Amazon SageMaker is a fully-managed service for a cloud platform that provides developers and data scientists a great way to understand the tools, technologies and concepts behind machine learning and it enables developers to easily build and deploy machine learning models.
It makes easy for developers to build machine learning models by doing most of the data science heavy lifting while we provide the code telling it what we want to get done. This can be used together or independently to build. The machine learning benefits advanced analytics for customer data or back-end security threat detection.
The AWS sagemakers accelerate all of the machine learning efforts and allow to add machine learning to the production applications quickly It can be used for a variety of use cases that include lots of data requiring pattern detection, labeling. Amazon SageMaker can accomplish some machine learning jobs like Facial recognition and consumer buying behavior.
Amazon SageMaker supports Jupyter notebooks, which are open source web applications that help developers share live code, ie, using built-in algorithms of SageMaker, our models could be deployed with a simple line of code.
The workflow for creating machine learning models is simplified by the SageMaker by its built-in algorithms.
SageMaker permits us to use a Jupyter notebook interface to launch and break down machine learning processes in simpler lines of Python code and it abstracts away many of the complex infrastructural details to training.
It includes two “high-level API” and “low-level API” APIs. The high-level API to work with previously optimized machine learning libraries eg: MXNet, TensorFlow and low-level API that allows running completely custom jobs.
There are 3 broad areas where the tasks are categorized
1. Build: It defines the problem, collects data, analyzes and cleans, transforms and engineers the data into the desired form.
2.Train: Train the model to learn the patterns from the engineered data.
3. Deploy: Deployment of the model into a production system where it caters its services to the larger ecosystem.
How SageMaker works?
Amazon SageMaker is not one software tool, but rather a collection of tools and services. All of these tools and services allow the training and deployment of machine learning models and we can choose and select different items, combinations of items based on our needs.
It provides Jupyter NoteBooks running R/Python kernels with a compute instance that we can choose as per our data engineering requirements on demand.
We can use the data set and import it into Amazon SageMaker, using the libraries, using your own code, and you can train and deploy a model.
1. Writing the image :
Docker image is used to manage the SageMaker machine learning training jobs and the first step to running the job is building the container.
2: Uploading the image to Amazon ECR
The SageMaker-compatible image is getting registered with Amazon’s container registry service.
3: Train the model
It is the interacting with SageMaker jobs programmatically is using the sagemaker API.
4: Deploy the model
Deployment jobs are managed using the container image. When a deployment request is received by AWS SageMaker, it begins to spin up the EC2 instance and loading in the container. It then extracts and injects the model to the path in the container.
AWS Sagemaker has been really effective for the majority of data scientists who want to accomplish truly end-to-end ML solutions. Its focuses on abstracting a ton of software development skills required to accomplish the task along with being highly effective, flexible and economic.
