5 min readSep 21, 2020

AWS : — Amazon web services

“Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 175 fully featured services from data centers globally. Millions of customers including the fastest-growing startups, largest enterprises, and leading government agencies are using AWS to lower costs, become more agile, and innovate faster.”

Machine Learning on AWS

AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist and expert practitioner. Named a leader in Gartner’s Cloud AI Developer services’ Magic Quadrant, AWS is helping tens of thousands of customers accelerate their machine learning journey.

Explore machine learning services that fit your business needs, and learn how to get started.

**Deep Learning Framework** >>**Choice and flexibility with broadest framework support**

**AI Services>>Easily add intelligence to your applications**

**ML Services>>Build, train and deploy machine learning models fast**

**Learning Tools>>Get hands-on with machine learning**

Explore AWS Machine Learning services

Amazon Sage Maker Build, train, and deploy machine learning models fast

Amazon Sage Maker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models at scale. It removes the complexity from each step of the ML workflow so you can more easily deploy your ML use cases, anything from predictive maintenance to computer vision to predicting customer behaviors.

Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. Sage Maker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost.

Build machine learning models

Improve productivity using Amazon Sage Maker Studio, the first fully integrated development environment (IDE) for machine learning

Use an IDE for ML development. For example, make updates to models inside a notebook and see how changes impact model quality using a side-by-side view of your notebook and training experiments.

Build and collaborate faster using Amazon Sage Maker Studio Notebooks

Generate a sharable link without manually tracking dependencies, to reproduce the notebook code.

You can choose from dozens of pre-built notebooks within Sage Maker for different use cases. You can also get hundreds of algorithms and pre-trained models available in AWS Marketplace making it easy to get started quickly.

Automatically build, train, and tune models with full visibility and control, using Amazon Sage Maker Autopilot

Automatically create machine learning models and pick the one that best suits your use case. For example, review the leaderboard to see how each option performs and pick the model that meets your model accuracy and latency requirements.

Amazon Sage Maker supports the leading deep learning frameworks

Train machine learning models

Organize, track, and evaluate training runs using Amazon Sage Maker Experiments

Track thousands of training experiments to understand the accuracy of your model. For example, view in a chart of how different time series datasets impact model accuracy.

Analyze, detect, and alert problems for machine learning using Amazon Sage Maker Debugger

Analyze and debug anomalies. For example, training a neural network will cease if gradients are determined to be vanishing. Sage Maker Debugger identifies vanishing gradients so you can remediate before training is impacted.

AWS is the best place to run Tensor Flow

AWS’ Tensor Flow optimizations provide near-linear scaling efficiency across hundreds of GPUs to operate at cloud scale without a lot of processing overhead to train more accurate, more sophisticated models in much less time.

Lower training costs by 90%

Amazon Sage Maker provides Managed Spot Training to help you to reduce training costs by up to 90%. This capability uses Amazon EC2 Spot instances, which is spare AWS compute capacity. Allowing you to save cost when you have flexibility with when to run training jobs.

Deploy machine learning models

One-click deployment

Amazon Sage Maker makes it easy to deploy your trained model into production with a single click so that you can start generating predictions for real-time or batch data. You can one-click deploy your model onto auto-scaling Amazon ML instances across multiple availability zones for high redundancy. Just specify the type of instance, and the maximum and minimum number desired, and Sage Maker takes care of the rest.

Keep models accurate over time using Amazon Sage Maker Model Monitor

Monitor models in production. For example, view charts with important model features and summary statistics, watch them over time and compare with the features used in training. Some features drift when the model is running in production, which can indicate the need to retrain your model.

Get high performance and low cost inference in the cloud

Using Amazon Sage Maker, you can deploy your trained machine learning models to Amazon Inf1 instances, built using the AWS Inferential chip, to provide high performance and low cost inference. Using Inf1 instances, you can run large scale machine learning inference applications like image recognition, speech recognition, natural language processing, personalization, and fraud detection.

Learning tools Get hands-on with machine learning

AWS Deep Composer gives developers a creative way to get started with machine learning. Get hands-on, literally, with a musical keyboard and the latest machine learning techniques, designed to expand your ML skills.

AWS Deep Lens helps put machine learning in the hands of developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained models designed to expand deep learning skills.