According to Gartner, a whopping 47% of machine learning experiments fail to reach experimentation. This number is stunning on the surface, but it is even more troubling when you consider the deluge of demands the MLOps workforce is facing in the wake of the COVID-19 pandemic. With the MLOps market size set to balloon to over $6 billion by 2028 – up from $612 million in 2021 – businesses are expecting their MLOps pros to churn out products from their pipelines at a record pace. The problem is that the tools pros have at their disposal continue to undermine their efficiency, product time-to-market, and ultimately the entire business’s revenue.
Plagued by having to rely on a broken ecosystem of fragmented point solutions that inhibit productivity, MLOps teams are desperately searching for ways to streamline their workflows and drive more business value. Thus, with constricting budgets, rising expectations, and a growing frustration with piecemeal solutions, MLOps pros are intently searching for a way to redefine the status quo. Enter open source.
Open-source technology has been a godsend for many areas of DevOps – which is why it is set to experience a 17% CAGR until 2027 and hit $60 billion in market value. However, within MLOps specifically, several key factors are contributing to a burgeoning groundswell of popularity among this community as well.
With that, here is a refresh on why open source is looking so appealing to the MLOps industry.
Maturing Market
While open source may be well-established in other branches of DevOps, the market is far less mature within MLOps. For example, until recently the open source MLOps market has consisted almost exclusively of point solutions. Moreover, many of the open-source solutions that now exist for MLOps have been repurposed from other DevOps sectors. These two factors have combined to create a workflow that is incredibly fragmented and frustrating and provides suboptimal and clunky performance. And naturally, this has become a major thorn in the sides of MLOps success, value, and revenue generation.
However, as MLOps has become more widely adopted and expectations have grown, the market has matured to a size where workforces feel empowered to demand that open-source platforms are the central pillar of their “MLOps toolbelt.” Thus, more robust and native open-source MLOps solutions are beginning to appear on the market to capitalize on this interest.
The Demands for Customization
As the MLOps market has matured, the desire for customization has naturally grown among MLOps teams. But these ambitions have been stunted by the fact that the point solutions – even ones that are labeled “open source” – aren’t really open source. Instead, while these solutions feature perhaps one open-source element, they more accurately fall into the closed-source bucket.
This presents a bevy of issues for MLOps professionals. The first of which is that the closed nature of these platforms makes it incredibly difficult for them to build the unique tools that they need to work their own way. And secondly, it inhibits a company’s ability to adopt automation within their organization in a way that is frictionless and works seamlessly with their already-established infrastructure.
This has catalyzed a push among the MLOps community to begin to look for platforms that are as robust in terms of customization functionality as the solutions that can be found in other areas in the broader DevOps space. And again, the MLOps open-source industry is beginning to respond to this interest by unveiling tools that provide both an end-to-end solution and modular functionality for MLOps teams so that they can have the fit-for-purpose workflow that they need.
MLOps professionals are looking to do more with less friction. Having grown tired of the traditional ways of doing business, they are looking for the flexibility and purpose-built functionality they need to drive success. With that, the MLOps space is likely to be dominated in the years ahead by solutions providers that use open source to the truest and fullest extent.