Machine Learning Pipelines on AWS Eğitimi

  • Eğitim Tipi: Classroom / Virtual Classroom / Online
  • Süre: 3 Gün
  • PDF indir
  • Bu eğitimi kendi kurumunuzda planlayabilirsiniz. Bize Ulaşın!
En Yakın Tarih

16 Mart 2021

3 Gün

This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. 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.

Intended 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

Delivery Method

This course is delivered through a mix of:

  • Instructor-led training
  • Hands-on labs
  • Demonstrations
  • Group exercises

We recommend that attendees of this course have the following prerequisites:

  • Basic knowledge of Python programming language
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
  • Basic understanding of working in a Jupyter notebook environm

In this course, you will learn how 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 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

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
  • Lab 1: Introduction to Amazon SageMaker

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
  • Problem Formulation Exercise and Review
  • Project work for Problem Formulation

Day Two

Recap and Checkpoint #1

Module 4: Preprocessing

  • Overview of data collection and integration, and techniques for data preprocessing and visualization
  • Lab 2: Data Preprocessing (including project work)

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

Day Three

Recap and Checkpoint #2

Module 6: Model Training

  • How to evaluate classification models
  • How to evaluate regression models
  • Practice model training and evaluation
  • Train and evaluate project models
  • Lab 3: Model Training and Evaluation (including project work)
  • Project Share-Out 1

Module 7: Feature Engineering and Model Tuning

  • Feature extraction, selection, creation, and transformation
  • Hyperparameter tuning
  • Demo: SageMaker hyperparameter optimization

Day Four

Lab 4: Feature Engineering (including project work)

Recap and Checkpoint #3

Module 8: Module Deployment

  • How to deploy, inference, and monitor your model on Amazon SageMaker
  • Deploying ML at the edge

Module 9: Course Wrap-Up

  • Project Share-Out 2
  • Post-Assessment
  • Wrap-up


Eğitimlerle ilgili bilgi almak ve diğer tüm sorularınız için bize ulaşın!

Machine Learning Pipelines on AWS Eğitimi hakkında ilginizi çekebilecek yazılar

DEEP LEARNING & MACHINE LEARNING

Deep Learning & Machine Learning: Aralarındaki Fark Ne? Deep learning ve machine learning - yani derin öğrenme ve makine öğrenmesi - arasındaki fark ne? Birbirlerinden ne kadar farklılar veya ne kadar benziyorlar? Machine learning ve deep learni...

YAPAY ZEKÂ VE TÜRLERİ

YAPAY ZEK VE TÜRLERİ Yapay zekânın - AI temel kategorilerini, nasıl çalıştıklarını ve neye katkı sağladıklarını biliyor musunuz? Yapay zekâyı; Machine Learning, Deep Learning, Natural Language Processing, Computer Vision ve Explainable AI olarak be...

Yakın tarihte açılacak eğitimler

Sınıf eğitimlerimizi İstanbul, Ankara ve Londra ofislerimizde düzenlemekteyiz. Kurumunuza özel eğitimleri ise, dilediğiniz tarih ve lokasyonda organize edebiliriz.

16 Mart 2021

3 Gün
Classroom / Virtual Classroom

İstanbul, Ankara, Londra
İstanbul, Ankara, Londra

17 Haziran 2021

3 Gün
Classroom / Virtual Classroom

İstanbul, Ankara, Londra
İstanbul, Ankara, Londra

06 Eylül 2021

3 Gün
Classroom / Virtual Classroom

İstanbul, Ankara, Londra
İstanbul, Ankara, Londra