Machine Learning Pipeline on AWS
Beschreibung
Learn how to use the machine learning (ML) pipeline with Amazon SageMaker with hands-on exercises and four days of instruction. You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train, evaluate, tune, and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of projects: fraud detection, recommendation engines, or flight delays.
Kursziele
Was Sie in diesem Kurs lernen:
- 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 in 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
Zielgruppe
Dieser Kurs ist konzipiert für:
- Developers
- Solutions architects
- Data engineers
- Anyone who wants to learn about the ML pipeline via Amazon SageMaker, even if you have little to no experience with machine learning
Voraussetzungen
We recommend that attendees of this course have:
- Basic knowledge of Python
- Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
- Basic understanding of working in a Jupyter notebook environment
Lehrmethode
Dieser Kurs setzt sich zusammen aus:
- Schulung mit Kursleiter
- Praktische Übungen
- Gruppenübungen
Kursdauer / Preis
- 4 Tage / Preis folgt in Kürze!
Kursinhalt
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
Day Two
Module 3: Problem Formulation (continued)
• 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 and discuss project progress
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, then present findings
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
WICHTIG: Bitte bringen Sie zu unseren Trainings Ihr Notebook (Windows, Linux oder Mac) mit. Wenn dies nicht möglich ist, nehmen Sie bitte mit uns vorher Kontakt auf.
Kursunterlagen sind in englischer Sprache, Kurssprache des Trainers ist deutsch.
Aktuelle Trainingstermine
Termine für dieses Training gerne auf Anfrage.