Amazon SageMaker
Tags: aws
Published: 2023-03-14 17:03:38

**What is Amazon SageMaker ?**

Amazon SageMaker is a fully-managed machine learning service provided by Amazon Web Services (AWS) thats allows developers and data scientists to build, train, and deploy machine learning models at scale. It provides an integrated development environment (IDE) for building, training, and deploying machine learning models, with features such as data labeling, feature extracting, and deployment to production.


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Through SageMaker, you can use popular machine learning frameworks such as TensorFlow, PyTorch, and Apache MXNet, as well as pre trained model machine learning datasets provided by Amazon, to build and train your models. Once your model is trained, SageMaker enables you to deploy it to production with a single click, making it easy to integrate with other AWS services such as Amazon EC2, and Amazon API Gateway.

SageMaker also added a range of tools for data management, preparation, visualization, and monitoring, which can provide you streamline your machine learning workflow and improve the accuracy of your models. Overall, Amazon SageMaker makes it effective and more efficient to build, train, and deploy machine learning models, helping you to grow your time-to-market and reduce the cost of machine learning development.


**Amazon SageMaker pricing**

Amazon SageMaker pricing is based on usage, and there are many others factors that determine the cost:

1. Storage: The cost of storing data in Amazon S3, Amazon EBS, or Amazon FSx for SageMaker is charged per GB per month.

2. Instance usage: The cost of running instances is charged hourly as well as peer second, depending on the instance type and region. The price ranges from a few cents per hour to several dollars per hour, depending on the instance type.

 3. Real-time inference: The cost of  your deployed model for real-time inference is charged per inference hour or per inference unit.

 4. Model hosting: The cost of hosting your model for real-time inference is charged hourly or per second.

5. Data processing: The cost of processing data using SageMaker's built-in algorithms or your own custom algorithms is charged per hour or per second.

6. Training: The cost of training your model using SageMaker's built-in algorithms or your own custom algorithms is charged per hour or per second.

There are also free trials available for certain SageMaker services, which can help you get started with machine learning development at no cost. Including, SageMaker offers cost optimization tools such as automatic model tuning and spot instances, that can help reduce costs even further. At last, SageMaker pricing is flexible and scalable, making it a cost-effective solution for machine learning development at any scale.

**SageMaker Studio**

Amazon SageMaker Studio is a web-based integrated development environment (IDE) where developers can  build, train, and deploy machine learning models in the cloud. It gives a single, visual interface for data scientists and developers to perform end-to-end machine learning workflows, from data preparation , model training to deployment and monitoring.

SageMaker Studio includes a range of pre-installed tools and frameworks for machine learning, such as JupyterLab, TensorFlow, PyTorch, and scikit-learn and many other useful libraries . It also supports custom images and allowing you to install your own software and libraries.

One of the main benefits of SageMaker Studio is its ability to manage the underlying infrastructure for you automatically, so you can focus on building and improving your models not at managing. It gives you managed instances that can be quickly spun up or down to match your workload, with automatic scaling and high availability built in.

SageMaker Studio also includes a range of collaboration and version control features, allowing many users to work together on the same project and track changes over time. It added with Amazon S3, allowing you to easily store and access your data, models, and other artifacts.

Overall, SageMaker Studio provides a powerful, scalable, agility and collaborative environment for machine learning development in the cloud.



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