Category: Amazon Machine Learning

Operationalizing machine learning models can be a challenge due to lack of established ML architecture and its integration with the existing landscape. DataRobot integrates with AWS and provides the flexibility for a model trained in DataRobot to be deployed on AWS services with centralized model governance, management, and monitoring. Learn how the DataRobot AutoML platform orchestrates the complete model development and training lifecycle.

Leveraging data to make better decisions is critical for driving optimal business outcomes. Palantir empowers organizations to rapidly extract maximum value from one of their most valuable assets—their data. Palantir Foundry solves for the real-world application of AI, and not how it works in the lab. Effective AI is impossible without a trustworthy data foundation, a representation of an institution’s decisions, and the infrastructure to learn from every decision made.

With exponential increases in the amount of visual data such as images and videos being generated, image analytics is fast becoming a major business driver for many organizations. The Deloitte Image Recognition Framework is a cloud-based image recognition platform leveraging machine learning to automatically distinguish between two images. It helps customers deploy image recognition and analytics solutions and customize it per their business requirements.

Asset integrity management is a key activity for energy companies, and with recent advances in the field of machine learning, specifically computer vision, there are digital technologies that can enhance customers’ existing workflows and help plan preventative work. Learn how Wipro’s visual inspection and integrity management solution, InspectAI, can help customers deploy a cloud-based solution and transform their inspection process on AWS.

There is broad acceptance that AI and ML will help improve health outcomes for patients, and make healthcare more affordable. Data Science as a Service (DSaaS) from Change Healthcare is a secure, managed, healthcare data science platform that customers can leverage the embedded datasets and load their own datasets to be linked to deliver transformative and compliant insights. Learn how Change Healthcare built DSaaS to address the needs of practitioners developing AI/ML algorithms.

Despite the investments and commitment from leadership, many organizations are yet to realize the full potential of artificial intelligence (AI) and machine learning (ML). How can data science and analytics teams tame complexity and live up to the expectations placed on them? MLOps provides some answers. Hear from AWS Premier Consulting Partner Reply how you can “glue” the various components of MLOps together to build an MLOps solution using AWS managed services.

Amazon Connect is an easy-to-use cloud contact center platform that helps enterprises provide superior customer service at a lower cost. TCS has built an adapter that can pick up the customer queries from social messenger channels and bring them into the web chat channel of Amazon Connect. This post describes the high-level architecture of the TCS solution, potential benefits, and ways to extend the solution to leverage other AWS services.

Organizations that have foresight into their business have a competitive advantage. Advanced analytics that enable foresight have historically required scarce resources to develop predictive models using techniques like machine learning. Traditionally, this is a difficult endeavor, but recent progress in ML automation has made democratization of ML a possibility. Learn about the value of augmenting analytics with ML through the Amazon SageMaker for Tableau Quick Start.

Machine learning allows users to drive insights about their business, and the AutoML approach speeds up this process through the automation of ML pipeline steps. Learn how Domo created AutoML capabilities powered by Amazon SageMaker Autopilot, which is a fully managed AWS solution that automatically creates, trains, and tunes the best classification and regression ML models based on the data provided by a customer.

Achieving “speed of thought” or instant analytics on large data sets is a key challenge for business intelligence platforms. Traditionally, data engineers would design and deliver an optimized, aggregated subset of the data to a data warehouse to drive the visualization. This can often take weeks of development and testing or incur significant infrastructure costs. Learn how Indexima uses machine learning and hyper indexes to automate this process and accelerate analytics by up to 1000x across a full data set on Amazon S3.