With AWS providing numerous cloud services that help us to handle the huge amount of data that we are generating these days, the AWS Sagemaker is the AWS service that enables you to quickly and easily build, train, and deploy machine learning models for any use case with its fully managed infrastructure, tools, and workflows. AWS SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models.
What is Amazon SageMaker?
In the current era, the hottest topic to study is Machine Learning and the cloud provider Amazon web service (AWS) provides tools and services like AWS Sagemaker that can be implemented to quickly and easily start working to create models with a high accuracy rate.
Amazon SageMaker is a fully managed machine learning service that AWS offers which helps to quickly and easily build, train, and directly deploy the ML models into the production-ready environment. AWS Sagemaker helps in creating efficient and more accurate rate ML models along with great integration with other AWS services like S3 bucket, and AWS Lambda for monitoring the performance, AWS Cloudwatch is also a great monitoring tool. For easily accessing and doing data exploration or analysis you can easily integrate the Jupyter authoring notebook instance without the need to bother about merging servers. Extremely huge data can also be worked around with common machine learning algorithms in a distributed environment which can be optimized to run efficiently.AWS SageMaker provides a platform for flexible distributed training options where you get the liberty to bring your own algorithms and frameworks and deploy your ML model into a secure and scalable environment with just a few clicks offered by SageMaker Studio or the SageMaker console. With its low pricing, you can train and host your ML model which is billed by minutes of usage, at no minimum fees or any hidden upfront commitments.
Various leading ML frameworks, toolkits, and programming languages are supported by AWS SageMaker as shown below:
- With AWS Sagemaker, people are empowered to innovate with ML even for no-code interfaces for business analysts and through a range of IDEs for data scientists.
- You can automatically extract, process, and analyze documents for a more accurate investigation leading to faster decision-making. You get the power to access, label and process large amounts of structured data and unstructured data as well.
- With AWS SageMaker, you can automate the detection of suspicious transactions at a fast speed and send alerts to your customers which can reduce potential financial loss.
- By implementing the AWS Sagemaker to build, train and deploy the ML models you reduce the training time from hours to minutes with its optimized infrastructure that helps to boost team productivity (up to 10 times) with the purpose-built tools.
- You can churn prediction by building an ML model by the AWS Sagemaker where you can predict how likely customer churn ate and accordingly start to improve the retention by honing on the likely abandoners and taking remedial actions such as promotional offers.
- You can start sending personalized recommendations which help to deliver customized, unique experiences to your customers which eventually shall improve customer satisfaction and help your business grow exponentially.
Machine Learning in AWS SageMaker - (should we expand more?)
Before we start learning about how we can start to create the machine learning models with AWS Sagemaker, have a look at the wide breadth and depth of features for the ML lifecycle:
With the fully-managed AWS SageMaker service that AWS offers you can easily and quickly build, train, and deploy machine learning models at a scale. With the numerous modules that can be integrated and interwind together or independently the AWS Sagemerk enable the data scientist, ML engineer, and even business analysts to build, train, and deploy their machine learning models.
As seen from the above diagram you build your machine le**arning models with three major steps:
Build your ML Model:
With AWS Sagemaker it gets easier to build the ML models and get them ready for extensive training. The AWS SageMaker includes hosted Studio Notebooks and Notebooks instances (Jupyter notebooks) which make it easier to explore and visualize the training data stored in the Amazon S3 bucket. With a few clicks, you can directly connect to S3 for your data, or even make use of AWS Glue for moving the data from Amazon RDS, Amazon DynamoDB, and Amazon Redshift into the AWS S3 bucket for analysis in the notebook.
AWS SageMaker includes the 10 most common machine learning algorithms which are pre-installed and optimized which provides you the flexibility to select the appropriate algorithm and helps to deliver (up to 10 times) high performance for the algorithms running anywhere else. It comes pre-configured with the two most popular open-source frameworks to run TensorFlow and Apache MXNet whereas it is flexible enough to help you optimize it to work with your framework as well.
