The Pipeline of Turning AI Into a Production Application
One of the many challenges that modern businesses have with machine learning and artificial intelligence is putting those technologies into production applications. Operationalizing AI is now one of the major challenges that corporations have to contend with. The main reason for this is that there is not a clear-cut path that takes companies from idea to production application with AI. The industry is currently very young, and there are a few open-source applications that need to be tied together to make it all work. Companies see the need for a systematic way of using AI to create a production artificial intelligence application.
Operationalizing AI or “AI Ops”
One of the many challenges that companies have with machine learning and artificial intelligence is finding a streamlined process that will enable them to go from idea to finished production-ready application. We have a process for software engineering and a field called DevOps. We now need something similar to what software engineering has, but it will operationalize artificial intelligence and machine learning. This MLOps field will need the same tools and processes, but it will have to work differently. This is because turning a data science idea into a machine learning application involves two different types of professionals. These professionals use fundamentally different tools, and it is one of the many stumbling blocks to making operationalized AI reality.
Figuring Out the Scope of the Project
The first thing that every business has to contend with is how to figure out what the end goal is. This is because there’s no point in putting machine learning and artificial intelligence into an application if it makes no difference to your business needs. This is why business KPIs are one of the first things that you have to think about before you even get started. After that happens, you can then let your data scientists understand what they need to accomplish with this project. It is up to the data scientists to figure out the entire scope of the project based on this end goal. The KPIs the business selects will be what determines what direction the models go in. It is one of the many reasons why it is crucial to make the right choice at this phase.
The next at the data scientist has to go through is to find the right data sets to help get the accurate business KPIs that was defined in the scoping of the project. It is one of the many areas where there needs to be more in terms of MLOps, as the job of the data scientists does is still not completely connected to the machine learning engineer that will start putting the models built in this step to good use. Data is the foundation of machine learning, and data scientists will need a better way of gathering, collecting, and organizing all the necessary data to build accurate models in this field.
Selecting the Proper Algorithms
After data gathering, data scientists also needed to use tools to start choosing and running algorithms on this data. It is usually the step where they build the models that will need to be tested and refined before production.
Building and Managing Models
One of the many challenges of operationalizing AI with AI ops is that there isn’t an industry-standard way of providing version control for models. In software engineering, there are tools like Git that can manage multiple people working on the same codebase and seeing that codebase from ideation to deployed application. There needs to be something like this in machine learning to make it all work well.
Managing Models in Production
Finally, getting a model into production and keep it there is a difficult step. There has to be compliance and performance as the two significant factors in deciding how the model deployment will be. There’s also the fact that the model has to be accurate and reach the goals that the business KPIs have set in the past.
The MLOps is slowly maturing, but it will take more time and effort to operationalize machine learning and artificial intelligence.