The Machine Learning Pipeline on AWS [GK7376]
Dates et lieux de début
computer En ligne: VIRTUAL TRAINING CENTER 27 juin 2022 jusqu'au 30 juin 2022check_circle Date de début garantie |
placeMechelen (Battelsesteenweg 455-B) 29 août 2022 jusqu'au 1 sept. 2022 |
computer En ligne: VIRTUAL TRAINING CENTRE 29 août 2022 jusqu'au 1 sept. 2022 |
placeMechelen (Battelsesteenweg 455-B) 21 nov. 2022 jusqu'au 24 nov. 2022 |
computer En ligne: VIRTUAL TRAINING CENTRE 21 nov. 2022 jusqu'au 24 nov. 2022 |
Description
Vrijwel iedere training die op een onze locaties worden getoond zijn ook te volgen vanaf huis via Virtual Classroom training. Dit kunt u bij uw inschrijving erbij vermelden dat u hiervoor kiest.
OVERVIEW
By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
OBJECTIVES
In this course, you will learn to:
- Select and justify the appropriate ML appr…
Foire aux questions (FAQ)
Il n'y a pour le moment aucune question fréquente sur ce produit. Si vous avez besoin d'aide ou une question, contactez notre équipe support.
Vrijwel iedere training die op een onze locaties worden getoond zijn ook te volgen vanaf huis via Virtual Classroom training. Dit kunt u bij uw inschrijving erbij vermelden dat u hiervoor kiest.
OVERVIEW
By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
OBJECTIVES
In this course, you will learn to:
- Select and justify the appropriate ML approach for a given business problem
- Use the ML pipeline to solve a specific business problem
- Train, evaluate, deploy, and tune an ML model using Amazon SageMaker
- Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
- Apply machine learning to a real-life business problem after the course is complete
AUDIENCE
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 Amazon SageMaker
CONTENT
Day One
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
Day Two
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
Day Three
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
Day Four
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
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