Train your ML Model:
Providing everything that you would need to quickly connect to the training data and helping you select and optimize the best algorithm and framework for your application is what makes AWS Sagemker easier to train the ML model. with a single click you can start to train your ML model. AWS SageMaker can easily scale to train models at a petabyte scale and quickly manages all of the underlying infrastructures as well for you. You can automatically tune your ML model to make the training process even faster and easier. This training of your ML model at a faster scale also helps you achieve the highest possible accuracy.
Deploy your ML Model:
Finally, the quickest and easiest way to deploy your ML model is with AWS Sagemaker with a few clicks. Once you are done training and tuning your ML model, AWS SageMaker helps you easily deploy the model in a production-ready environment so that you can start running and generating predictions on new data (a process called inference). The deployment happened on an auto-scaling cluster of Amazon EC2 instances which helps to deliver both high performance and high availability. as it spread across multiple availability zones. It also includes built-in A/B testing capabilities by which AWS SageMaker help you test and perform various experiment with your ML model for different versions to achieve the best results possible. It takes away the heavy lifting of machine learning, making it fun to work with ML models by building, training, and deploying the ML models quickly and easily.
Let us dive into a brief history of how the AWS Sagemaker became what it is today:
On 29th November 2017-11-29, AWS SageMaker was launched at the AWS re: Invent conference. After which it had the capability to Managed TensorFlow and MXNet deep neural network training which made it automatically scale model inference to multiple server instances by 27th-28th February 2018. Then, in July 2018, Amazon SageMaker started to support recurrent neural network training, word2vec training, multi-class linear learner training, and distributed deep neural network training in Chainer with Layer-wise Adaptive Rate Scaling (LARS). Also, to add to this AWS Batch Transform helped AWS SageMaker to have high-throughput non-realtime machine learning inference.
The below image depicts the journey of AWS SageMaker:
It was in November 2018 when AWS Sagemaker Reinforcement Learning helped data scientists and developers quickly and easily develop reinforcement learning models at scale. With AWS Sagemaker Neo it also elevated the SageMaker game to provide the flexibility to enable deep neural network models with edge devices like smartphones and smart cameras. Then, the AWS Marketplace for SageMaker gets launched which attracted 3rd-party developers to buy and sell machine learning models which can be trained and deployed in SageMaker. In 2019, AWS announced the AWS SageMaker Neo as open-source software.
Features of AWS SageMaker
Listed below are the features that the AWS SageMaker provides to its users and has its wide demand accordingly:
SageMaker Studio This is an integrated machine learning environment that helps you to build, train, deploy, and analyze the models all in the same application.
SageMaker Canvas This is an auto ML service that provides people with no coding background or experience, who can build models and make predictions with them.
SageMaker Inference Recommender With the SageMaker Inference Recommender you can use your ML models and workloads to get recommendations on inference instance types and configurations (e.g. instance count, container parameters, and model optimizations).
SageMaker Model Registry The versioning, artifact, lineage tracking, approval workflow, and cross-account support for deployment of the machine learning models is easily possible by SageMaker Model Registry.
SageMaker Ground Truth Plus This is the turnkey data labeling feature that helps to create high-quality training datasets without having to build labeling applications and manage the labeling workforce on your own.
SageMaker Feature Store The SageMaker Feature Store is a centralized store for features and associated metadata so features can be easily discovered and reused. There are two types of stores, an Online or Offline store where the Online Store is used for low latency, real-time inference use cases while the Offline Store is used for training and batch inference.
SageMaker Model Monitor To detect data drift and deviations in model quality, you can make use of the SageMaker Model Monitor which helps you to monitor and analyze models in production (endpoints).
SageMaker Neo With SageMaker Neo you can run your machine learning models anywhere in the cloud or at the edge after training them once.
SageMaker Studio Universal Notebook With SageMaker Studio Universal Notebook you get the flexibility to easily discover, create, terminate, connect to and manage Amazon EMR clusters in single as well as cross-account configurations directly from the SageMaker Studio.
SageMaker Studio Lab In an environment based on open-source JupyterLab, the SageMaker Studio Lab is a free service that gives customers access to AWS compute resources.
SageMaker Projects With the SageMaker Projects you can create end-to-end ML solutions with CI/CD.
