Rackspace Technology®(NASDAQ: RXT), a leading end-to-end, multicloud technology solutions company, announced today that it has achieved Amazon Web Services (AWS) Machine Learning Competency status in the new Applied Artificial Intelligence (Applied AI) and Machine Learning Operations (MLOps) categories. This designation recognises that Rackspace Technology has demonstrated deep experience and expertise in building or integrating machine learning (ML) solutions on AWS. AWS Partners recognised as part of the AWS Machine Learning Competency expansion help customers take advantage of intelligent solutions for the business, from creating, automating, and managing end-to-end ML workflows to modernising applications with machine intelligence.
The AI and ML driven applications are maturing rapidly and creating new demands on enterprises. AWS is keeping pace and continuously evolving AWS Competency Programs to provide customers an ability to engage enhanced AWS Partner technology and consulting offerings. AWS launched the two new categories within the AWS Machine Learning Competency to help customers easily and confidently identify and engage highly specialised AWS Partners with Applied AI and MLOps capabilities. With this programme expansion, customers will be able to go beyond the current data processing and data science platform capabilities and find experienced AWS Partners who will help productionise successful models (MLOps) and find off-the-shelf packages for their business problems (Applied AI).
Achieving the Applied AI and MLOps category differentiates Rackspace Technology as an AWS Partner with deep domain expertise and proven customer success.
“Rackspace Technology is proud to be one of the first AWS Partners to achieve the new Applied AI and ML Ops category status,” said Jeff Deverter, CTO, Solutions at Rackspace Technology. “Our team is dedicated to helping companies achieve their business transformation goals by leveraging the agility, breadth of services, and pace of innovation that AWS provides and accelerating a customer’s ability to move to Cloud Native. Our new category status further spotlights our strengths in the data practice and our strong achievements with AWS.”
Applied AI empowers a customer’s data to make automated recommendations, take preemptive action, and streamline decision-making while leveraging the Rackspace Technology proven cloud-native AI frameworks and deep expertise to design, develop, deploy, and maintain ML applications. MLOps significantly reduces the ML lifecycle with Rackspace Technology model factory framework leveraging automation, an easily repeatable model, and resource training to achieve rapid development, training, scoring and deployment of models.
AWS is enabling scalable, flexible, and cost-effective solutions from startups to global enterprises. To support the seamless integration and deployment of these solutions, AWS established the AWS Competency Program to help customers identify Consulting and Technology AWS Partners with deep industry experience and expertise.
Rackspace Technology Applied AI and MLOps Case Studies
Rackspace Technology worked with Brave Software, Inc, a company that provides a free, open-source private and secure web browser for PC, Mac and mobile environments, to improve the scalability of Brave’s software, increase the team’s efficiency and reduce infrastructure costs by 50 percent.
Once on AWS, Rackspace Technology made critical improvements in automating Brave’s ML processes by employing MLOps Foundations to provide an architecture pattern for Brave. These cloud-based data pipelines provided a consistent structure for ML development, training and deployment, accelerating the process of publishing new models from several days to several hours. As a result of improvements to ML testing and functionality.
“Working with Rackspace Technology and AWS was beneficial to the continued success and scaling of Brave,” said Jimmy Secretan, VP of Services and Operations, Brave Software. “It substantially improved the way we created and deployed new models, which has helped us to be much more responsive to advertisers’ needs.”
McChrystal Group, a renowned advisory services and leadership development firm that helps organisations identify opportunities to improve their performance so they can operate optimally in today’s complex environments, worked with Rackspace Technology to develop a machine learning-based natural language processing algorithm to analyse surveys faster and with greater accuracy.
Rackspace Technology worked closely with McChrystal Group to understand the desired output from the ML data processing and was then able to select the right tools and ML models to ensure the delivery of high value data derived from processing responses to open ended questions. The solution uses Amazon Athena, AWS Glue, Amazon Simple Storage Services (Amazon S3), Amazon SageMaker, and Amazon Translate and the automated ML natural language processing solution enables the company to replace a time intensive manual process that consumed considerable resources with an automated process that is significantly faster, more consistent and replicable.
“Rackspace Technology understood our vision and displayed a depth of expertise in designing and implementing a solution that will help us process our surveys faster and more accurately than before,” said Victor Bilgen, Partner and Head of McChrystal Analytics. “Due to our experience working with the Rackspace Technology team gave us a lot of confidence that they would be the right partner to deliver high quality results on the project.”
PipeSearch, a U.S.-based software company that connects and supports the global pipe industry, worked with Rackspace Technology to expand data modernisation and ML capabilities to aide PipeSearch’s efforts to revolutionise the pipe industry with a modern supply chain solution.
The Rackspace Technology team designed a data flow plan and extract data to be fed into a searchable database and provide normalised descriptions, allowing easy access to customers. The solution also allowed the team to identify data it was unsure about, which was then used to retrain the model, further strengthening results.
“The Rackspace Technology team was incredibly collaborative and the company’s expertise in ML and data analytics helped us simplify the difficult problem of normalising non-standard raw inventory data,” said Briggs Thompson, co-founder of PipeSearch. “Using a combination of an agile approach with sprints and common tools, Rackspace Technology helped us stay on track and delivered a successful solution.”
To learn more about Rackspace Technology AI and ML capabilities, please visit https://www.rackspace.com/data/ai-machine-learning.