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February 6, 2020Voiced by Amazon Polly |
Overview
AWS Machine Learning is among the fastest-growing technologies today and being backed with ML skills is considered one of the most sought-after attributes in today’s job market.
This blog will give you an understanding of AWS ML and SageMaker.
What is Machine Learning?
Machine Learning is the study of various algorithms and models that a computer system uses to execute certain tasks without any explicit instructions.
Machine Learning Methods:
- Supervised ML Algorithm
In the Supervised Method, input and output variable is given. It learns from the input and output data to produce the desired output. - Unsupervised ML Algorithm
In the Unsupervised Method, only input data is given. It uses only input data to learn and produce the output.
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What is AWS SageMaker?
Amazon SageMaker is a machine learning service that helps developers and data scientists to build and train the machine learning models and then directly upload them to the production environment.AWS SageMaker provides an integrated Jupyter authoring notebook instance for access to your data source for exploration and analysis.AWS SageMaker also provides optimized algorithms to run efficiently with large data in a distributed environment.
AWS SageMaker Providing the following features:
- Amazon SageMaker Studio
Amazon SageMaker Studio is an environment to build, train, analyze and deploy models in a single application. - Amazon SageMaker Ground Truth
It is used to create high-quality training datasets. - Amazon SageMaker Autopilot
It is helpful to build classification and regression models quickly. - Amazon SageMaker Model Monitor
It continuously monitors the quality, such as data drift of learning models in a production environment. - Amazon SageMaker Notebooks
Notebooks with SSO integration, fast startup and single-click sharing. - Amazon SageMaker Experiments
It automatically tracks the inputs, parameters, configuration and results so you can easily manage your Machine Learning Experiments. - Amazon SageMaker Neo
It enables the developers to train the model once and runs them anywhere in the Cloud. - AWS Marketplace
It is the platform where customers can find, buy, deploy and manage third-party software, data and services. - Amazon SageMaker Debugger
It automatically detects and alerts while errors are occurring. - Amazon Augmented AI
It is used to implement Human review for Machine Learning predictions. - Automatic Model Tuning
It helps to find the best version of a model.
How AWS SageMaker works?
- Generate Data
To design a solution for any business problem, we need data, where a type of data depends on a problem.To preprocess the data, we need to do the following:- Fetch the Data (Pull datasets into a single repository)
- Clean the Data (Inspect the data and clean it if needed)
- Prepare / Transform the Data (Combine attributes into new attribute to improve performance)
In AWS SageMaker, you can preprocess the Data in Jupyter notebook instance.
- Train a Model
- Training the Model
To train a model, you need to use an algorithm. You can use algorithms that are provided by Amazon SageMaker. Or you can use your algorithm to train a model - Evaluating the Model
You evaluate the model to determine whether the accuracy of the inferences is acceptable or not. You can use AWS SDK for Python (BOTO) or High-level Python library which are provided by AWS SageMaker to send a request to model for inferences.In AWS SageMaker you can use a Jupyter notebook instance to train and evaluate the model
- Training the Model
- Deploy the Model
In AWS SageMaker, you can deploy your model using SageMaker Hosting Services.
Reference
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- Amazon SageMaker Ground Truth
- Amazon SageMaker Model Monitor
- Amazon SageMaker Neo
- Amazon SageMaker Notebooks
- Amazon SageMaker Studio
- Automatic Model Tuning
- AWS Marketplace
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- Machine Learning
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