SageMaker Serverless Endpoints For hosting your ML model, the SageMaker Serverless Endpoints is a serverless endpoint option. This automatically scales in capacity to serve the endpoint traffic in addition to removing the need to select instance types or manage scaling policies on an endpoint.
SageMaker Model Building Pipelines By directly integrating with SageMaker jobs, you can create and manage machine learning pipelines by the SageMaker Model Building Pipelines feature.
SageMaker Training Compiler With SageMaker Training Compiler you can train deep learning models faster on scalable GPU instances all fully managed by SageMaker.
SageMaker ML Lineage Tracking It is used to track the lineage of machine learning workflows.
SageMaker Data Wrangler With SageMaker Data Wrangler you get the ability to import, analyze, prepare, and feature data in SageMaker Studio. This also helps to simplify and streamline data pre-processing and feature engineering using little to no coding.
SageMaker JumpStart With the curated 1-click solutions, example notebooks, fine-tune the models, and pre-trained models that you can deploy, and deploy them by the SageMaker JumpStart.
SageMaker Clarify To help improve your machine learning models you can use SageMaker Clarify which helps to detect potential bias and explain the predictions as well.
SageMaker Edge Manager With SageMaker Edge Manager, you can optimize custom models for edge devices, create and manage fleets and run models with an efficient runtime.
Benefits of AWS SageMaker
Summarized below are the major benefits that the AWS Config provides to its users and has its wide demand accordingly:
As the AWS SageMaker is built on Amazon’s two decades of experience in developing real-world ML applications which include product recommendations, personalization, intelligent shopping, robotics, and voice-assisted devices.
- With AWS SageMaker, more people are enthusiastic about innovating with ML. People having less experience with coding like business analysts can now also play with AWS SageMaker and innovate ML tools through a choice of tools—IDEs for its data scientists.
- AWS Sagemaker helps to access, label, and process large amounts of structured data (tabular data) and unstructured data (photo, video, and audio) for ML.
- You can automate as well as standardize the MLOps practices across the company by building, training, deploying, and managing models at scale.
- AWS Sagemaker enhances the productivity of the machine learning project and also helps in creating and managing to compute instances with the least amount of time involved.
- By inspecting the raw data and automatically creating, deploying, and training model with complete visibility which reduces the cost of building the ML models by up to 70%.
- With AWS Sagemaker your training time is reduced (from hours to minutes) with its optimized infrastructure. This helps to boost team productivity up to 10 times with purpose-built tools.
- You can store all the ML components in one place and data labeling tasks are also done with reduced timelines which makes the model faster and more scalable.
Amazon SageMaker Pricing
As AWS SageMaker is helping developers, business analysts and data scientists to easily and quickly prepare, build, train, and deploy premium-quality machine learning (ML) models with its broad set of capabilities purpose-built for Machine Learning.
Concerning pricing concerned for AWS SageMaker, you only pay for what you use. Here you have two choices for pricing options:
- On-Demand Pricing
- SageMaker Savings Plans
With the above two options, the On-Demand Pricing options offer no minimum fees and no upfront commitments, and the SageMaker Savings Plans offer a usage-based and flexible pricing model in exchange for a commitment to a steady and consistent amount of usage. You can try AWS SageMaker for free. With the AWS Free Tier, you can quickly and easily get started with AWS SageMaker for free. When you start to create your first AWS Sagemaker resource, this free tier will start from the first month itself.
