MLOPS : the "Agile" version of Machine Learning
MLOps according to COGNILYTICA
Dear new AI companies, let us introduce you to the MLOps and give you some tips on this cutting-edge technology, to help you save time and money.
Pressured by a high-speed environment to achieve more in less time, today most startups tend to look for the "Agile"-st methods of producing their information and technology services.
These IT industries often have the ambition to integrate Artificial Intelligence into the loop - including Machine Learning, Data Science, and Deep Learning- to engross the jackpot. However, to afford it should not be taken for granted.
To this end, the newcomers find it difficult to find a way to start. On one hand, while trying to implement AI, it is difficult to balance the right organizational architecture, the compatible categories of people to compose the team, and the technologies to employ, with the design of an optimized workflow.
On the other hand, it will be costly to embark and expect to catch up later when everything is already set into place.
So the right thing to do would be to adopt the new process optimization method, known as MLOps, developed for IA industries, earlier in the configuration stage of their ML systems to address each of the problems mentioned above.
Let us see what it is.
MLOps = ML+ DevOps
Etymologically speaking, MLOps stands for ML+Ops, the fusion of Machine Learning processes with the DevOps workflow. Therefore, this could be transcribed as bringing to Machine Learning, all best practices of application development as part of an Agile approach.
DevOps is, as a reminder, not only a methodology but also a movement that was born in the early 2000s. Combining the terms Dev for development and Ops for IT Operations, DevOps revolutionized IT operations and quickly became the norm.
This includes all protocols for automating the software development process, such as building, iterations, versioning, deployment, monitoring, and testing.
Now, concerning MLOPs, it draws its concept from the assumption that running the development of ML models can be considered a form of software development that makes its workflow no different from this latter's usual mechanism.
The real advantage of MLOPs is to be able to aim at large-scale production thanks to the velocity gained in a well-arranged line of work.
Well, it's not far from that, because it has quite similar steps. In fact, as with software development, the repetitive work of ML involves continuous integration (CI), continuous delivery (CD), and, besides, continuous training (CT) to improve models.
What makes MLOps different from "standard" DevOps?
MLOps differs from DevOps in the simple fact that it is typically ML-centered with DevOps principles applied to ML.
That is to say, that all the steps for deploying ML models into production will all be present: data preprocessing, evaluation, training, so on.
All of this will be controlled with the DevOps method.
Professor Ajit Jaokar, a leading scholar in Data Sciences and the Internet of Things, gives us a clear explanation of the subtleties between these two concepts and how to distinguish them which we will try to resume as follows:
- "Experimental nature" MLOps is ready to undergo as many iterations as necessary in terms of "tests and errors" before finding the right set of parameters and obtaining replicable models. There are no possible reliable preset configurations such as in DevOps,
- Updating data profiles impact on the performance change in the model necessarily engenders a change in pipeline.
- Model monitoring: generated models should be tracked in logs to find how it crashed if it comes to happen.
- Automation of pre-build stages: several steps should be automated before running a build
- Team elements: the members may come from disparate fields of proficiency: they may include business people Versus all-Software engineers teams in DevOps
- Evaluating: Apart from the software tests such as unit testing and integration testing, evaluating an ML system implies model validation, model training, and other fine-tuning operations.
How to implement the MLOps?
To implement MLOps should start with a thorough knowledge of what the company wants to acquire. And invest in this direction.
There are a few components to consider to obtain an efficient and synergic system governed by MLOps :
- Organizational architecture and team members: on one hand, we will have the data scientist team and on the other hand the IT, business team. IT practitioners and software development team in charge of all the Ops.
- Technologies involved; testing Elk , container orchestrators such as Kubernetes , continuous integration with Docker or Rancher for managing microservices.
- Design of an optimized workflow
An optimized workflow is schematized here:
Final thoughts
We hope that this article will serve you well as a guide if you want to add ML to your services.
MLOps is a relatively young field to be able to understand and discover its full potential. However, many companies have already found many advantages in integrating it into their production chain. This is why, we encourage AI startups to adopt it. The sooner, the better.
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