Are you eager to learn what AWS SageMaker is? No worries! You have landed at the right blog. AWS SageMaker is an AWS service that creates, trains, and deploys Machine Learning models into production. It is a fully managed as well as cloud-based platform that simplifies building ML models. Well! We will jump into the blog without much ado. This blog covers what is AWS SageMaker, its various features, use cases, pricing, and many more in greater detail.
It’s worth noting that deploying Machine Learning (ML) models in a production environment is really challenging. AWS SageMaker overcomes this setback by building high-quality ML models. Also, it simplifies the deployment of the models.
Now, the question is, what is AWS SageMaker exactly? AWS SageMaker is nothing but a cloud-based ML-hosted environment. With AWS SageMaker, You can quickly build and train ML models. Then, you can deploy the models in the production-ready environment effortlessly.
The main thing about AWS SageMaker is that you don’t need to manage servers since it is a fully managed platform. Curious to learn its features, use cases, and others?
Well! In this blog post, you can learn what is AWS SageMaker, how it works, its vital features, benefits, and a lot more things that await you.
|Table of Content - What is AWS SageMaker?|
AWS SageMaker is a cloud-based ML platform with which you can quickly create, test, train, and deploy advanced ML models. This AWS service uses powerful ML algorithms to develop and train the models. After that, it deploys them in the production environment smoothly.
Generally, if you want to build and train efficient ML models, you must manage a huge volume of data. And you need to manage powerful computing resources as well. AWS SageMaker simplifies these requirements in collaboration with other AWS services.
Know that Sagemaker supports all the popular ML frameworks and programming languages.
We Hope you are clear about what is AWS SageMaker. Next, we will look at how it works and all other vital aspects of SageMaker in the coming topics.
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As you know, AWS SageMaker is the cloud platform that you can use to create, train, and deploy ML models into applications in a production-ready environment. In other words, AWS SageMaker functions in three phases: building, training, and deploying ML models.
Let’s discover more about the three phases in detail in the following.
Before building models, the essential thing that we need to do is prepare the example data. This is the data required to train ML models. We need to store this data in a single repository. Also, we need to clean the data to bring consistency to the data. Because of this, we can quickly transform the data and then allow ML models to learn the data to derive the best results.
We use Jupyter Notebooks on the AWS SageMaker to process the example data. With notebooks, you can quickly retrieve, clean, and transform the data. When it comes to fetching data, you can bring any amount of data from the Amazon S3 bucket. Also, AWS SageMaker creates ML instances in AWS EC2. The EC2 instances support running Jupyter notebooks.
Know that we use algorithms to train ML models. AWS SageMaker offers many powerful algorithms. For example, image classification and linear regression are a few AWS SageMaker algorithms developers or data scientists widely use to build ML models. Not only that, SageMaker allows using custom-built ML algorithms as well as Docker container images to build ML models.
However, we need to choose the algorithms based on the application requirements. Also, we need to select the resources based on the requirements. For instance, we can use only one general-purpose instance or a cluster of instances. Besides, you need to specify the instance types and location of data while training ML models.
Once you complete training an ML model, you need to evaluate the ML models to check whether they derive accurate inferences. In this regard, Sagemaker allows using Jupyter notebooks to assess the ML models. Also, it allows using a high-level Python library for evaluating the models.
You can enhance the quality of ML models before deploying them in production. And you need to perform the necessary health checks in the ML models before the deployment. Applying security is also essential before the deployment. You need to set auto-scaling to allocate resources. Besides, you need to create HTTPS endpoints to improve security.
AWS SageMaker offers many advantages to its users. We will list them as follows:
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Essentially, we build and train ML models to make predictions or inferences. We use ML algorithms to develop and train the models. Then, we deploy the models into the applications in a production environment. Finally, the models generate inferences based on the real-time input data.
The significant thing about ML models is that they can process high-volume data in no time. In other words, it can process millions of data and generate results in milliseconds.
Let’s find out the role of ML in AWS SageMaker now.
SageMaker uses effective tools to automate mundane or repetitive processes. The SageMaker tool set includes all the crucial ML modeling components. And the SageMaker templates consist of all the required software capabilities. Using these features, SageMaker performs creating, training, and deploying ML models at scale. Besides, you reduce human errors and modeling costs remarkably using the automation capabilities of SageMaker.
