Enterprises today are transforming their businesses using Machine Learning (ML) to develop a lasting competitive advantage. From healthcare to transportation, supply chain to risk management, machine learning is becoming pervasive across industries, disrupting markets and reshaping business models. Organizations need the technology and tools required to build and deploy successful Machine Learning models and operate in an agile way. MLOps is the key to making machine learning projects successful at scale. It is the practice of collaboration between data science and IT teams designed to accelerate the entire machine lifecycle across model development, deployment, monitoring, and more.
Businesses increasingly solve complex problems with data science. Access to very large data sets, accelerated advances in ML research fields, and inexpensive computing power are driving an AI-fueled transformation across industries. In a crowded market where consumers can have anything at any time, ML/AI applications that prevent fraud, mitigate churn, serve product suggestions in real-time, and manage predictive maintenance on infrastructure can be the critical differentiator. Yet as AI/ML projects come into the mainstream, businesses are finding just how hard it is to go from data science to business value. It's dangerous for any company to think of these AI-driven wins as coming for free.
January is the customary time to make predictions on what the year holds in store. Working in partnership with companies across multiple industries that are looking to develop data science and AI skills in their workforce, I have a good vantage point on the trends that are developing across the realm of technology. In addition, I have published recent research with colleagues at Cambridge University about the challenges that face organizations with deploying machine learning. From this perspective, there is a clear picture forming that 2021 will be a turning point within leading businesses for making a priority of operationalizing AI. In fact, the second half of 2020 has seen a new crop of tools, platforms and startups receiving investment to provide solutions to this difficult problem.
"What good is an ML model if it isn't fast? Having machine learning in a company's portfolio used to be an investor magnet. Now, the market is bullish on MLaaS, with a new breed of companies offering machine learning services (libraries/APIs/frameworks) to help other companies get their job done better and faster. According to PwC, AI's potential global economic impact will be worth $15.7 trillion by 2030. And, as interests slowly shift towards MLOps, it is possible that these companies, which promise to scale and accelerate ML deployment, might grab a bigger piece of the pie. Last week, OctoML raised $28 million. The Seattle-based startup offers a machine learning acceleration platform built on top of the open-source Apache TVM compiler framework project. The $28 million Series B funding brings the company's total funding to $47 million. For OctoML's CEO, Luis Ceze, there is still a significant gap between building a model and making it production-ready. Between rapidly evolving ML models, wrote Ceze in a blog post, ML frameworks and a Cambrian explosion of hardware backends makes ML deployment challenging. "It is not easy to make sure your model runs fast enough and to benchmark it across different deployment hardware.
Enterprise AI service provider DataRobot has unveiled MLOps, a machine learning operations (MLOps) solution for deploying, monitoring, and managing machine learning models across the enterprise. MLOps combines DataRobot's existing model management and monitoring solution with capabilities from MLOps category leader ParallelM, which DataRobot acquired in June. DataRobot's new MLOps offering provides a centralised hub for deployment, monitoring, and governance of models created from a variety of tools. As a result, organisations will be able to cut the time it takes them to deploy and scale machine learning-based services in production. Despite the investments in data science teams and infrastructure, many companies have not been able to derive measurable value from AI projects.