Why Would you need this service?

  • Open Platform
    Machine Learning is suitable for the data researcher, Machine Learning researcher, or developer. AWS offers Machine Learning services and tools tailored to fulfill your wants and level of expertise.
  • API-Driven Machine Learning Service
    Developers will simply add intelligence to any application with a various choice of pre-trained services that give computer vision, speech, language analysis, and chatbot practicality.
  • Broad Framework Support
    AWS supports all the most important Machine Learning frameworks, together with TensorFlow, Caffe2, and Apache MXNet, so you’ll bring or develop any model you select.
  • A Breadth of Computing Choices
    AWS offers a broad array of computing choices for coaching and inference with powerful GPU-based instances, compute and memory optimized instances, and even FPGAs.
  • Deep Platform Integrations
    ML services are deeply integrated with the rest of the platform together with the data lake and database tools you wish to run Machine Learning workloads. The data on AWS offers you access to the foremost complete platform for large data.
  • Comprehensive Analytics
    Choose from a comprehensive set of services for data analysis together with data storage, business intelligence, batch processing, stream process, data progress orchestration.
  • Secure
    Control access to resources with granular permission policies. Storage and database services provide sturdy coding to stay your data secure. Versatile key management choices enable you to settle on whether or not you or AWS can manage the encryption keys.
  • Economical
    Consume services as you wish them and just for the amount you utilize them. AWS pricing has no direct fees, termination penalties, or future contracts. The AWS Free Tier helps you start with AWS.

How we deliver this service

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.

Predictive Analytics  is a powerful approach that uses machine learning and artificial intelligence (AI) to predict incidents before they impact customers and end users. By using AI and predictive analytics, IT organizations are able to deliver seamless customer experiences that meet changing customer behaviour and business demands. Discover the critical steps required to build your IT strategy, and learn how to harness predictive analytics to reduce operational inefficiencies and improve digital experiences. 1tech will work with you to define your predictive analytics strategy.  We will help you define the required business result; collect and prepare relevant data; chose the right analytical tool and validate predictive model


  • Amazon SageMaker – To build, train, and deploy machine learning models fast. This 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 behaviours.
  • AI services – Easily add intelligence to applications with no machine learning skills required
  • Pre-trained AI Services provide ready-made intelligence for your applications and workflows to help you improve business outcomes – based on the same technology used to power Amazon’s own businesses. You can build AI-powered applications without any machine learning expertise.
  • Predictive analytics to help reduce customer churn through personalised offerings, support superior forecasting and set optimal product pricing

Benefits/ Typical Outcomes

  • Easily identifies trends and patterns
    Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans. For instance, for an e-commerce website like Amazon, it serves to understand the browsing behaviors and purchase histories of its users to help cater to the right products, deals, and reminders relevant to them. It uses the results to reveal relevant advertisements to them.
  • No human intervention needed (automation)
    With ML, you don’t need to babysit your project every step of the way. Since it means giving machines the ability to learn, it lets them make predictions and also improve the algorithms on their own. A common example of this is anti-virus softwares; they learn to filter new threats as they are recognized. ML is also good at recognizing spam.
  • Continuous Improvement
    As ML algorithms gain experience, they keep improving in accuracy and efficiency. This lets them make better decisions. Say you need to make a weather forecast model. As the amount of data you have keeps growing, your algorithms learn to make more accurate predictions faster.
  • Handling multi-dimensional and multi-variety data
    Machine Learning algorithms are good at handling data that are multi-dimensional and multi-variety, and they can do this in dynamic or uncertain environments.
  • Wide Applications
    You could be an e-tailer or a healthcare provider and make ML work for you. Where it does apply, it holds the capability to help deliver a much more personal experience to customers while also targeting the right customers.

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