Compared to software engineering, machine learning engineering is a relatively new field. Software engineering has DevOps as a set of tools and processes to automate functions that would be the bane of every programmer’s existence. Since machine learning is such a new field, these same tools and processes are not there yet for this field. However, MLOps is seeking to change that. It is more than just tools, as it is a set of processes and methodologies for developing applications with integrated machine learning models. To make machine learning worthwhile, organizations need a way to integrate it without investing a lot of time, effort, and money.
Why MLOps Is Needed
MLOps provides the tools necessary to operationalize machine learning and engineering. That is to say; it provides the tools to automate the process of going from raw data and a hypothesis to a finished model in your software. In DevOps, you have continuous integration and deployment. These tools provide everything necessary to automate deployment and testing. MLOps does this, and the benefits to organizations are numerous. The first thing is that it allows organizations to integrate machine learning into their applications at a low cost. It also speeds up the time that data scientists and machine learning engineers would spend cleaning up data. Previously, data scientists and engineers spent most of their time cleaning up data to be used for model building. MLOps means that this is no longer the case.
The 411 On MLOps
The application of DevOps practices machine learning and artificial intelligence is MLOps. The name of the game is to automate as many things as possible. In effect, you create a smooth pipeline from data to a finished model. It reduces the cost of maintaining your AI application, and this is usually where most companies spend their money in this field. It also makes it easy to continuously refine your machine learning model. Companies don’t just create a model and stop there. They usually refine and tune the model to be as accurate as possible. Having these tools available allows them to achieve this goal quite easily. It essentially creates a machine learning optimized CI/CD pipeline.
Why Your Organization Should Pay Attention to MLOps
Many people in an organization should care about MLOps. If you are an engineer or data scientist, you should care because it will make your job a lot easier. It will automate the majority of the time you would spend wrangling data to be used for modeling. If you are an executive, it could provide the competitive edge you need to take over your industry. It provides benefits from top to bottom in your organization, and it makes the job of everyone much easier.
With that being said, the most important thing you need to understand is that MLOps is not just tools. It is also a process and a methodology for achieving your goal. It is about creating systems to automate all aspects of your data science pipeline, and the organizations that can master these practices will dominate in the future.