We have seen What is machine learning operations (MLOps) and why do they matter? . In this post we will cover MLOps Design Principles.
As guiding principles, the MLOps methodology comprises four primary pillars.
From data extraction through model deployment and monitoring, MLOps encourages teams to make everything that goes into creating a machine learning model transparent. It is quite simple for a data scientist to develop machine learning models using Python or R without the help of anybody else in the company. This is excellent for development, but what if you want to put it into production and there isn’t a unified use case? Collaboration must begin on the first day, with a thorough examination of all processes. Permissions and visibility across the organisation will ensure that machine learning models are deployed strategically, with everyone aware of even the most intricate details.
Machine learning should be able to be replicated. Machine learning models are typically created to be one-of-a-kind. The main reason for this is that data is rarely designed in a one-size-fits-all manner. What works for one company may not work for another, as the data will yield different results. Every production model should be able to be audited and replicated by data scientists. Version control for code is standard in software development, but machine learning necessitates more. Most crucially, it entails the versioning of data, settings, and metadata. Storing all model training artefacts guarantees that models can be replicated at any time. The availability of these attributes is critical to the MLOps lifecycle. Reproducibility assures that we’re working with a process rather than a one-off experiment right away.
For efficient MLOps, continuous integration and deployment are essential. This is the procedure for confirming that freshly added code and data are valid before beginning automated development and testing. Machine learning has the potential to enslave data scientists by requiring them to train their models within a known technology stack. When it comes to machine learning, flexibility is essential for developing the best model. The lifecycle of a trained model is totally dependent on the use case and the underlying data’s dynamic nature. Building a fully automated self-healing system may yield decreasing returns depending on your use case, but machine learning should be viewed as a continuous process, and retraining a model should be as simple as possible. Data scientists would waste a lot of time constructing manual and ad hoc models if they didn’t use continuous workflows.
Machine learning, last but not least, should be scaled and monitored. The machine learning lifecycle requires a lot of computing power. Machine learning engineers require an infrastructure layer that enables them to expand their work without requiring them to be networking experts. Data volumes can quickly rise, and data teams need the correct setup to allow them to do so naturally. Engineering methods include monitoring, and machine learning should be no different. The notion of performance in the context of machine learning is not just focused on technical performance such as latency, but also on predictive performance.