Introduction to Amazon Sagemaker

  • 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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