Amazon SageMaker is a service that helps the user in developing machine learning models.
Amazon SageMaker is an entirely-managed service in the AWS public cloud. This service aids in the development of more efficient and accurate Machine Learning models. SageMaker has a number of modules that can be used together or separately, depending on the needs of the user. This is how machine learning models are built, trained, and deployed.
Another advantage of this service is that it allows you to incorporate other AWS services into your models, such as S3 buckets, Amazon Lambda, and AWS Cloudwatch. Even experienced application developers find it difficult to deploy machine learning models.
Amazon SageMaker intends to make the process easier. It accelerates the machine learning process by utilizing widely accepted algorithms as well as other tools.
In this blog, we will discuss in brief Amazon SageMaker. Initially, let’s look at the topics that we will be covering in this blog:
- How does Amazon SageMaker work?
- Amazon SageMaker automation tools
- What are its use cases?
- Amazon SageMaker pricing
How does Amazon SageMaker work?
AWS SageMaker divides machine learning modeling into three steps:
A developer begins by logging into the SageMaker command line and launching a notebook instance. SageMaker comes with a number of built-in training algorithms. It also includes linear regression and image classification. The programmer can also import personalized algorithms.
Before beginning the training procedure, model training developers specify the location of the data in an Amazon S3 bucket as well as the preferred instance type. SageMaker Model Monitor allows continuous automatic model tuning.
This tuning determines the best set of parameters, or hyperparameters, to optimize the algorithm.
When the model is ready for deployment, the service operates and scales the cloud infrastructure automatically. It makes use of a set of SageMaker instance types that includes multiple graphics processing unit accelerators optimized for machine learning workloads.
SageMaker also performs the following tasks in this step:
- Applies security patches
- Deploys multiple availability zones
- Performs health checks
- Sets up AWS Auto Scaling
SageMaker automation tools
Since SageMaker’s initial release in 2017, Amazon has added new features. AWS SageMaker Studio’s automation tools enable users to automatically debug, handle, and monitor ML models. Among the SageMaker tools are the following:
- The Autopilot tool allows AI models to train for a specific data set and ranks each algorithm in terms of accuracy.
- The Data Wrangler tool provides a complete solution for importing, preparing, transforming, and analyzing data.
- The Debugger tool automatically monitors the use of system resources such as GPUs and CPUs. It also collects detailed metrics for the ML framework.
- The Notebook tool creates Jupyter notebooks with a single click and transfers notebook content for collaborative use.
- The Pipelines tool automates the machine learning workflow. This is done by allowing data to be transformed and correlated into a model. Later, the model is analyzed to produce results.
- The Ground Truth tool helps to lower labeling costs when processing large AI training samples.
- The Edge Manager tool extends machine learning monitoring and management to edge devices.
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What are its use cases?
There are various use cases of SageMaker. Some of them are:
- Music streaming service
- Sharing modeling code
- Processing large data sets
- Fleet predictive maintenance
- E-Commerce personalization
- Enhancing data training and inferences
- Accessing and sharing code
- Computer vision for medical imaging
Let’s try to understand the use of SageMaker with the help of an example:
Suppose we want to build an AI program. That AI program should be capable of opening the gate of a house or any building automatically for the owner’s car.
We can use SageMaker to recognize cars by reading license plates and then opening a gate automatically to let them into the parking area.
Amazon SageMaker pricing
There are various pricing plans available for SageMaker. Some of those plans are:
In this pricing plan, the pricing is billed by the second and does not require an upfront commitment or a minimum fee.
In this pricing plan, the costs are reduced by 64%. It is a flexible pricing plan in which a commitment is made to regularly use the SameMager for a one or three-year term.
This pricing plan is a part of the AWS free tier plan and hence SageMaker is free to use. But, in this free tier, only limited services are provided such as 25 hours of ml.m5.4xlarge instance or 150,000 seconds of inference duration.
In today’s article, we went over the SageMaker provided by Amazon in great detail. I hope that by writing this article, I was able to dispel some of your concerns.
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