Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By : Somanath Nanda, Weslley Moura
Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By: Somanath Nanda, Weslley Moura

Overview of this book

The AWS Certified Machine Learning Specialty exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus using practical examples to help you with your real-world machine learning projects on AWS. Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover a wide variety of techniques for data manipulation and transformation for different types of variables. The book also shows you how to handle missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with the specific ML algorithms you need to know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them. By the end of this book, you'll have gained knowledge of the key challenges in machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS ML.
Table of Contents (14 chapters)
Section 1: Introduction to Machine Learning
Section 2: Data Engineering and Exploratory Data Analysis
Section 3: Data Modeling

Creating notebooks in Amazon SageMaker

If you're working with machine learning, then you need to perform actions such as storing data, processing data, preparing data for model training, model training, and deploying the model for inference. They are not easy, and each of these stages requires a machine to perform the task. With Amazon SageMaker, life becomes much easier when carrying out these steps.

What is Amazon SageMaker?

SageMaker provides training instances to train a model using the data and provides endpoint instances to infer by using the model. It also provides notebook instances, running Jupyter Notebooks, to clean and understand the data. If you're happy with your cleaning process, then you should store them in S3 as part of the staging for training. You can launch training instances to consume this training data and produce a machine learning model. The machine learning model can be stored in S3, and endpoint instances can consume the model to produce...