Category: Artificial Intelligence

Increasing penetration of analytics in the life sciences industry is expected to drive significant growth for businesses in the coming years. Learn about Accenture’s life sciences data and analytics accelerator which enables customers to respond to these challenges and use data for their competitive advantage. Particular focus is given to the commercial domain and use of analytics to increase customer engagement and optimize sales and marketing.

Customer experience is at its best when a customer perceives the experience offered is unique and aligns to their preferences. The need to engage, at a very personal level, becomes key. Learn how Capgemini’s data and analytics practice implements customer intelligence platforms on AWS to help companies build a unified data hub. This enables customer data to be converted into insights that can be used for reporting and building AI/ML predictive analytics capabilities.

Kubernetes is hardware-agnostic and can work across a wide range of infrastructure platforms, and Kubeflow—the self-described machine learning toolkit for Kubernetes—provides a Kubernetes-native platform for developing and deploying ML systems. Learn how D2iQ Kaptain on AWS directly addresses the challenges of moving ML workloads into production, the steep learning curve for Kubernetes, and the particular difficulties Kubeflow can introduce.

Organizations are pursuing agility by developing automation for business processes using virtual robots, but they need technology that goes beyond simply helping automate a single process. UiPath offers an end-to-end platform for automation with the enterprise-ready cloud infrastructure, AI services, and intelligent automation solutions from AWS that provide the foundation to scale your enterprise automation. UiPath is a leader in the “automation first” era that is enabling robots to learn new skills through AI/ML.

Learn about the AWS AI services stack for government agencies and partners to develop intelligent automation solutions to extract information from digitalized paper documents. Intelligent Document Processing (IDP) is a solution that enables extraction and processing of specific data elements from documents using AI and machine learning techniques. AWS services that add AI/ML intelligence to IDP solutions include Amazon Textract, Amazon Comprehend, Amazon Augmented AI, and Amazon Kendra.

We are excited to introduce AI for data analytics (AIDA) partner solutions which embed predictive analytics into mainstream analytics workspaces. These AI/ML solutions from AWS Partners have interfaces and integrations that help bring predictive analytics into the normal workflow of business experts. AWS AIDA includes partner solutions from Amplitude, Anaplan, Causality Link, Domo, Exasol, InterWorks, Pegasystems, Provectus, Qlik, Snowflake, Tableau, TIBCO, and Workato.

Dataiku orchestrates the entire machine learning lifecycle and makes it accessible to data scientists and analysts alike. With deep integration with AWS AI tools, Dataiku enables users to augment their analytics workflow with pretrained NLP and computer vision models. Learn how you can use Amazon Comprehend and Amazon Rekognition plugins on Dataiku Data Science Studio (DSS) to build a simple workflow of NLP and computer vision use cases, respectively.

Building effective machine learning models requires storing and managing historical data, but conventional databases can quickly become a nightmare to regulate. Queries start taking too long, for example, slowing down business decisions. Learn how to use Amazon Redshift ML and Query Editor V2 to create, train, and apply ML models to predict diabetes cases for a sample diabetes dataset. You can follow a similar approach to address other use cases such as customer churn prediction and fraud detection.

Montoux’s next-generation decision science platform for insurers uniquely combines actuarial and data science in its software, providing a modern, flexible, and sophisticated alternative to legacy actuarial software. Learn how the AI-driven model replication module is specifically developed to support an insurer’s transition to cloud and reduces the time, cost, and effort required to migrate to a new platform.

As multi-tenant SaaS providers look to leverage machine learning services, they must consider how they’ll protect the data that flows in and out of these services from different tenants. Learn how tenant isolation of machine learning services can be achieved using AWS IAM, and how the integration between IAM, Amazon SageMaker, and many other AWS services provide developers with a rich set of mechanisms that can be applied to realize tenant isolation goals.