AWS Machine Learning is one of the fastest-growing technologies right now. Being backed by ML skills is one of the most sought-after attributes in today’s job marketplace.
This blog will provide an overview of AWS ML, SageMaker.
What is Machine Learning?
Machine Learning refers to the study of algorithms and models that computers use to perform certain tasks without being explicitly instructed.
Machine Learning Methods:
Supervised ML AlgorithmIn Supervised Method, input variable and output variable are given. It uses the input and output data to produce desired output.
Unsupervised ML AlgorithmIn Unsupervised Method, only input data are provided. It only uses input data to learn the output and produce it.
What is AWS SageMaker?
Amazon SageMaker is a machine-learning service that allows developers and data scientists access to large amounts of data to optimize their performance in distributed environments.
AWS SageMaker provides the following features:

Amazon SageMaker StudioAmazon SageMaker Studio allows you to create, train, analyze, and deploy models within a single application.
Amazon SageMaker Ground TruthIt’s used to create high-quality training datasets.
Amazon SageMaker AutopilotIt’s useful to quickly build classification and regression models.
Amazon SageMaker Model MonitoringIt continuously monitors quality, such data drift, of learning models within a production environment.
Amazon SageMaker NotebooksNotebooks, SSO integration, quick startup and single-click sharing
Amazon SageMaker ExperimentsIt automatically records the inputs, parameters and configuration so that you can manage your Machine Learning Experiments.
Amazon SageMaker NeoIt allows developers to train the model once, and then run them anywhere in the Cloud.
AWS MarketplaceIt’s the platform that allows customers to find, buy, deploy, and manage third-party software, data, and services.
Amazon SageMaker DebuggerIt detects and alerts when errors are occurring.
Amazon Augmented AIIt’s used to implement Human Review for Machine Learning Predictions.
Automatic Model TuningIt allows you to determine the best version of a particular model.
How does AWS SageMaker work?

Generate DataTo create a business solution, we need data.
Fetch the Data (Pull data into a single repository).
Clean the Data (Inspect and clean the data if necessary).
Prepare/Transform the Data (Combine attributes to improve performance)
You can preprocess data in a Jupyter notebook instance using AWS SageMaker.
Train a ModelTraining the Model Amazon SageMaker provides algorithms. You can also use your algorithm to train a new model
Evaluation of the Model To evaluate the model, you can use AWS SDK (BOTO) and High-level Python Library (AWS SageMaker).

Deploy the ModelIn AWS SageMaker you can deploy your model using SageMaker Hosting Services.
Reference:
https://docs.aws.amazon.com/sagemaker/
https://aws.amazon.com/sagemaker/
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