Massive organizations have already proven the value that machine learning and artificial intelligence can bring to an organization. It makes programs smarter, and it can give companies the insights needed to dominate their industry. However, integrating machine learning models into a production application is a long and arduous process. It involves taking data to build the model to deploy. Within this pipeline, two different specialties are working together with different tools and workflows. It makes it quite difficult to bridge the gap and create a smooth pipeline that flows easily. This is why MLOps exists. It does the job that DevOps does for machine learning applications. Why use it?
Why Use MLOps
The biggest problem organizations have found is that maintaining your artificial intelligence implementation in your production application is the most difficult process. Eventually, you reach a point where the cost of maintaining your implementation outweighs the value that artificial intelligence and machine learning bring. MLOps provides the tools needed to take your application through every step of the pipeline. This includes providing tools to help you maintain your artificial intelligence application as well. MLOps helps with ingesting, preparing, and processing data. It then helps with the process of training the model and deploying it. There are many components, including data version control and automated training, inference, and deployment.
Benefits MLOps Brings Your Organization
The machine learning development process is many more times complicated than DevOps. MLOps brings them a little bit closer, and it helps machine learning engineers and data scientists automate much of the pipeline. It makes it easier for organizations to justify investing the time and effort needed to add machine learning and artificial intelligence to their applications. It provides the automation of data ingesting, preparation, and feature development. It also makes it easy to build models and continuously improve them as time goes on. There are specific tools around version control that make the process a lot better as well.
Version Control for ML
MLOps provides the data version control technology that organizations need to keep track of their models, code, and data. This is the foundation of a good MLOps system, as it means that engineers can easily see how their models and data have changed over time. It provides the same functionality that Git does for software engineers. These tools provide the functionality to store and version your models. You can catalog your releases and iterate on the entire model development process. It makes it easy for you to fine-tune your models over time, and it does the same thing with your code as well. You can easily see how code has changed, and it makes it easy for you to reproduce certain functionality when you are experimenting.
A Pipeline for Training and Validation of Your Models
Finally, the biggest benefit that MLOps provides is an entire pipeline to continuously train and validate your model. Machine learning isn’t a one-step process. After you have developed the model, it does not end there. You are continuously fine-tuning everything to ensure that the model gets more accurate with new data. That data pipeline needed to go from raw data to a finished model is what MLOps can automate. It provides a tool necessary to change your data, code, and models while keeping track of them at all times.
The benefit to all of these features is you get a better experience developing quality models. It brings more value to your organization, and it puts you on par with companies in your industry that are adopting machine learning for their applications.