Overview of AWS Sagemaker Studio

Machine learning (ML) is a complex, iterative, often time-consuming process. One difficult aspect is the lack of integration between the workflow steps and the tools to accomplish them.

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning that lets you build, train, debug, deploy, and monitor your machine learning models. Studio provides all the tools you need to take your models from experimentation to production while boosting your productivity.

Following are key features of Sagemaker Studio

1. Sagemaker studio notebooks are one click Jupyter notebooks that can start quickly, fully elastic and can be shareable.
2. Sagemaker studio notebooks can be utilized to connect to EMR Apache Spark environment to interactively query, explore and visualize data, and run Spark jobs using SQL, Python and Scala.
3. Sagemaker studio support and provides a set of built-in images for popular frameworks such as TensorFlow, MXNet, PyTorch. You can also register custom images , Kernels and share to all users of Sagemaker Studio Domain.

You can use many services from SageMaker Studio, AWS SDK for Python (Boto3), or AWS CLI including:

• SageMaker Pipelines to automate and manage ML workflows
• SageMaker Autopilot to automatically create ML models with full visibility
• SageMaker Experiments to organize and track your training jobs and versions
• SageMaker Debugger to debug anomalies during training
• SageMaker Model Monitor to maintain high quality models
• SageMaker Clarify to better explain your ML models and detect bias
• SageMaker JumpStart to easily deploy ML solutions for many use cases. You may incur charges from AWS Service Catalog for the underlying API calls made by Amazon SageMaker to AWS Service Catalog on your behalf
• SageMaker Inference Recommender to get recommendations for the right endpoint configuration

Amazon SageMaker Studio Lab

Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security—all at no cost—for anyone to learn and experiment with ML.

Key Features – No AWS Account needed, ability to chose compute power, persistent storage and prepacked ML frameworks

Architecture of SageMaker Studio Lab

Amazon SageMaker RStudio

RStudio is an integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management. Amazon SageMaker supports RStudio as a fully-managed IDE integrated with Amazon SageMaker Domain.

More information to be found in https://docs.aws.amazon.com/sagemaker/latest/dg/rstudio.html

Sagemaker Studio Usage

Pls follow the SageMaker Workshops and Immersion day exercises listed below

Amazon Sagemaker Workshop – https://sagemaker-workshop.com/

SageMaker Immersion day – https://sagemaker-immersionday.workshop.aws/en/

Sagemaker Studio Pricing

For more information about Sagemaker Studio Pricing, check out post – http://www.cloudinfonow.com/aws-sagemaker-studio-pricing/

Additional References

Introducing Amazon SageMaker Studio – https://d1.awsstatic.com/events/reinvent/2019/NEW_LAUNCH_REPEAT_1_Introducing_Amazon_SageMaker_Studio,_the_first_full_IDE_for_ML_AIM214-R1.pdf