How will MLOps streamline your AI projects?

When speaking of Artificial Intelligence, the efficiency and profitability of projects depend on the ability of companies to deploy reliable applications quickly and at low cost. To succeed, you need to organize and improve the processes for creating, implementing, and maintaining AI models with a diverse and sizable team.



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Linear modes of operation, where each person completes his task before passing the project on to the next, are no longer viable! A paradigm shift is needed in AI design, inspired by agile project development methodologies. Models are increasingly complex, data is bigger and the stakes are higher. In this context, linear and risky development methods are no longer sufficient. 

AI application linear workflow that rarely allows the application to be tested in real conditions

It is essential to move to an iterative mode, where model development is divided into short, regular cycles, thereby optimizing results and reducing risks.

Anyone wishing to build AI applications must therefore take this new approach into account. The implementation of MLOps practices helps to break down this wall of AI industrialisation where 80% of projects crash.

By integrating the MLOps method from the start of an AI project, companies can benefit from efficient project management that reduces costs, increases project gains, improves model quality, simplifies processes, reduces the risk of failure, and unites all teams involved. These advantages offered by the MLOps method contribute to the success of AI exploitation.


How to create your MLOps Workflow ? 

Here is a beginners guide to better understand what is needed for a MLOps stack.


Agile AI

Every AI project starts in the hands of a Data Scientist. However, a DS may be an expert in developing AI models, but they now need the expertise of ML Engineers, DevOps or developers to ensure the success of the project. Putting AI into production, integrating it with production information systems and scaling it up doesn't depend on one department only, but on project management between the different teams involved. This can lead to a considerable increase in initial project costs and complex project management.

In this respect, MLOps provides the necessary tools and practices to conduct the deployment of your AI in project mode: 

The development of models in a dedicated structured and managed infrastructure allows them to be tested in real conditions from the very first iterations. This allows you to integrate all the teams involved in the project as early as possible, from the development phase. You can then deploy the prototypes very early on and serve the results via API to the functional teams.

By testing the solution early on in the prototyping phase using the API service and in actual usage scenarios, not only can the quality of the models be improved, but it also enables the collection of valuable feedback from end users right from the start of the project.

The business teams are involved in the development of the solution, which leads to a better match between the development work and the real needs and creates a virtuous circle of involvement of everyone in the project: collection of much more feedback because the functional prototype is available earlier. Users have something concrete in their hands and can see its evolution.

Finally, the collaborative work and iterative project management that MLOps allows, reduces the risks linked to the integration of AI models in the company's technical stack.

An iterative workflow allowing AI applications to be tested in real conditions and its integration to be un-risked

Growth & savings

The second major contribution of MLOps to companies wishing to exploit AI is to enable economies of scale and immediate financial gains thanks to the industrialization of AI projects at scale and in record time.

MLOps ROI calculator : test here

Cost reduction 

The cost reduction enabled by MLOps is based on the optimization of two resources. One is expensive and scarce, the other is recent and poorly optimized.

The first resource is human. It can be optimized in two ways with MLOps: 

  • Thanks to the iterative operation in project mode, facilitating inter-team collaboration. This leads to a sharp reduction in the number of man-days of Data Scientists, DevOps, ML Engineer and Data Engineer required. 
  • Deploying your models as structured pipelines increases replicability. Breaking down your models into containerized steps, or blocks of code, not only increases their replicability but also saves you valuable time in terms of data scientist, data engineer, ML engineer, and DevOps man-days.

The second resource to optimize is the machine. MLOps, by allowing easy deployment from the experimentation phase, gives a unique visibility on the power and storage needs of your AI projects. 

This visibility becomes Cloud Resource Optimisation (FinOps) and enables infrastructure reductions of up to 20% in cost.

Increased profits

Optimizing your AI workflows not only reduces your operating costs, it also has a direct positive impact on your revenues: by reducing the average deployment time of an AI application from 6 months to a few minutes, you gain 6 months of additional revenues for a lower production cost. 

Finally, the level of experiment tracking and monitoring in production that MLOps allows greatly increases the quality of your models and therefore directly improves the performance of your AI projects.

MLOps means a faster, more reliable financial gain with a higher margin!

Simplicity of deployment and ease of use

The last great advantage of integrating the MLOps method from the start of an AI project is the ease of work offered by a coherent stack or an end-to-end platform. This will save time in onboarding the different actors of the project, will make each iteration more fluid, and will facilitate internal collaboration thanks to intelligent management of access to the project, versioning and experiment tracking tools.
In addition, the high reproducibility of pipelines saves time and efficiency by avoiding duplication of tasks. This improves the quality of projects by allowing easier maintenance and regular updating of models. 

Finally, the MLOps method allows for better adoption by business teams as results are available earlier and more transparently.
End-users have access to the results more quickly, which increases their confidence and satisfaction in the project.
Integrating the MLOps method from the beginning of an AI project creates an optimal working environment.


Efficient project management by reducing costs, increased profits by reducing the time of your roadmaps, improved model quality, simplified processes, reduced risks, internal adoption up to business teams, better collaboration between different teams: by integrating the MLOps method from the beginning of an AI project, companies can benefit from all these advantages and succeed in exploiting AI.

To learn more about integrating MLOps into your AI innovation projects, request a demo of Craft AI's MLOps platform.

Written by Hélen d'Argentré

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