Machine learning: how to industrialize your models?

Currently, a multitude of machine learning models are in the making or in the testing phase. Yet few of them will actually be used. Industrialization and the deployment of models is a real challenge that is too often overlooked. In this article, we return to the basic principles to be followed to give the best chance of emancipation to your machine learning model.


What is machine learning?

Many people confuse artificial intelligence with machine learning. Nevertheless, these are two different things.


The name artificial intelligence represents the concept that a machine would be able to perform tasks autonomously and intelligently. The good grail for AI is to create systems that can use their own thinking mode to perform any task. Until now, most of the existing artificial intelligence systems are more focused on one type of skill at a time, we're talking about applied artificial intelligence. The classic example is that of trading AI which aims to dominate the vagaries of financial markets.


Machine learning, on the other hand, is only one aspect of artificial intelligence that assumes that machines must be able to learn on their own, as long as they are provided with enough data. While many see machine learning as a subset of artificial intelligence, others believe that machine learning is a step towards the development of an omnipotent artificial intelligence.  Machine learning therefore refers to a mode of learning. The foundation of machine learning is the principle that machines must be able to learn independently and use, for example, a deduction process, just like humans.

Multilateral approach to deploying a machine learning model

Machine learning models are on the rise. Companies are promised cost rationalization, increased efficiency or effective marketing; all to improve the organization and of  course  the returns. So much for the theory.


In practice, most of these models are never industrialized and therefore end up in oblivion.


A company that wants to use the potential of AI and of machine learning must take the time to develop the models but must also deploy them afterwards. This implies cooperation at different levels of the company and a global vision from the creation of the model.


Indeed, to develop a model without adopting a multilateral approach and involving different players within the company is a bit like writing a bestseller and not finding the right publisher. The work will remain in your boxes and will never be known to the public.


Efficiently develop its machine learning model

Although brilliant, many models of machine learning prove useless for companies. Before developing a model, it is therefore essential to understand the why of the model and the needs of the company.


The following questions are key:

  • What are the needs in terms of availability? In other words, should the model be available on demand?  Who will use it and for what purpose?
  • What are the needs in terms of speed? Does the information have to be provided in real time or is an interval of several hours or even several days acceptable?
  • What are the cost constraints, also in terms of infrastructure (storage and other)? What is the budget allocated by the company? How are we going to maintain the model long term? How can we organize updates and audits on its reliability? Is it scalable?


Although a model that produces results in real time (usually through an API) is often considered ideal (it is called on-demand model), it is not always necessary. In some cases, it is therefore important to make certain "aesthetic" concessions so as not to lose sight of the objectives of profitability and efficiency. Models based on manual mode or batch mode (by batch) can be very suitable for a number of practical applications. On the other hand, sophisticated models capable of producing results in real time with a suitable edge architecture are needed in certain scenarios.


In any case, a model will never be deployed properly if it does not meet cross-organizational acceptance within the company. This also involves important communication efforts to explain the benefits of the model.

The difficult transition to the big bath

Industrializing a machine learning model is a bit like teaching a child to go from paddling to a big bath. This is essential to learn how to swim properly. For thrill seekers, you can compare this to a solo parachute jump. During your training, you will be given all the keys to complete your jump in the best conditions and react to the unexpected. However, once you have jumped into the void, the success of your jump will be your sole responsibility.


The truth is that training will never be perfect. No one is immune from an accident or an error of judgment. This is also the case for a machine learning model. So you have to accept a certain amount of risk, but it is essential to control that risk. It is therefore important to put in place warning signals in the event of "abnormal" results, "atypical" data, and regular checks and updates. We’re talking about integration or continuous industrialization.


At this point, a question is asked: aren’t these machine learning models adding an unnecessary degree of complexity? When we measure the magnitude of the different parameters to be considered, migraine is on the horizon. However, these models are essential in many cases where computing power is needed to achieve an adequate result. So we come back to the starting point: the importance of understanding the needs before embarking on the  development  of machine learning models at all costs. 

The importance of proper data selection

One factor that is too often overlooked is data sources. Machine learning models use the data streams available. If these flows are inadequate, the result will never be correct. Imagine using Bamako weather sensor data to determine the weather in Paris and you'll have an idea of the influence of a poor selection of data on the performance of a machine learning model.


The effective industrialization of a machine learning model must therefore respect certain principles:

  • Adequate dentification of needs and costs;
  • Communication and cross-learning efforts;
  • Acceptation of the fallible character of the machine learning model;
  • Identification of these flaws and regular updates and checks;
  • Appropriate election of the data used.


At Ryax, our platform aims to industrialize your algorithms and machine learning models faster. In this way, you do not lose sight of the essentials, in this case to add value to the business project.

If you want a demonstration of our platform or if you want more information, please contact us.

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La Ryax Team.