A small number of companies manage to leverage the true value of their machine learning proofs of concept, and the majority of those are still struggling to overcome the experiment-production gap for their AI applications fueled by machine learning and data. Learn about MLOps, why organizations should care about it on their AI journey, and how DXC Technology and AWS can help to quickly integrate MLOps best practices into your daily business using the MLOps Quick Start for MLOps on AWS.
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.
In line with Capgemini Group‘s sustainability vision to become a net zero business by 2040, Sogeti has collaborated with AWS to find a pragmatic solution that is helping to bring this vision to life—the Sogeti Carbon Estimator (SCE). This tool can be used to automatically bring insights into the carbon footprint of any cloud component used to serve technology, including complex AI solutions enabled by MLOps. SCE highlights the goal of many cloud providers and businesses—to unlock the value of cloud sustainably.
Recognizing a user’s signature is an essential step in banking and legal transactions, and typically involves relying on human verification. Learn how Capgemini uses machine learning from AWS to build ML-models to verify signatures from different user channels including web and mobile apps. This ensures organizations can meet the required standards, recognize user identity, and assess if further verifications are needed.
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.
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.
Artificial intelligence (AI) and machine learning (ML) are maturing rapidly. According to Gartner, 75% of enterprises will shift from piloting to operationalizing AI by 2024. That’s why we are expanding the AWS Machine Learning Competency to help customers identify and engage qualified AWS Partners that have deep technical expertise and proven customer success in the areas of Applied AI and Machine Learning Operations (MLOps).
How Provectus and GoCheck Kids Built ML Infrastructure for Improved Usability During Vision Screening
For businesses like GoCheck Kids, machine learning infrastructure is vital. The company has developed a next-generation, ML-driven pediatric vision screening platform that enables healthcare practitioners to screen for vision risks in children in a fast and easy way by utilizing GoCheck Kids’ smartphone app. Learn how GoCheck Kids teamed up with Provectus to build a secure, auditable, and reproducible ML infrastructure on AWS to ensure its solution is powered by highly accurate image classification model.
Deploying AI solutions with ML models into production introduces new challenges. Machine Learning Operations (MLOps) has been evolving rapidly as the industry learns to marry new ML technologies and practices with incumbent software delivery systems and processes. WordStream is a SaaS company using ML capabilities to help small and mid-sized businesses get the most out of their online advertising. Learn how Slalom developed ML architecture to help WordStream productionize their machine learning efforts.