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With 2022 well behind us, taking stock in how machine learning (ML) has evolved as a discipline, technology and industry is critical. With AI and ML spend expected to continue to grow, companies are seeking ways to optimize rising investments and ensure value, especially in the face of a challenging macroeconomic environment.

With that in mind, how will organizations invest more efficiently while maximizing MLs impact? How will big techs austerity pivot influence how ML is practiced, deployed, and executed moving forward? Here are 5 ML trends to expect in 2023.

Although we saw plenty of top technology companies announce layoffs in the latter half of 2022, its likely none of these companies are laying off their most talented ML personnel. However, to fill the void of fewer people on deeply technical teams, companies will have to lean even further into automation to keep productivity up and ensure projects reach completion. We expect to also see companies that use ML technology implement more systems to monitor and govern performance and make more data-driven decisions on managing ML or data science teams. With clearly defined goals, technical teams will have to be more KPI-centric so that leadership can have a more in-depth understanding of MLs ROI. Gone are the days of ambiguous benchmarks for ML.

Recent layoffs, specifically for those working with ML, are likely the most recent hires as opposed to the more long-term staff that have been working with ML for years. Since ML and AI have become more common in the last decade, many big tech companies have begun hiring these types of workers because they could handle the financial cost and keep them away from competitors not necessarily because they were needed. From this perspective, its not surprising to see so many ML workers being laid off, considering the surplus within larger companies. However, as the era of ML talent hoarding ends, it could usher in a new wave of innovation and opportunity. With so much talent now looking for work, we will likely see many folks trickle out of big tech and into small and medium-sized businesses or startups.

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Looking at ML projects in progress, teams will have to be far more efficient given the recent layoffs and look towards automation to help projects move forward. Other teams will need to develop more structure and determine deadlines to ensure projects are completed effectively. Different business units will have to begin communicating more improving collaboration and sharing knowledge so that smaller teams can act as one cohesive unit.

In addition, teams will also have to prioritize which types of projects they need to work on to make the most impact in a short period of time. I see ML projects boiled down to two types: sellable features that leadership believes will increase sales and win against the competition; and revenue optimization projects that directly impact revenue. Sellable feature projects will likely be postponed as theyre hard to get out quickly. Instead, now-smaller ML teams will focus more on revenue optimization as it can drive real revenue. Performance, in this moment, is essential for all business units and ML isnt immune to that.

Its clear that next year, MLOps teams that specifically focus on ML operations, management, and governance, will have to do more with less. Because of this, businesses will adopt more off-the-shelf solutions because they are less expensive to produce, require less research time, and can be customized to fit most needs.

MLOps teams will also need to consider open-source infrastructure instead of getting locked into long-term contracts with cloud providers. While organizations using ML at hyperscale can certainly benefit from integrating with their cloud providers, it forces these companies to work the way the provider wants them to work. At the end of the day, you might not be able to do what you want, the way you want, and I cant think of anyone who actually relishes that predicament.

Also, you are at the mercy of the cloud provider for cost increases and upgrades, and you will suffer if you are running experiments on local machines. On the other hand, open source delivers flexible customization, cost savings, and efficiency and you can even modify open-source code yourself to ensure that it works exactly the way you want. Especially with teams shrinking across tech, this is becoming a much more viable option.

One of the factors slowing down MLOps adoption is the plethora of point solutions. Thats not to say that they dont work, but that they might not integrate well together and leave gaps in a workflow. Because of that, I firmly believe that 2023 will be the year the industry moves towards unified, end-to-end platforms built from modules that can be used individually and also integrate seamlessly with each other (as well as integrate easily with other products).

This kind of platform approach, with the flexibility of individual components, delivers the kind of agile experience that todays specialists are looking for. Its easier than purchasing point products and patching them together; its faster than building your own infrastructure from scratch (when you should be using that time to build models). Therefore, it saves both time and labor not to mention that this approach can be far more cost-effective. Theres no need to suffer with point products when unified solutions exist.

In a potentially challenging 2023, the ML category is due for continued change. It will get smarter and more efficient. As organizations talk about austerity, expect to see the above trends take center stage and influence the direction of the industry in the new year.

Moses Guttmann is CEO and cofounder of ClearML.

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5 ways machine learning must evolve in a difficult 2023 - VentureBeat