Peak Launches AI Platform-as-a-Service to Improve Enterprise AI System Deployment
High growth artificial intelligence (AI) company Peak has launched its AI Platform-as-a-Service (PaaS) beta programme to improve AI success rates and data scientist productivity across the enterprise.
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PaaS beta set to increase data team productivity by 300%
• Unproductive activity related to poor legacy systems accounts for 80% of data scientist output
• PaaS offers seamless connectivity with limitless data sources
• Enabling simple, self-serve or managed AI deployments across business systems
Peak’s AI PaaS beta program sees the company’s AI System opened up to the data science and engineering community for the first time, helping enterprise users to build, train and deploy machine learning solutions across their organizations at scale.
Peak’s AI System is a new category of enterprise technology product. Its AI PaaS combines the three critical components necessary for enterprise AI deployments:
- Infrastructure: data handling, storage, compute, scaling, extensibility, security, and robustness.
- Workflow: the workflow of taking raw data, interpreting it, joining, transforming, feature extraction, model training, orchestration, and deployment.
- Solutions: easy-to-build machine learning business solutions.
A recent PWC report found that just 4% of businesses have successfully implemented AI, which is unsurprising given data scientists spend up to 80%* of their time on unproductive tasks as a consequence of legacy systems. Peak’s new service aims to solve this, boosting success rates by making data teams four times more productive.
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Speaking at AWS re:Invent, Richard Potter, CEO of Peak, explained, “Research shows that AI projects often fail due to the complexity of deployment within the enterprise. Every enterprise must become AI-driven, yet no system is dedicated to this in the enterprise today. What’s more, we know 80% of a data team’s time is spent cleaning or collecting data, data manipulation, building training sets or with feature extraction. Peak’s AI system changes that, and with it data teams are able to focus on what they do best.”
“Legacy business systems are inflexible, slow and expensive to maintain. Data warehouses are not built to deliver machine learning solutions and data tends to exist in silos due to the proliferation of cloud applications. This all makes AI deployment challenging for many businesses.
“We’ve launched our Platform as a Service to enable teams to focus on machine learning and invest in the creation of their own machine learning solutions. Companies can then extract more value from data and experience the real benefits of AI.”
Today’s news marks Peak’s latest product development milestone and the beta platform is now open for any data science or engineering team to use, provide feedback and contribute to over time.
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Peak’s core product, the AI System (AIS), is currently deployed by organizations, including: ASOS, Footasylum and Speedy Hire. It embarked on the development of its AI PaaS in 2017, after securing £5m Series A funding in 2017 with MMC Ventures and Praetura Capital.
Peak is a pioneering artificial intelligence company on a mission to help businesses do great things with data, providing its customers with the technology and skills to become AI-driven. It is recognised for its unique technology plus service model, built upon its own AI System. This enables enterprises to put AI at the heart of its operations, boosting revenues and profits in turn.
Founded in November 2014, Peak has grown rapidly. In 2016, it secured seed funding to launch its subscription-based service and opened a development centre in Jaipur, India. In 2017 Peak secured Series A funding led by MMC Ventures to fund the company’s ongoing investment in machine learning and AI technologies, as well as further accelerate sales growth.
This investment in innovation and growth was recognised when Peak won the Judges’ Innovation Award at the GP Bullhound Northern Tech Awards in early 2018, in addition to being named in the top 25 fastest-growing tech companies.
