Introduction to Amazon Sagemaker

INSAID

4 min readFeb 24, 2022

by Pronay Ghosh and Hiren Rupchandani

In the previous article, we covered a high-level overview of how Amazon S3 works.
We understood how does S3 works, how to use Amazon S3, typical behaviors of Amazon S3, and S3 Functionality.
Here, in this article, we will learn about Amazon Sagemaker.
After that, we will learn how to build, train and deploy a machine learning model with the help of Amazon Sagemaker.
It is a machine learning service that is wholly managed by Amazon.
Data scientists and developers can use SageMaker to construct and train machine learning models fast and easily, then deploy them directly into a production-ready hosted environment.
You don’t have to manage servers because it has an integrated Jupyter writing notebook instance for easy access to your data sources for exploration and analysis.
It also includes common machine learning methods that have been improved for use in a distributed setting with exceptionally huge data sets.
SageMaker provides a variety of distributed training alternatives that may be tailored to fit your specific workflows.

One can launch a model from SageMaker Studio or the SageMaker console in a safe and scalable environment with just a few clicks.
Training and hosting are invoiced by the minute, with no minimum payments or commitments up ahead.

Top 3 tools of Amazon SageMaker

The top 3 Amazon SageMaker features include:

1. Amazon SageMaker Studio:

Amazon SageMaker Studio is a web-based machine learning integrated development environment (IDE).
This allows one to create, train, debug, deploy, and monitor machine learning models.
SageMaker Studio gives you everything you need to take your models from prototype to production while increasing your productivity.
To learn more about Amazon Sagemaker Studio one can visit here.

2. Amazon SageMaker Studio Lab:

Amazon SageMaker Studio Lab is a free offering that provides clients with AWS compute resources in a JupyterLab-based environment.
It has the same architecture and user experience as Amazon SageMaker Studio, but only supports a fraction of Studio’s features.
You can use Studio Lab to build and execute Jupyter notebooks on AWS compute resources without having to sign up for an AWS account.
You can use open-source Jupyter extensions to execute your Jupyter notebooks because Studio Lab is built on open-source JupyterLab.
To learn more about Amazon Sagemaker Studio Lab one can visit here.

3. Amazon SageMaker Studio Universal Notebook:

The studio provides data scientists, ML engineers, and general practitioners with the tools they need to execute large-scale data analytics and data preparation.
One may visually browse, discover, and connect to Amazon EMR from within a Studio notebook.
After you’ve connected, you may use Apache Spark, Hive, and Presto to interactively explore, visualize, and prepare data for machine learning.

Bird-eye view of Build-Train and Deploy using Amazon Sagemaker

1. Build:

Amazon SageMaker makes it simple to develop ML models.
It gets the model ready for training by offering everything one needs to quickly connect to training data.
It also comes with hosted Jupyter notebooks, which make it simple to analyze and visualize your Amazon S3 training data.
One can connect directly to data stored in S3 or use AWS Glue to bring data into S3 for analysis in your notebook from other Amazon platforms.
These platforms may include Amazon RDS, Amazon DynamoDB, and Amazon Redshift.

2. Train:

In the Amazon SageMaker dashboard, you may start training your model with a single click.
Amazon SageMaker takes care of all of the underlying infrastructures, and it can readily scale to train models on a petabyte scale.
It can automatically modify your model to obtain the maximum possible accuracy.
This makes the training process even faster and easier.

3. Deploy:

Once your model has been trained and fine-tuned, SageMaker makes it simple to put it into production.
After that, it starts making predictions on new data (a process called inference).
To achieve both high performance and high availability, Amazon SageMaker installs your model on an auto-scaling cluster of Amazon EC2 instances spread across various availability zones.
A/B testing tools are built-in to Amazon SageMaker to enable you to test your model and experiment with different versions to get the best results.
Amazon SageMaker takes care of the heavy lifting in machine learning.
This allows one to quickly create, train, and deploy machine learning models.

Conclusion:

So far in this article, we covered a high-level overview of Amazon Sagemaker.
In the next article, we will learn about how to build, train and deploy an ML model using Amazon Sagemaker.

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