Did you know that a machine learning model (ML model) helped produce 92 percent accuracy when learning to predict the mortality of COVID-19 patients?
ML models serve a greater purpose in a lot of industries than many might realize, and they're a great tool to put into practice. How do you go about putting one into operation, though? What happens if you don't operationalize your model before you start using it?
We're here to take you through the basics. Read on to learn everything you need to know about operationalizing a machine learning model.
You might already know that machine learning consists of software using algorithms to make a classification or prediction. This is relatively simple to understand, but there's a reason you have to operationalize it. While it's great to have beautiful models available, they won't really be of use until they're deployed for a specific business application.
Oftentimes, data scientists are creating ML models based on nothing more than design, so until it's fully operational, you really have no way of knowing whether or not it'll work for what you need it for.
In fact Bitstrapped specialized in what we call MLOps, or machine learning operations. So our core model is to operationalize machine learning.
So, you can deploy as many models as you'd like, but until it's analyzing data and producing predictions, they're not going to be of much use for your business.
There are four main steps to MLOPs or operationalization of machine learning: build, manage, deploy, monitor. Let's take a deeper dive into each one.
The first step is to identify the data you're attempting to collect, and then work to build an analytics pipeline to make tracking easier. This can be done through predictive models, ML algorithms, and other built-in features that help to optimize the process.
What data scientists build, however, needs to stand up to the speed needed for real-time production environments, and they need to be usable for other teams, workers, and anyone else who might need to access it.
The point of an effective ML model is to make tracking processes easier, yet it's easy to complicate them.
That's why tracking everything in a way that makes it all easy to manage is important. There are a few key features that management needs to cover:
Management strategies need to be easy-to-follow, but still effective enough to keep track of necessities. This involves countless experiments, along with different parameters, hyperparameters, optimizers, and even loss functions.
They're necessary to track things like development, training, versioning, and deployment of models, and are best viewed as something to pair alongside other steps rather than as its own process.
For deployment, the pipeline you've been working to build is taken from its original development environment and turned into something applicable to business environments. This is typically done by integrating the ML model into existing business applications and software.
This step typically requires rigorous testing and reformulating to ensure everything runs smoothly, and that it's going to integrate well with day-to-day operations.
If things go wrong here, it can mean you have to go back to the drawing board. But if things go well and everything runs smoothly, then you get to move on to the next step.
Though this step is the last, it might also be the most important. Without tracking things you can't control, like data, your ML system isn't as likely to work. Monitoring is going to help you stay up-to-date with prediction performance and service updates, both of which are important to any model.
When you stay on top of your model, any changes you need to make are that much easier.
Different teams work in different ways, and bridging the gap between the teams operationalizing the model vs. the data scientists who created it can be challenging.
You also have to remain vigilant of your ML models. They're not always created for real-world scenarios, and you have to be sure they're targeting and accommodating the issues you're looking to tackle.
To combat these things, you can hire teams to work together from the beginning or you can employ a team capable of making the required changes so your model is operational without compromising its overall efficacy.
While there are challenges, there are also plenty of benefits to making your ML model operational.
For starters, you're creating a pipeline program instead of a silo for each department. When you have silos, it becomes more difficult to track things and harder to actually achieve a goal. When you have a pipeline, it becomes easier to gather and process relevant data, find new training datasets, and keep track of the model's performance.
That pipeline then helps automate processes and learning. When you automate these processes, you're freeing up a lot more time for MLOPs teams and other departments alike.
It also becomes easier for those teams to communicate, coordinate, and collaborate. Any relevant info becomes available on one platform instead of multiple, meaning the entire process speeds up and hiccups are easier to avoid.
Now that we've gone over the basics of operationalizing your machine learning model, it's time for you to get started with MLOps. No matter what you're looking to create, there's a model and a method out there for you.
If you're looking for a company with a track record of proven success, then turning to Bitstrapped is your answer. Our models come with Google Cloud certified architects and ongoing Cloud platform management. Check out our MLOps services today for more information.
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