There’s a lot of potential in Machine Learning (ML). Unfortunately, there are also a number of obstacles companies hit when it comes to realizing that potential.
During a panel at last summer’s Transform 2019 conference, it was pointed out that nearly 90% of ML models cooked up by data scientists never actually make it into production.
Why is this the case? Why are one in ten ML projects doomed to failure? There are a couple of reasons.
One is that the technology is new, and most IT organizations are simply unfamiliar with the software tools and specialized hardware, such as Nvidia GPUs, that are required to effectively deploy ML models.
The other reason is the disconnect between IT and data science. IT tends to stay focused on making things available and stable. They want uptime at all costs. Data scientists, on the other hand, are focused on iteration and experimentation. They want to break things.
Originally developed by Google, Kubeflow is an open-source project designed to facilitate the end-to-end process of developing and deploying ML models.
Kubeflow sits atop Kubernetes in your development workflow, providing data scientists with a self-service playground to conduct ML model experiments. Then, once those experiments are completed, it packages the model up and publishes it in a way that can be used by production systems.
Right now, Kubeflow is still in its infancy—in fact, its 1.0 release is targeted for early 2020. But some companies have already put it to work, and so far the results are promising.
But while Kubeflow is a tool that can potentially solve the ML deployment problem, it won’t be of much use unless companies looking to unlock the potential of ML address their biggest hurdle: not understanding their technical maturity.
Enter the Technical Maturity Framework
Effectively putting ML to work means understanding a number of technical variables, including from the outset, things like:
- Where your data is currently located
- Whether that data is clean
- What data you need from elsewhere in order to drive your ML process
- Where your ML workloads will be running
Getting to the bottom of these and other variables requires using a technical maturity framework. Without knowing whether you are even ready to utilize ML, you’re going to go nowhere fast.
The Redapt Technical Maturity Framework will help you nail down exactly what your business is trying to achieve, and whether actually using something like ML makes sense in the first place.
So what’s the solution to the ML model problem?
There’s no one solution for the logjam between data science ML models and production—at least not yet. But there are steps that can improve the percentage of models making it to deployment.
One of those steps is Kubeflow, which is tailor made for making it easier to deploy ML across the board.
The other step—the one every company looking to utilize ML can take right now—is to thoroughly assess their technical maturity.
That way, they can get a handle on their current capabilities, learn whether ML is even something they need to pursue, and pinpoint ways to bridge the gap between how data scientists dream up ML models and IT puts those models to work.
If you’re interested in learning more about ML and assessing the technical maturity of your business, download our free eBook The Redapt Technical Maturity Framework.
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