We were at the top of the stairs up Beydoun Street in Beirut, named after the Beydoun Mosque at its entrance, and also t

I’m deliberately speaking of decades and not years, though. For example, consider that 90 percent of companies are in the cloud — so many that it’s hard to even think about how our lives would be without it. On the flip side, clouds took several decades to gain widespread adoption.

Connected with the above issue is that there is, as of now, no go-to way of versioning machine learning models. It’s quite obvious that data scientists need to keep track of any changes they make, but that’s quite cumbersome these days.

It’s all the more important that as soon as a project is started, a benchmark is established against which the model runs now and in the future. In combination with diligent version control, data scientists can get their models reproducible.

There’s no reason to believe that the AI revolution should be any different. It will take a while to implement because the status quo contains a host of obstacles to tackle.

Once managers have outlined a clear and simple project, the second step is to choose the right team. It should be cross-functional, and should include data scientists, engineers, DevOps, and any other roles that seem important for its success.

My city exploded on 8.4.2020, with my family, friends and memories right in the middle. I wasn’t in Beirut, I was in Brooklyn. Though I never felt a pain as great as the one felt at that moment, I somehow didn’t cry. At all.

But since machine learning offers so many ways to improve customer experience and corporate efficiency, it’s clear that the winners will be those that deploy models fast and early.

In addition, some pipelines might make use of containerization with Docker and Kubernetes, others might not. Some pipelines will deploy specific APIs, others not. And the list goes on.

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with me till the end! These days I am a GrowthClub member who became a hands-on advisor there. GrowthClub is a community of founders with an average of $5K MRR where founders exchange growth hacks and build genuine connections in 1-on-1 video calls.

Change doesn’t come from one day to the next. For companies who are just getting started in machine learning models, it’s therefore advisable to start with a really small and simple project.

The next decade is bound to be revolutionary — just like the last one was. The widespread adoption of artificial intelligence is only one of many growing trends. The rise of the internet of things, advanced robotics, and blockchain technology count to this list, too.

If 90 percent of a data scientist’s efforts lead to nothing, that’s not a good sign. This isn’t the fault of data scientists, as shown above, but rather due to inherent and organizational obstacles.

Third, managers should consider leveraging third parties to help them accelerate at the beginning. IBM is among the companies that offer such a service, but there are others on the market, too.

But since machine learning offers so many ways to improve customer experience and corporate efficiency, it’s clear that the winners will be those that deploy models fast and early.

A final caveat is not to strive for sophistication at all costs. If a cheap and simple model fulfills 80 percent of customer needs and could be shipped within a couple of months, that’s already a great feat. Moreover, the learnings of building the simple model will fuel the implementation of a more sophisticated model that, hopefully, makes customers 100 percent satisfied.