amazon SageMaker Ground Truth |
| Practice problem formulation |
| Formulate problems for projects |
| Checkpoint 1 and Answer Review |
| Module 4: Preprocessing |
| Overview of data collection and integration, and techniques for data preprocessing and |
| visualization |
| Practice preprocessing |
| Preprocess project data |
| Class discussion about projects |
| Checkpoint 2 and Answer Review |
| Module 5: Model Training |
| Choosing the right algorithm |
| Formatting and splitting your data for training |
| Loss functions and gradient descent for improving your model |
| Demo: Create a training job in Amazon SageMaker |
| Module 6: Model Evaluation |
| How to evaluate classification models |
| How to evaluate regression models |
| Practice model training and evaluation |
| Train and evaluate project models |
| Initial project presentations |
| Checkpoint 3 and Answer Review |
| Module 7: Feature Engineering and Model Tuning |
| Feature extraction, selection, creation, and transformation |
| Hyperparameter tuning |
| Demo: SageMaker hyperparameter optimization |
| Practice feature engineering and model tuning |
| Apply feature engineering and model tuning to projects |
| Final project presentations |
| Module 8: Deployment |
| How to deploy, inference, and monitor your model on Amazon SageMaker |
| Deploying ML at the edge |
| Demo: Creating an Amazon SageMaker endpoint |
| Post-assessment |
| Course wrap-up |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.