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Operationalizing machine learning is one of the final stages before deploying and running an ML model in a production environment.
You could think of it as a transition phase, in between the development and training stages, which take place in a training environment using cleaned data, and the deployment and management stages, when the model runs in one of many different business application environments using messy, real-world data.
Sometimes, operationalization is referred to as “o16n,” because the word is just too long for the fast-paced tech world.
Operationalizing machine learning is a complex process that encompasses a number of other tasks, including:
The operationalization of machine learning models is usually carried out by operations (DevOps) or MLOps teams, as opposed to the data science and data engineering teams who are responsible for developing and training the model.
Machine learning models can bring a lot of value to enterprises across every vertical, but the business can only actualize that value once they operationalize the machine learning model.
One of the challenges in machine learning projects is that the data science teams could end up in a silo of pure ML modeling.
They may produce many beautiful models, but the business won’t enjoy any benefit from them until they are operationalized, deployed in a business application, and begin analyzing data and producing predictions.
When data scientists set out to develop ML models, they generally have no idea in what context it will be deployed. Each model is developed in the vacuum of a training environment. It needs to be operationalized to prepare it to work in a business application, analytical platform, etc., after deployment.
Data science and operations teams work in different ways
The teams who operationalize a machine learning model often use different tools, concepts, and tech stacks to the data scientists who trained the models. It’s not easy to bridge this gulf, so it can be a struggle to transition from training to operationalizing environments.
ML models may not accommodate real-world issues
Data scientists focus on data science. They may not always consider other issues which impact on the way that ML models are deployed in the real world, such as legal, compliance, IT ops, or data architecture restrictions that can require changes to the way the model operates, but ops teams need to accommodate them without affecting the efficacy of the model.
A machine learning platform can create end-to-end machine learning pipelines that gather and process data, train models, and operationalize and deploy models, instead of siloing each step in separate departments.
Automate data pipelines
Automated machine learning pipelines can gather and process relevant data, monitor model performance, find new training datasets, and more. By automating these workflows, you’ll free up time for MLOps teams, data scientists, analysts, and data engineers to focus on other tasks.
When the entire process takes place on a single platform, it eases communication, coordination, and collaboration between your data science and analyst teams and your ops teams, supporting faster, smoother operationalization and deployment without too many hiccups or false starts.
Generate business value
Once you’ve operationalized your ML models, you can enjoy business value from them, using them to drive better business decision-making, assess risk, spot opportunities, and more across every department of the business. The faster you can reach this sweet spot, the more benefit you’ll enjoy from your investment in data science and AI.