When it comes to the potential of AI and machine learning, there’s no shortage of enthusiasm among business leaders eyeing opportunities to drive organizational performance. A recent study of executives and IT decision-makers found 95% of respondents believed their organization would benefit from embedding AI into daily operations—yet, that same study also found only 6% had actually adopted AI-powered solutions across their business. 

Despite all that enthusiasm, many organizations still encounter challenges implementing and managing AI and machine learning technology, especially at scale. That’s where MLOps enters the picture. A mashup of “machine learning” and “information technology operations,” MLOps is a rising space that’s improving collaboration between data scientists and IT professionals with the goal of productizing and industrializing machine learning.

A MLOps player we’re excited to work with is InfuseAI, a Taiwan-based startup that’s helping customers quickly develop and implement AI at scale by providing enterprises with tools to manage workflow from prototyping to production. 

The Nuts & Bolts

Problem:

Machine learning adoption is growing, but many organizations struggle to implement and use this technology effectively due to processes that obstruct experimentation and collaboration between product teams, operational staff and data scientists. Around 28% of AI and machine learning initiatives fail, with a lack of expertise and an integrated development environment being key reasons. Meanwhile, less than 15% of large enterprises have deployed AI capabilities into widespread production.

One issue is that developing and deploying AI tools is time consuming. A recent survey of nearly 750 business decision-makers found over half spent between 8 and 90 days deploying one machine learning model into production, while 18% needed even longer. Deloitte sees many organizations constrained by artisanal development and deployment techniques, with star data scientists given considerable creative control. The result is machine learning models that aren’t very scalable, due to manual and customized development processes. 

Against that backdrop, MLOps offers a clear solution to the challenges presented by AI implementation and management. It brings together and automates model development and operations, offering an opportunity to accelerate the process from start to finish. It also makes it easier to monitor and maintain models. 

As Deloitte puts it: as machine learning and AI increasingly become key drivers of organizational performance, enterprises are realizing the need to shift from personal heroics to engineered performance to more efficiently move ML models from development through to production and management.

Solution:

InfuseAI helps companies deploy and manage machine learning models with turnkey solutions that run on any infrastructure, from Kubernetes to AWS. Its first product is PrimeHub, an open source MLOps platform for enterprises that includes a model training environment, cloud or on-premise cluster computing and collaboration features for teams.

PrimeHub integrates tools for AI model training, deployment and management, allowing different teams to supervise and connect stages of AI development workflow for multiple projects and data sets. The platform helps data engineers and scientists collaborate smoothly with business analysts and IT teams, which gives enterprises the ability to quickly and efficiently productize models at scale.

Already, InfuseAI has helped enterprise customers cut down model deployment from several days to under one hour after training. Its products are tailored for enterprises with dedicated AI teams that need to handle multiple models in production, with customers coming from industries including manufacturing, financial services and healthcare. 

Getting to know InfuseAI

InfuseAI’s co-founders both honed their technical skills as active members of Taiwan’s developer community and startup ecosystem. CEO Chia-Liang Kao and COO Liang-Bing Hsueh met at an event for developers and then worked together for a U.S.-based open-source consulting firm. Chia-Liang also helped create g0v, a global civic tech community, while Liang-Bing co-founded Registrano, an online registration service that was acquired in 2013. 

In 2018, Chia-Liang was involved in setting up the Taiwan AI Academy when he found there weren’t suitable tools to help students manage AI model training environments and deployment. He then spoke with several companies and found it also took time and effort for them to manage AI workflows. He saw an opportunity to build a tool to help manage and automate the process, and brought in Liang-Bing to found InfuseAI later that year.

Last year, InfuseAI also hired Nick Chen as VP of business, a move that compliments the tech skills of the founders. Chen brings over 20 years experience in enterprise sales at companies including Akamai, TrendMicro and Microsoft. Since joining InfuseAI, he has been building a partnership network.

Why now?

MLOps could be the missing link for unlocking AI-powered enterprise solutions. The COVID-19 pandemic saw enterprises accelerate AI adoption, creating a larger need for MLOps services. According to Cognilytica, the market for MLOps solutions could grow from $350 million to $4 billion by 2022, while MLOps looks poised to take off among enterprises as AI investments heat up to further drive digital business transformations.

The future of InfuseAI

InfuseAI recently raised its Series A and the company has built solid momentum with customers in Taiwan. It now has an opportunity to expand in Japan and Southeast Asia. The MLOps space is seeing players rise in the U.S., Europe and Israel, but the market in Asia is still wide open for InfuseAI to target.

You can learn more about Infuse at: https://www.infuseai.io/ 

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