As ML matures from research to applied business solutions, so do we need to improve the maturity of its operation processes.
MLOps is communication between data scientists and the operations or production team. It’s deeply collaborative in nature, designed to eliminate waste, automate as much as possible, and produce richer, more consistent insights with machine learning. ML can be a game changer for a business, but without some form of systemization, it can devolve into a science experiment. MLOps brings business interest back to the forefront of your ML operations. Data scientists work through the lens of organizational interest with clear direction and measurable benchmarks. It’s the best of both worlds!
Let us now begin this article to take a deep dive into what is DevOps, its features and architecture. Main Technology/Tools Used:
Contrary to what you may think, MLOps allows your data scientists freedom to do what they do best — find answers. Take business decisions off their plates, and they can build and deploy models that get your insights more quickly. Think about it. You didn’t hire your data team to understand the ins and outs of your industry. You didn’t hire them to keep up with regulation. You hired them for their skills in information gleaning. Remove the barriers and let them find your answers. MLOps follows a similar pattern to DevOps. The practices that drive a seamless integration between your development cycle and your overall operations process can also transform how your organization handles big data. Just like DevOps shortens production life cycles by creating better products with each iteration, MLOps drives insights you can trust and put into play more quickly.
However, MLOps is challenging for the following reasons:
- Tight coupling between the data and the model
- Managing Data , code and model versioning
- Silos - Data engineers, data scientists, and the engineers responsible for delivery operate in silos which creates friction between teams
- Skills - Data scientists are not often trained engineers and thus do not always follow good DevOps practices
- No easy way to identify model drift and trigger a pipeline for retraining the model
- Too many manual steps. No Automation
- Difficulty in migrating ML workloads from local environments to the cloud
Solving these challenges will require ML engineers to leverage a robust platform capable of incorporating CI/CD principles into the ML lifecycle, thus achieving true MLOps.
CI/CD helps to accelerate and improve the efficiency of workflows while shortening the time it takes data scientists to experiment, develop, and deploy models into production for real business applications.
By definition, a well-implemented MLOps process should achieve continuous development and delivery (CI/CD) for data and ML intensive applications. However, an effective CI/CD system is vital to this process. Not only should it understand ML elements natively but it also must stay in sync with any changes to underlying data or code, irrespective of the platform on which the model runs.
ML engineers looking to truly automate ML pipelines need a way to natively enable continuous integration of machine learning models to production
Project completed under LinuxWorld Informatics Ltd. - MLOps Training.
+ Vedant Shrivastava | [email protected]