CIOs realize data is the new currency. But, if you can’t use your data as a differentiator to gain new insights, develop new products and services, enter new markets, and better meet the needs of existing ones, you’re not fully monetizing your data. That’s why building and deploying artificial intelligence (AI) and machine learning (ML) models into a production environment quickly and efficiently is so critical.
Yet many enterprises are struggling to accomplish this goal. To better understand why, let’s look back at what has stalled AI in the past and what continues to challenge today’s enterprises.
Yesterday’s challenge: Lack of power, storage, and data
AI and ML have been around far longer than many companies realize, but until recently, businesses couldn’t really put those technologies to use. That’s because companies didn’t have sufficient computing power, storage capabilities, or enough data to make an investment in developing ML and AI models worthwhile.
In the last two decades though, computing power has dramatically increased. Coupled with the advent of the Internet and the development of new technologies such as IPv6, VOIP, IoT, and 5G, companies are suddenly awash in more data than ever before. Gigabytes, terabytes, and even petabytes of data are now being created daily, making vast volumes of data readily available. Combined with increases in storage technologies, the main limitations to using AI and ML models are now problems of the past.
Today’s challenge: Model building is complicated
Due to the removal of those constraints, companies have been able to show the promise of AI and ML models in areas such as improving medical diagnoses, developing sophisticated weather models, controlling self-driving cars, and operating complex equipment. Without question, in those data-intensive realms, the return from and impact of those models has been astonishing.
However, the initial results from those high-profile examples have shown that while AI and ML models can work effectively, companies without the large IT budgets required for the development of AI and ML models may not be able to take full advantage of them. The barrier to success has become the complex process of AI and ML model development. The challenge, therefore, becomes not whether a company should use AI and ML, but rather, can they build and use AI and ML models in an affordable, efficient, scalable, and sustainable way?
The reality is that most companies don’t have the tools or processes in place to effectively allow them to build, train, deploy, and test AI and ML models. And then repeat the process again and again. For AI and ML models to be scalable, consistency over time is important.
To really use AI and ML models to their fullest, as well as reap their benefits, companies must find ways to operationalize the model development processes. Those processes must also be repeatable and scalable to eliminate creating unique solutions for each individual use case (which is another challenge to the use of AI and ML models today). The one-off mentality of use case creation is not financially sustainable, especially when developing AI and ML models, nor is it a model that drives business success.
In other words, they need a framework. Fortunately, there’s a solution.
The Solution: ML Ops
Over the last few years, the discipline known as machine learning operations, or ML Ops, has emerged as the best way for enterprises to manage the challenges involved with developing and deploying AI and ML models. ML Ops is focused on the processes involved in developing an AI or ML model (developing, training, testing, etc.), the hand-offs between the various teams involved in model development and deployment, the data used in the model itself, and how to automate these processes to make them scalable and repeatable.
ML Ops solutions help the enterprise focus on governance and regulatory requirements, provide increased automation, and increase the quality of the production model. An ML Ops solution also provides the framework necessary to eliminate having to create new processes every time a model is developed—making it repeatable, reliable, scalable, and efficient. In addition to the benefits listed, many ML Ops solutions may also provide integrated tools, so developers can easily and repeatedly build and deploy AI and ML models.
ML Ops solutions lets enterprises develop and deploy those AI and ML models systematically and affordably.
How HPE can help
HPE’s machine learning operations solution, HPE Ezmeral ML Ops, addresses the challenges of operationalizing AI and ML models at enterprise scale by providing DevOps-like speed and agility, combined with an open-source platform that delivers a cloud-like experience. It also includes pre-packaged tools to operationalize the ML lifecycle from pilot to production and supports every stage of the ML lifecycle. These include data preparation, model build, model training, model deployment, collaboration, and monitoring—with capabilities that enable users to run all their machine learning tasks on a single unified platform.
HPE Ezmeral ML Ops provides enterprises with an end-to-end data science solution that has the flexibility to run on premises, in multiple public clouds, or in a hybrid model. It’s able to respond to dynamic business requirements in a variety of use cases, speeds up data model timelines, and helps reduce time to market.
To learn more about HPE Ezmeral ML Ops and how it can help your business, visit hpe.com/mlops or contact your local sales rep.
About Richard Hatheway
Richard Hatheway is a technology industry veteran with more than 20 years of experience in multiple industries, including computers, oil and gas, energy, smart grid, cyber security, networking and telecommunications. At Hewlett Packard Enterprise, Richard focuses on GTM activities for HPE Ezmeral Software.