Machine Learning is the hottest topic in the current era and the leading cloud provider Amazon web service (AWS) provides lots of tools to explore Machine Learning, creating models with a high accuracy rate. This article makes you familiar with one of those services on AWS i.e Amazon Sagemaker which helps in creating efficient and more accuracy rate Machine learning models and the other benefit is that you can use other AWS services in your model such as S3 bucket, amazon Lambda for monitoring the performance of your ML model you can use AWS Cloudwatch which is a monitoring tool.
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models.
Amazon SageMaker makes it easy to build ML models and get them ready for training by providing everything you need to quickly connect to your training data and to select and optimize the best algorithm and framework for your application. Amazon SageMaker includes hosted Jupyter notebooks that make it is easy to explore and visualize your training data stored on Amazon S3. You can connect directly to data in S3, or use AWS Glue to move data from Amazon RDS, Amazon DynamoDB, and Amazon Redshift into S3 for analysis in your notebook.
To help you select your algorithm, Amazon SageMaker includes the 10 most common machine learning algorithms which have been pre-installed and optimized to deliver up to 10 times the performance you’ll find running these algorithms anywhere else. Amazon SageMaker also comes pre-configured to run TensorFlow and Apache MXNet, two of the most popular open-source frameworks. You also have the option of using your own framework.
You can begin training your model with a single click in the Amazon SageMaker console. Amazon SageMaker manages all the underlying infrastructure for you and can easily scale to train models at the petabyte scale. To make the training process even faster and easier, AmazonSageMaker can automatically tune your model to achieve the highest possible accuracy.
Once your model is trained and tuned, Amazon SageMaker makes it easy to deploy in production so you can start running generating predictions on new data (a process called inference). Amazon SageMaker deploys your model on an auto-scaling cluster of Amazon EC2 instances that are spread across multiple availability zones to deliver both high performance and high availability. Amazon SageMaker also includes built-in A/B testing capabilities to help you test your model and experiment with different versions to achieve the best results.
Amazon SageMaker takes away the heavy lifting of machine learning, so you can build, train, and deploy machine learning models quickly and easily.
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- What is AWS EC2 Instance Storage?
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- Launching an EC2 instance using AWS CLI
- Launching AWS EC2 Instance using Python
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