Amazon Web Services (AWS) is offering the machine learning algorithm along with different model packages in the AWS marketplace. A user can choose from the free and paid algorithms in the AWS that can cater to different types of requirements, including the computer vision (CV), natural language processing (NLP), speech recognition, data, text, image, video, and predictive analysis. The announcement was made at the AWS re:Invent Conference and news blog later explained that how the developers can use the Machine Learning (ML) algorithm and deploy them directly on the SageMarker service of Amazon.
The Sagemaker uses a different machine, and deep learning framework for the following tools MXNet, TensorFlow, Chainer, and PyTorch. This helps in preparing and labeling the data, using different types of algorithm, deployment optimization according to the requirement, creating a model to make decisions and helping them convert into actions.
AWS is on a mission that will put machine learning in the hands of every developer and it directly connects with their recent launch of SageMaker. The launch of SageMaker in 2017 has made it one of the fastest growing services in the AWS history used by thousands of customers globally. Machine learning is developed on the concept that it solves the customer problem and saves time in problem-solving. Many Customers are using AWS for cloud and data center solutions, providing them different algorithm and model helping the user to solve the problems without investing again into machine learning.
The new machine learning category created by AWS offers different types of models in the marketplace that includes more than 150 algorithms and model package developed, with many new algorithms being added every day to solve different user and enterprise requirements for machine learning and models. Currently Retail has 35 products, media 19 products, manufacturing 17 products, and HCLS with 15 products and many more. The customers can find solutions to different user problems such as medical problems- Predict breast cancer risk, emergency service requirement according to the day, region and time using Machine Learning.
For more details on the Web Services, download our Whitepapers.
The customers can deploy the algorithms and model packages at AWS marketplace. For example, if a user subscribes for a machine learning solution they can directly deploy for the following using any one of the following:
1. SageMaker console.
2. SageMaker SDK.
3. Jupyter Notebook.
4. AWS CLI.
The Amazon SageMaker also protects users against threats by implementing security measures such as static scans, network isolation, and runtime monitoring. For the developers who use AWS as a marketplace for developing machine learning models and give access to users. The secured SLL connection for communication helps to deploy, ensuring the role-based access for deployment. AWS is giving a secured way for developers to monetize their work publishing different algorithm and model.
The access to the machine learning category can be done through the AWS marketplace, that can be achieved through Amazon SageMaker console or using the AWS marketplace. Once the algorithm has been successfully subscribed it can be accessed by the user using the console- SDK and AWS CLI. The algorithm and model can be deployed using a different model or algorithm by selecting the AWS marketplace.
Customers can pay for the subscription of the algorithm and model package combined along with the AWS resource fee. The AWS provides a consolidated monthly bill for all the purchased subscriptions. The AWS marketplace for machine learning can include different algorithm and model that includes deep vision AI inc, Knowledgent, Persistent Systems, H2Oai, Intel Corporation and many more are being added every day to cater to the requirements of enterprises.
AWS offers end-to-end solutions for applying different machine learning algorithms and models along with deployment and usage of the data. This helps many enterprises to directly using Amazon SageMaker for the deployment of the algorithm over the data without investing in creating, testing and deploying the Algorithm for the data.