SINGAPORE, 15 June 2020 - BasisAI, an early-stage artificial intelligence (AI) company, today announced the launch of Bedrock, an end-to-end machine learning platform which empowers data-driven enterprises to deploy AI in the real world responsibly. Bedrock is purposefully designed to help businesses achieve a faster time to impact from machine learning, whilst providing the governance-by-design that is required to develop trustworthy and performant systems.
AI adoption is expected to grow exponentially across the world - a recent IDC report found that, in 2019, companies spent USD37.5 billion on AI and this figure is set to more than double to USD97.9 billion by 2023. Another study by McKinsey shows that, by 2030, 70 per cent of companies globally will have adopted at least one type of AI technology and AI will add USD13 trillion to the global economy. The findings of the same study revealed that companies adopting AI successfully are more likely to apply core practices for using AI to drive value across the organisation and mitigate risks associated with the technology.
In parallel with advancements in AI and anticipated global growth, there has been heightened discourse in recent years on AI governance and the limitations of “black box” AI systems - opaque in nature due to perceived difficulty in explaining AI decision-making to consumers and regulators. Growing concerns pertaining to lack of transparency and unintended bias against minority groups, or based on gender, is at the core of what BasisAI is tackling with the launch of their responsible AI platform.
With Bedrock, BasisAI is enabling organisations to avoid black boxes by harnessing modern research and software engineering methods. Feng-Yuan Liu, CEO and Co-Founder, BasisAI, explains, “AI systems should be trustworthy and explainable - rebuilding trust in AI is key to addressing the foundational challenges that remain in the path to widespread adoption”.
Another major hurdle is overcoming challenges associated with productionisation of AI. Only a small percentage of AI projects typically transform from innovation experiments into real-time intelligent systems that can power business applications, products, services and customer experiences. Bedrock addresses these issues by offering a hybrid software-as-a-service (SaaS) platform that enables the end-to-end development of machine learning-powered applications. It orchestrates and monitors these applications while allowing enterprises to secure control over their data. Bedrock enables technology leaders to provide guide-rails and automation for their teams to adopt best-in-class responsible machine learning practice, both during the development process and oversight, once these systems go live.
“In the last year, we’ve been told repeatedly by enterprises that they often have to tread a balance between speed and governance. We have built Bedrock so that ambitious enterprises who want both rapid time-to-market and reliability don’t have to compromise. If you rush a machine learning software build and it’s not robust, it can fail silently in unintended ways, leading to bottlenecks down the line. Likewise, if governance is just a matter of filling in compliance paperwork, rather than built into the workflow, it’s simply going to stifle innovation.” said Mr Liu.
Bedrock brings two key value propositions to the data-driven enterprise:(1) Faster time-to-impact for machine learning in your enterprise
Bedrock enables machine learning operations (MLOps) practices which reduces the time-to-market of machine learning systems by up to 70 per cent. It does so by automating workflows for training, reviewing and deploying machine learning systems in a reproducible manner at scale. It allows technology leaders to leverage the best of their technical teams by enabling a single coordination point for collaboration, and removes the frustration from data science teams, allowing them to focus on what they do best.(2) Achieve AI governance-by-design
Having a strong AI governance platform enables machine learning adoption to be accelerated and used responsibly from the ground up. Every machine learning engine built on Bedrock is more reproducible and explainable. A single-pane-of-glass enables digital auditability and the ability to trace the provenance of models. Bedrock is also designed to detect unintended bias in complex models on attributes the organisation has deemed ethically objectionable. Constraints can then be placed on the models to mitigate bias based on determinants like gender or race. Only trusted and unbiased AI models make it through to production, and they continue to be monitored after go-live, alerting the technology teams before things go wrong.
Bedrock is now available for enterprises, please email get in touch for demos or more information.