Introduction to AWS SageMaker

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Amazon SageMaker

Amazon SageMaker is a cloud machine-learning platform that helps users build, train, and deploy machine-learning models in a production-ready hosted environment. Amazon SageMaker helps data scientists and developers to prepare data and build, train and deploy machine learning models quickly by bringing together purpose-built capabilities. These capabilities allow users to build highly accurate models that improve over time without all the undifferentiated heavy lifting of managing ML environments and infrastructure.

What does AWS SageMaker Do?

  • Select and Prepare Training Data
  • Choose and Optimize ML Model
  • Setup and Manage Environment for Training
  • Train and Tune Model
  • Deploy ML Model to Production
  • Scale and Manage Production Environment

Benefits of Using AWS SageMaker

  • Reduces machine learning data costs
  • All ML components can store in one place
  • Highly scalable
  • Trains model faster
  • Maintains uptime
  • High data security
  • Simple Data Transfer

Machine Learning with AWS SageMaker

Traditional machine learning development is a complex iterative process amazon SageMaker studio solves this challenge by providing all the tools needed to build, train and deploy models. We need to create a machine learning model to predict the cost of cars, data containing a number of models, details to predict sale prices; all this data can be put into a CSV file and then drop into an Amazon S3 bucket. Then the user can launch SageMaker autopilot and spin up models with different algorithms, datasets, and parameters. Iteratively trains dozens of models at once and then puts the best sets on a leaderboard accuracy.

In addition, the user can dive into any of these individual models and inspect their features and then deploy the best one according to their use case with a single click after deployment. The user can oversee model quality using the Amazon SageMaker model monitor at any point.  If problems are detected, the user will receive an alert to retrain the model as needed with Amazon SageMaker studio.

It also allows for pulling the tool used in traditional software development such as debuggers and profilers into a single pane of glass to build, train and deploy machine learning models at scale.


  • It provides more than 15 widely used ML algorithms for training purposes.
  • Now to build a model, collect and prepare training data or choose an iron Amazon S3 bucket.
  • Choose and optimize the required algorithm, such as:
    •  K-Means
    • Linear Regression
    • Logistic Regression
  • SageMaker helps developers to customize ML instances with the Jupyter notebook interface.

Test and Tune

  •  Set up and manage the environment for training.
  •  Train and tune a model with Amazon SageMaker.
  • SageMaker implements hyper-parameter tuning by adding a suitable combination of algorithm parameters.
  • It divides the training data and stores it in Amazon S3, whereas the training algorithm code is stored in ECR.
  • Later, SageMaker sets up a cluster for the input data, trains it, and stores it in Amazon S3.
  • Once tuning is done, models can be deployed to SageMaker endpoints.
  • In the endpoints, real-time prediction is performed.
  • Now, evaluate the data model and determine whether the user has achieved their business goal.

How to Validate a Model?

The user can evaluate their model using offline or historical data.

Offline Testing: Use historical data to send requests to the model through Jupyter notebook in Amazon SageMaker for evaluation.

Online Testing with Live Data: It deploys multiple models into the endpoint of Amazon SageMaker and directs live traffic to the model for validation.

Validating using a “holdout set”: Here, a part of the data is set aside, which is called “holdout set,” setup data is not used for model training. Later, the model is trained with the remaining input data and generalizes the data based on what it learned initially.

k-fold validation: Here, the input data is split into two parts. One part is called k-fold, which is the validation data for testing the model, and the other part, K-1, is used as training data. Now, based on the input data, the machine learning models evaluate the final output.

Companies Using AWS SageMaker

  • ADP Zalando
  • ProQuest
  • Intuit

Built-In Algorithms

  • XGBoost, FM, Linear, k-NN, and Forecasting for supervised learning.
  • k-Means, PCA, and Random Cut Forest for unsupervised learning.
  • It provides image classification and object detection for computer vision.
  • LA, Neural Topic Model, Seq2seq, and word2Ves for text and NLP.


Author: SVCIT Editorial

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