A few lists of pricing offered for Standard Instances are as below:
A few lists of pricing offered for Accelerated Computing are as below:
A few lists of pricing offered for Memory Optimised are as below:
A few lists of pricing offered for Compute Optimised are as below:
Listed below are the details for the pricing related to the free tier for AWS SageMaker for its usage per month for the first 2 months:
|Capability of AWS SageMaker
|Pricing for Free Tier (Usage per month for the first 2 months)
|50 hours of m4.xlarge or m5.xlarge instances
|125 hours of m4.xlarge or m5.xlarge instances
|Studio notebooks, and notebook instances
|250 hours of ml.t3.medium instance on Studio notebooks OR ml.t3.medium instance on notebook instances OR 250 hours of ml.t2 medium instance
|10 million write units, 10 million read units, 25 GB storage
|RStudio on SageMaker
|250 hours of ml.t3.medium instance on RSession app AND free ml.t3.medium instance for RStudioServerPro app
|150,000 seconds of inference duration
|750 hours/month for session time, and up to 10 model creation requests/month, each with up to 1 million cells/model creation requests
|25 hours of ml.m5.4xlarge instance
Use Cases of AWS SageMaker
Listed below are a few use cases of AWS SageMaker, which we can implement if the same scenario arises:
- Iterate more accurate data models.
- Accelerate and enhance data training and inferences.
- To access, and share modeling code and the code base.
- Speed up the delivery of AI modules that are production-ready.
- Modify and optimize data ingestion and its output.
- Processing huge data sets.
For Business Analysts, AWS Sagemaker use cases are summarized below diagram:
- It helps to easily prepare data, train models, and generate predictions with its point-and-click interface.
- Helps to improve collaboration with its sharing models and datasets within teams and data scientists.
- With the AutoML integrated into common BI tools such as Domo, Snowflake, and Amazon Redshift.
For Data Scientists, AWS Sagemaker use cases are summarized below diagram:
- Provides access to data from structured and unstructured data sources
- It helps to improve productivity with its purpose-built tools
- Can optimize the fully managed Jupyter Notebooks with just a few clicks
For ML Engineers, AWS Sagemaker use cases are summarized below diagram:
- To accelerate model development, with AWS Sagemkaer you can create repeatable training workflows.
- For model reproducibility and governance, ML Engineers can implement the catalog of ML artifacts centrally.
- By integrating ML workflows with CI/CD pipelines helps the ML Engineer for faster time to production.
- To maintain high quality, AWS Sagemaker can continuously monitor data and models in production.
Companies using AWS Sagemaker
Now as we learned about AWS Sagemaker and its features, benefits, and pricing options, we shall also be looking at which companies have started using AWS Sagemaker and are already using its advantage to build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
The below image refers to some of the talked about companies that have been using the AWS Sagemaker and unleashing its benefits:
Intuit (the financial and tax-preparation software company) has integrated the AWS Sagemaker to serve its customers by being on all AWS SageMaker to easily and quickly train the machine-learning models at scale, which helped them to cut the time needed to deploy the models by up to 90 percent.
Aurora is using the AWS Sagemaker to train their machine learning model and millions of cloud-based simulations per day to easily, safely, and quickly train, test, and validate self-driving vehicle technology.
Started using AWS Sagemaker to accelerate their research and development of natural language processing solutions.
It has implemented the AWS Sagemaker to reduce ML model training time for its autonomous driving models. using Amazon SageMaker
Roche has unleashed the AWS SageMaker capability to harness the Power of Health Data at Scale
Implemented the AWS SageMaker Studio to accelerate time to insights where it automates most of the machine learning development process.
Celgene used the AWS Sagemaker advantages to advance its pharma research and discovery.
Standardized its ML model development and deployment process by implementing the AWS Sagemaker which helped them reduce the turnaround cycle. This led to the creation of new ML environments from 40 days to 2 days and access and rate time to value for ML use cases from 40 to 16 weeks.
Some key takeaway points from the article are as below:
Amazon SageMaker is a fully managed machine learning service that AWS offers that helps to quickly and easily build, train, and directly deploy ML models into the production-ready environment.
AWS Sagemaker enhances the productivity of the machine learning project and also helps in creating and managing to compute instances with the least amount of time involved.
Pricing for AWS SageMaker is that you only pay for what you use. Here you have two choices for pricing options: On-Demand Pricing: Offer no minimum fees and no upfront commitments SageMaker Savings Plans: Offer a usage-based and flexible pricing model in exchange for a commitment to a steady and consistent amount of usage
With AWS SageMaker, more people are enthusiastic about innovating with ML. People having less experience with coding like business analysts can now also play with AWS SageMaker and innovate ML tools through a choice of tools—IDEs for its data scientists.