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General Published on: Fri Feb 10 2023
If you are into Machine Learning, you already know what Amazon SageMaker all is about. It provides an ideal environment for you to proceed with developing your machine learning algorithms. Amazon SageMaker has got solutions for every stage of model building and model deployment. You can work with a Data Science Services Company and get the most out of Amazon SageMaker for your machine learning model developments.
Features of Amazon SageMaker
There are a few important features in Amazon SageMaker, which you can use for machine learning model development and implementation. Here are the most prominent features.
Before you build a machine learning model, you can evaluate it with the help of Amazon SageMaker. It provides two different methods to test the machine learning models. They include online testing and offline testing. If you are going to do online testing, you can use Jupyter Notebook and proceed with validations. On the other hand, you can deploy the model and then proceed with analyzing the handle requests sent to the traffic threshold to evaluate the model via online testing.
After you deploy the model, you will be able to proceed with deploying it on Amazon SageMaker. This is where you can use the CreateModel API in Amazon SageMaker. You can define the configurations and endpoint to deploy the model successfully.
You can allow Amazon SageMaker to monitor the performance of the model in real-time. This is where you can analyze all performance deviations as well. You will also be able to train new samples and validate them with real-time data.
What are the benefits of Amazon SageMaker?
You can expect to receive multiple benefits out of Amazon SageMaker as well. For example, it has a debugger, which you can use to specify the hyperparameters automatically. On top of that, you can receive assistance to deploy the end-to-end machine learning pipeline within a short period.
Even if you have a requirement to deploy the machine learning models at the edge, you may think about using SageMaker Neo. On top of that, the Machine Learning compute instance would suggest the different instance types while you are training and running the machine learning models.
How to start using Amazon SageMaker?
Now you know what Amazon SageMaker can deliver to you. You can use it for storage, computation, and even for data processing. On top of that, you will be able to use Amazon SageMaker to develop, train, log, and perform different types of machine learning models. After that, you can simply proceed to make ongoing predictions.
To experience all these benefits, you can partner with a data science consulting company like Hexaview Technologies. This is where you can get the help of experts in Amazon SageMaker to help you with model development and deployment on the cloud.
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