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Explore machine learning on AWS

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By John White Last Updated on Jun 14, 2021

Today, Machine learning is not only more accessible in terms of programming language and development paths but also in terms of hardware resources. Amazon SageMaker, part of the AWS ecosystem, provides developers and data scientists with the environment required to build and train an AI model effectively.

Machine Learning in the Cloud

Amazon SageMaker is designed to be an all-in-one solution for artificial intelligence development in the cloud. It handles everything from the development of algorithms based on problems to deployment to a production environment. The process in between these points — the process of training your AI using machine learning — is simplified and fully supported.

That process begins with data collection and labeling. This is one of the very few parts of machine learning where human operators are required. You begin collecting data using the built-in tool of Amazon SageMaker, known as Ground Truth.

Suggested Read: How do machines learn?

Ground Truth is unique in one particular respect: it provides access to human operators that are used to data labeling and processing. You simply define a workflow (or select one from the provided models) and define labeling tasks based on the AI you want to develop. The rest of the process is fully automated.

Amazon SageMaker Ground Truth alone is a big leap in the right direction. Rather than investing a lot of time in manually processing the initial training data, developers can simply focus on preparing the right active learning process for your AI. This leap makes machine learning not only more accessible but also more effective.

Performance Is Key

That brings us to the actual machine learning process. Amazon SageMaker provides the hardware needed for a speedy and accurate machine learning process. Active training of AI is up to 10 times faster and more accurate. As long as the training data streams are correct, you can expect to have a capable AI in no time.

It doesn’t stop there either. Amazon SageMaker automatically optimizes frameworks like TensorFlow, SparkML, Keras, and PyTorch to further optimize the learning process. The company even provides detailed guides on how to create and train AI using machine learning.

When you take into other services presented within the AWS ecosystem, you will see how huge the possibilities really are. Buckets are used to store data, so you always have the storage you want to collect information; you will have enough storage buffer for data processing too.

Amazon SageMaker also integrates well with services like IAM. You can maintain the security of your machine learning environment using the same tools that you used to maintain the security of other AWS services. You can even use credentials and roles to specify access to different parts of SageMaker.

implementation of AI

Seamless Implementation of Artificial Intelligence

Here’s another interesting thing about Amazon SageMaker: you don’t need to train your own AI model. There is a marketplace filled with pre-trained AIs and they are all easy to integrate with the existing applications you run on AWS.

Also Read: Machine learning and artificial intelligence

The GluonNLP Sentence Generator, for instance, is a pre-trained sequence sampler that can be used to generate sentences — human-readable sentences — using predetermined parameters. GluonNLP also supports translations and other features.

The module is present for object detection, vision-oriented analysis, and classification. The marketplace is filled with SageMaker modules, with infrastructure software for AI implementation and business applications. Since you don’t have to train your own AI, you can focus on the integration part of the equation.

The presence of a marketplace filled with pre-trained AIs isn’t the only thing that makes AWS the perfect environment for AI enthusiasts and researchers. There is also a machine learning certification from AWS, designed for those who want to develop AI or be more knowledgeable in data science in general.

The certification takes you through the process of selecting the right machine learning approach for specific problems, creating the right environment for machine learning, and creating a scalable deployment for AI. It is an in-depth platform that brings into line well with the services offered by Amazon, counting the Amazon SageMaker.

Rapid Deployment Of AI

It is clear that Amazon SageMaker and AWS, in general, are here to speed up AI development while making it more accessible. You don’t have to invest in expensive hardware like the NVIDIA DGX-1 to gain access to machine learning. You also do not have to master critical programming to create your algorithm and begin training AI.

We’re seeing solutions from the likes of Deep Vision and Plasticity becoming more accessible too. With SageMaker powering their developments, established AI companies and research bodies can now open their APIs to more developers. They can even integrate their AIs into existing apps to create a more complex solution. In the case of Plasticity, their natural language processing AI can now be used in enterprise solutions and business-specific use cases.

Also Read: How to manage AI projects?

The rapid machine learning procedure leads to faster implementation of AI. We are seeing AI being implemented in various industries and across the globe. Nodeflux, an Artificial Intelligence organization based in Indonesia, is making smart city solutions based on vision AI while leveraging the power of AWS as a cloud ecosystem. Other startups and AI devotees are following suit.

By encouraging AWS as an ecosystem, smaller, independent programmers can have the same access to machine learning as big research firms and tech organizations. Do not be amazed if the next Alexa or Siri is a product built by a small team of data scientists and AI researchers. Amazon SageMaker makes this type of deployment possible.