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Home » Technology » Cloud » Machine Learning on AWS

Course Skill Level:

Intermediate to Advanced

Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Cloud

  • Course Code:

    MLOAWSL21E09

Who should attend & recommended skills:

Those with intermediate IT and basic or better machine learning, Linux, & Microsoft Azure skills

Who should attend & recommended skills

  • This course is geared for those who want to gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow.
  • Skill-level: Foundation-level Machine Learning on AWS skills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Microsoft Azure: Basic to Intermediate (1-5 years’ experience)
  • Machine Learning: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This course is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the lessons, you’ll gain insights into how these algorithms can be trained, tuned, and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the course will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few lessons, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics and predictive modeling through to sentiment analysis. By the end of this course, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning on AWS instructor, students will learn about and explore:
  • Building machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark, and TensorFlow
  • Model optimization and understand how to scale your models using simple and secure APIs
  • Developing, training, tuning, and deploying neural network models to accelerate model performance in the cloud
  • Managing AI workflows by using AWS cloud to deploy services that feed smart data products
  • Using SageMaker services to create recommendation models
  • Scaling model training and deployment using Apache Spark on EMR

Course breakdown / modules

  • How AWS empowers data scientists
  • Identifying candidate problems that can be solved using machine learning
  • Machine learning project life cycle
  • Deploying models

  • Classification algorithms
  • Naive Bayes classifier
  • Classifying text with language models
  • Naive Bayes – pros and cons

  • Predicting the price of houses
  • Understanding linear regression
  • Evaluating regression models
  • Implementing linear regression through scikit-learn
  • Implementing linear regression through Apache Spark
  • Implementing linear regression through SageMaker’s linear Learner
  • Understanding logistic regression
  • Pros and cons of linear models

  • Understanding decision trees
  • Understanding random forest algorithms
  • Understanding gradient boosting algorithms
  • Predicting clicks on log streams

  • Understanding How Clustering Algorithms Work
  • Clustering with Apache Spark on EMR

  • Making theme park attraction recommendations through Flickr data
  • Collaborative filtering
  • Finding recommendations through Apache Spark’s ALS
  • Recommending attractions through SageMaker Factorization Machines

  • Understanding deep learning
  • Applications of deep learning
  • Understanding deep learning algorithms
  • Understanding convolutional neural networks

  • About TensorFlow
  • TensorFlow as a general machine learning library
  • Training and serving the TensorFlow model through SageMaker
  • Creating a custom neural net with TensorFlow

  • Introducing Amazon SageMaker for image classification
  • Training a deep learning model using Amazon SageMaker
  • Classifying images using Amazon SageMaker

  • Introducing Amazon Comprehend
  • Accessing AmazonComprehend
  • Named-entity recognition using Comprehend
  • Sentiment analysis using Comprehend
  • Text classification using Comprehend

  • Introducing Amazon Rekognition
  • Implementing object and scene detection
  • Implementing facial analysis

  • Introducing Amazon Lex
  • Building a custom chatbot using Amazon Lex

  • Choosing your instance types
  • Distributed deep learning

  • The importance of model optimization
  • Automatic hyperparameter tuning
  • Hyperparameter tuning in Apache Spark
  • Hyperparameter tuning in SageMaker

  • Introduction to the EMR architecture
  • Tuning EMR for different applications
  • Managing data pipelines with Glue

  • SageMaker model deployment
  • Apache Spark model deployment

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