First, we need to create a training job. Creating the training job is essential to train ML models. Every training job will include the following:
Let’s now look at how the training of ML models is performed.
When training ML models, you can fetch input data from the Amazon S3 bucket. The AWS SageMaker launches the ML compute instances once you have built the training job. Then, you can train the ML model using the input data. Lastly, you store the output data in the AWS S3 bucket.
It is essential to note that SageMaker automatically scales its resources based on the requirements. Also, SageMaker uses distributed training libraries to train high-level ML models faster. Also, SageMaker uses third-party libraries such as Megatron, DeepSpeed, and others to train the models.
The above image shows the automated workflows in training ML models step by step. You can use high-performance GPUs as well as CPU instances to train ML models. However, Sagemaker generated billing only based on the resources used for the training. Developers and data scientists can use SageMaker or third-party libraries to increase training performance. Also, Sagemaker uses the training compiler to boost performance.
Moreover, Tuning is one of the crucial steps performed while training models. If training parameters don’t fall within limits, they are tuned to fit within them. It is essential to note that Sagemaker performs tuning ML models automatically.
In the next step, debugging is performed to detect errors in training data and modeling codes. Subsequently, profiling and experimentation are completed. You can store the model training results in an Amazon S3 bucket. Finally, you can deploy the model in the production environment and record the inferences.
Once you complete training ML models, you must evaluate the models' effectiveness. To assess the models, you must validate them by applying different methods. It will help you to choose the best-fit model.
We can evaluate ML models using the following four methods. It can be done with either live data or else historical data. Note that historical data is offline data.
Let’s go to the methods right now.
In this testing, we use historical data to validate ML models. So we can get results or inferences for the offline data. We can use Jupyter notebooks in SageMaker for making this testing. Besides, we can use a high-level Python library to perform this testing.
A holdout set is nothing but a part of training data. Usually, this data will be 20-30% of the training data. First, we need to use the holdout set to train an ML model and record inferences. Then, we need to train the model with the training data and record inferences. Lastly, we can compare both inferences and validate the model's effectiveness.
In this testing, we use live data to validate ML models. The significant thing about this testing is that we allow only 10 % of the live data for evaluating a model. If we get satisfied with the model, we can allow 100% of the live data into the model.
SageMaker allows splitting the example data set into ‘k’ parts in this testing. Here, each ‘k’ part is assumed as a holdout set. We need to train an ML model using the parts separately. Then, we need to add the models to derive the final model. Usually, the ‘k’ value will be mainly between 5 to 10.
There are many features that AWS SageMaker offers to its users.
Let’s look at the brief of a few key features in the following one by one.
The use cases of AWS SageMaker are plenty. Below is a list of a few use cases.
|Related Article: Amazon SageMaker|
No wonder AWS SageMaker provides countless benefits to its users. To list a few, AWS SageMaker:
Following are the various pricing plans offered by AWS SageMaker.
Let’s have a close look at them below:
AWS SageMaker is used for building, training, and deploying highly-sophisticated ML models. Mainly, SageMaker eliminates the complexity of building ML models. And it simplifies the training process. Besides, it scales seamlessly based on demands.
SageMaker offers many key advantages. It allows using compute resources dynamically. Also, SageMaker has a rich library of ML algorithms with which you can build advanced ML models. SageMaker offers Jupyter notebooks that help to develop, train and deploy ML models quickly.
SageMaker is the AWS platform used for building and deploying ML models. EC2 is another AWS service that offers virtual compute machines known as instances.
SageMaker is a platform that you can use to build, train and implement ML models. SageMaker supports frameworks such as TensorFlow, XGBoost, etc.
It is a class with which you can create and interact with SageMaker transform jobs.
Sagemaker is nothing but a hosted environment where you can create, train, and deploy ML models. Sagemaker supports creating ML models more quickly than any other platform.
At a glance, AWS Sagemaker is the platform that supports creating, training, and deploying ML models in the production-ready environment. You can build cost-effective and sophisticated ML models with the help of SageMaker. Above all, SageMaker can scale rapidly – no matter the training data size. We hope this blog might have helped you learn what is AWS SageMaker and associated things in greater detail. But, if you aspire to learn more about AWS SageMaker, you can enroll in "AWS Training" and get a certification.
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Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .
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