Category: Amazon Machine Learning

The AWS Machine Learning Visionaries Partners Report is a quarterly series that tracks, selects, collates, and distributes horizontal technology capabilities enabled by machine learning in areas that AWS expects to be transformative in 1-3 years. The series’ purpose is to share our insights with AWS Partners and to collect their interest, expertise, and insights in co-building along these prioritized themes. The reports include updates on series topics as we see changes in those areas, and new topics will also be added.

Infusing machine learning into core business processes such as credit scoring creates a competitive edge for banks and financial services institutions. It does not require a data science team, expertise, or platform rollout. Explore an ML-based credit-decisioning model built by ElectrifAi in collaboration with AWS whose model rapidly determines the creditworthiness of a SME, and data-driven, actionable insights reduce the overall processing cost and are consistent and free from any potential human biases.

Featurization is one of the most difficult problems in machine learning. Learn how graph features engineered in Neo4j can be used in a supervised learning model trained with Amazon SageMaker. These novel graph features can improve model performance beyond what’s possible with more traditional approaches. Together, these components offer a graph platform that can be used to understand graph data and operationalize graph use cases.

As direct to customer (D2C) gains popularity among retailers, there’s an increasing need to mix online and offline experiences to improve customer engagements and sentiment. One such popular channel is popup stores. This post explores a Capgemini solution that uses Amazon Web Services (AWS) to help retailers engage with customers in a smart way. The solution leverages deep learning to enhance the customer experience through gamification and provides key insights and marketing leads to retailers.

Like many of today’s leading media and streaming platforms, PBS wanted to take its overall user experience to the next level. That’s why PBS approached AWS Premier Tier Consulting Partner ClearScale, a leader in machine learning. ClearScale came up with a detailed roadmap for tackling PBS’s recommendation system project that included data operations, MLOps, and demonstrational user interface. Together, PBS and ClearScale decided to move forward with an AWS-powered solution on top of Amazon Personalize.

Deploying machine learning (ML) models as a packaged container with hardware-optimized acceleration, without compromising accuracy and while being financially feasible, can be challenging. As machine learning models become the brains of modern applications, developers need a simpler way to deploy trained ML models to live endpoints for inference. This post explores how a ML engineer can take a trained model, optimize and containerize the model using OctoML CLI, and deploy it to Amazon EKS.

Industry analyst firm Gartner has published its annual report evaluating cloud AI developer services, the 2022 Magic Quadrant for Cloud AI Developer Services (CAIDS). AWS was once again named a Leader and placed highest among 13 recognized vendors for “Ability to Execute.” Choosing the right provider for cloud AI developer services is critically important right now. AWS Partners can leverage this report with their customers to showcase the value that AWS will bring to them.

A closed loop assurance system predicts network events, such as faults and congestions, that are highly probable of causing service degradation or interruption, and automatically take preventive actions to avert service disruptions. Learn how Infosys leveraged AWS data streaming, data analytics, and machine learning services to ingest, process, and analyze high volumes of data from disparate sources; and to build ML models to predict network events that cause service degradation.

Learn now Provectus looked into how machine learning models were prototyped and evaluated at VTS, and then delivered a template-based solution enabling their data scientists to more easily create Amazon SageMaker jobs, pipelines, endpoints, and other AWS resources. The resulting coherent set of templates, with usage cookbook and extension guidelines, was applied successfully on an ML model that predicted leasing outcomes.

Healthcare customers use Snowflake to store all types of clinical data in a single source of truth. One method for gaining insights from this data is to use Amazon Comprehend Medical, which is a HIPAA-eligible natural language processing service that uses machine learning to extract health data from medical text. Learn how the Snowflake Data Cloud allows healthcare and life sciences organizations to centralize data in a single and secure location.