Machine Learning Engineering on AWS

هندس حلول تعلّم الآلة من البداية إلى النهاية على AWS. تصوغ مشكلات تعلّم الآلة

3 أياممتوسط13 وحدة

نظرة عامة

هندس حلول تعلّم الآلة من البداية إلى النهاية على AWS. تصوغ مشكلات تعلّم الآلة، وتعالج البيانات وتهندس خصائصها، وتختار أساليب النمذجة، وتدرّب النماذج وتقيّمها وتضبطها، وتبني خطوط معالجة آلية قابلة للتوسّع باستخدام SageMaker.

ما ستتعلّمه

المحتوى التقني أدناه معروضٌ باللغة الإنجليزية كما ورد في منهاج الدورة، حفاظاً على دقّة المصطلحات.

Explain ML fundamentals and its applications in the AWS Cloud.
Process, transform, and engineer data for ML tasks by using AWS services.
Select appropriate ML algorithms and modeling approaches based on problem requirements and model interpretability.
Design and implement scalable ML pipelines by using AWS services for model training, deployment, and orchestration.
Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows.
Discuss appropriate security measures for ML resources on AWS.
Implement monitoring strategies for deployed ML models, including techniques for detecting data drift.

المنهاج

13 وحدة

1Course Introduction

Topic A: Introduction to ML
Topic B: Amazon SageMaker AI
Topic C: Responsible ML

Topic A: Evaluating ML business challenges
Topic B: ML training approaches
Topic C: ML training algorithms

Topic A: Data preparation and types
Topic B: Exploratory data analysis
Topic C: AWS storage options and choosing storage

Topic A: Handling incorrect, duplicated, and missing data
Topic B: Feature engineering concepts
Topic C: Feature selection techniques
Topic D: AWS data transformation services
Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR
Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK

Topic A: Amazon SageMaker AI built-in algorithms
Topic B: Selecting built-in training algorithms
Topic C: Amazon SageMaker Autopilot
Topic D: Model selection considerations
Topic E: ML cost considerations

Topic A: Model training concepts
Topic B: Training models in Amazon SageMaker AI
Lab 3: Training a model with Amazon SageMaker AI

Topic A: Evaluating model performance
Topic B: Techniques to reduce training time
Topic C: Hyperparameter tuning techniques
Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI

Topic A: Deployment considerations and target options
Topic B: Deployment strategies
Topic C: Choosing a model inference strategy
Topic D: Container and instance types for inference
Lab 5: Shifting Traffic A/B

Topic A: Access control
Topic B: Network access controls for ML resources
Topic C: Security considerations for CI/CD pipelines

Topic A: Introduction to MLOps
Topic B: Automating testing in CI/CD pipelines
Topic C: Continuous delivery services
Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio

Topic A: Detecting drift in ML models
Topic B: SageMaker Model Monitor
Topic C: Monitoring for data quality and model quality
Topic D: Automated remediation and troubleshooting
Lab 7: Monitoring a Model for Data Drift

13Course Wrap-up

لمن هذه الدورة

مهندسو تعلّم الآلة وعلماء البيانات والمطوّرون الذين ينشرون النماذج على AWS.

قبل أن تبدأ

Familiarity with basic machine learning concepts
Working knowledge of Python programming language and common data science libraries such as NumPy, Pandas, and Scikit-learn
Basic understanding of cloud computing concepts and familiarity with AWS
Experience with version control systems such as Git (beneficial but not required)

3 أيام

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