Online Magazine
Businesses all over the world see the benefits that data, artificial intelligence (AI) and machine learning (ML) can have. However, getting there is everything but easy.
In a 2019 Accenture survey, 84 percent of executives noted that they would not achieve their growth objectives without scaling AI. To do so, companies can benefit from a so-called ML platform. However, in today’s crowded vendor landscape, making an informed decision (‘build vs. buy’) on a platform for scaling AI can be tricky.
Thus, this article aims at providing a starting point for you to engage with the topic of ML platforms. It first explains what an ML platform is and why your organization needs one and then continues with an overview of ML platforms in the market and how you can find the one that suits your organization best.
In many areas of research and industry, artificial intelligence and machine learning are becoming increasingly popular for problem solving. While a huge effort is made to further improve the performance of ML models, the true bottleneck has shifted towards moving such ML solutions out of the labs and into production.
As ML gains foothold across organizations, teams developing ML solutions are struggling with the complexities surrounding the ML lifecycle. As a result, solutions that help to manage the end-to-end ML lifecycle are in high demand. Not surprisingly, cloud providers are offering native services to support different aspects of the ML lifecycle and an increasing number of software providers are expanding their offerings in this direction. Additionally, more and more start-ups are pushing innovative solutions and ML services to the market.
So, what is a ML platform?
A ML platform is defined as a collection of services that covers steps encompassing the end-to-end ML lifecycle and helps organizations continuously develop, deploy, integrate, and monitor their AI and ML solutions.
ML models are often developed in a very complex, even incomprehensible, way. Particularly, the complexity of ML solutions and their development process is due to the following factors:
The solutions developed in such a way generate little to no value and business impact. To unleash their full potential and generate value continuously, they need to be taken out of the experimental phase and integrated deeply within the business processes of the organization. However, this is impossible without certain components such as
Only a comprehensive set of ML components – meaning a ML platform – enables this transition to the next phase of the ML lifecycle, in which the carefully trained and selected ML solution makes it out of the lab into the real world. After all, you would not want your models to belong to the astounding 90 percent which never see the light of the day.
In theory, everyone can build their own ML platform. That is what many of the tech giants have done, e.g., Uber (Michelangelo), Airbnb (Bighead), Facebook (FBLearner), Netflix (Metaflow) and Apple (Overton) to name a few. However, while this might be possible for organizations with huge engineering teams, it is not a feasible approach for the majority of organizations. To overcome this hurdle and bridge the gap, enterprise ML platforms increasingly provide an answer.
To cope with the complexities of managing the ML lifecycle, more and more ML teams are looking towards PaaS solutions. Several vendors and cloud providers are offering end-to-end ML platforms and/or services, including AWS (Amazon Web Services), Microsoft Azure, Google Cloud Platform, Databricks, Dataiku, H2O.ai, and several others.
Cloud providers offer several possibilities to effectively support most, if not all, components of the ML lifecycle and provide flexibility, but at the cost of relatively high cloud engineering effort. On the other hand, proprietary solutions like Databricks, Dataiku and Domino offer out-of-the-box services encompassing the ML lifecycle which may simplify your ML journey at a certain cost of flexibility.
There is no shortage of choices regarding ML platforms in the market but the natural question any ML team or organization starting out may ask is – which one is the best one? We can safely assume that there is no ‘one ML platform to rule them all’. Or in other words: there is no such thing as the best ML platform. No matter how good a product or service provider claims to be, there will be competitors with a similar offering portfolio. Instead, the right question to ask is:
Which ML platform would be best suited for my organization?
The choice of a ML platform depends on three things:
After considering all of the above, to help find the most suitable ML platform for your organization, a ML capability framework is required which helps you assess all the above mentioned aspects consistently across several platforms. Making the right decision is especially important because long-term vendor lock-in can be costly.