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
- 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.
- 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.
- 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.
- 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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