iPepper's Tech Articles


What ?
The "FABRIK" is a team dedicated to carrying out and developing projects to help customers manage their peaks and troughs of activity.
It's a mixed team made up of AI experts, architects, POs, developers, UX/UI specialists and testers, enabling us to pool skills and deliver a highly flexible, responsive service.

Why do you want to do this? 
To solve your problems: "I don't have the time", "I need manpower", "I want to test an idea", "I don't have the skills", "I have discontinuous workloads", "I don't have specifications", "I'd like to concentrate my teams on Build issues, maintenance generates Turn", "I need to justify a senior team", etc.

Who is it for?
Local start-ups and tech companies.

What do we cover?
We cover development, AI and DevOps around Java, .Net, PHP, Ruby, React and Angular technologies.

How do we do it?
You make an appointment with our experts to assess your needs, we give you an answer in principle within 48 hours and are able to start work within 1 week thanks to our in-house team. The project is then easily managed on Trello.

What is the added value of the Fabrik by iPepper?
Responsiveness, expertise and customisation!

Date :

25th August 2023

Subjects :

Flexibility, Development, Expetise


iPepper Group

> MLOps by iPepper: the convergence of AI and operational development

In a world increasingly focused on data and artificial intelligence, new professions are emerging to meet the specific needs of this rapidly expanding field. One of the hottest jobs is that of MLOps, which combines skills in machine learning and operational development. While DevOps has become essential for managing software infrastructures, the MLOps role focuses specifically on the lifecycle of machine learning models, and aims to optimise their deployment, maintenance and performance.

MLOps, which stands for Machine Learning Operations, is an emerging field that aims to apply the principles of software engineering to machine learning models. While the development and deployment of AI models can be complex and subject to specific challenges, MLOps seeks to solve these problems by adopting well-established operational development practices.

While the MLOps and DevOps professions share some common goals, they differ in their specific areas of expertise and responsibilities. While DevOps focuses primarily on the continuous integration, deployment and operation of software in general, MLOps is more focused on the challenges associated with AI and machine learning.

One of the main differences lies in the management of the lifecycle of machine learning models. MLOps focuses on the creation, deployment and maintenance of models, ensuring that they remain high-performing and up-to-date throughout their lifecycle. This involves tasks such as managing model versions, monitoring performance metrics and regularly updating models as new data becomes available.

MLOps is also concerned with the infrastructure specific to machine learning, which often differs from that used for traditional software applications. MLOps professionals need to have an in-depth understanding of computing environments, machine learning frameworks and associated libraries. They work closely with data scientists and machine learning engineers to ensure that the infrastructure meets the specific needs of AI models.

This new technology also focuses on automating data pipelines. This involves orchestrating the various stages of data pre-processing, model training, deployment and performance evaluation. Automating these processes guarantees repeatability and traceability of results, while reducing the potential for human error.

This is a new and fast-growing profession that meets the specific needs of artificial intelligence and machine learning. By merging operational development skills with those of machine learning, MLOps professionals play a key role in the effective management of the lifecycle of AI models, from their creation to their deployment and ongoing maintenance. With the constant evolution of technology, the MLOps profession is becoming increasingly crucial for companies wishing to exploit the full potential of artificial intelligence and machine learning. That's why iPepper is developing a specific offering dedicated to MLOps activities for its customers. For more information, visit info@ipepper.fr.


Date :

10 juillet 2023

Sujets :

IA, DevOps, MLOps

Auteur :

iPepper Group

> AI and Green Code : Towards minimized energy consumption!

During this week dedicated to the environment, it is important for us to bridge the gap between ecological footprint and artificial intelligence. The energy consumption of information and communication technology (ICT) has become a major concern. In fact, by 2040, ICT could account for up to 14% of the total carbon footprint, according to some predictions. Faced with this alarming reality, artificial intelligence and green code (eco-coding) emerge as two promising approaches to reduce our environmental impact. iPepper has made it one of its areas of focus.

AI plays an ambiguous role in energy consumption. On one hand, deep learning algorithms require enormous amounts of data and computational power for training, resulting in significant energy consumption. On the other hand, AI can offer innovative solutions to minimize energy usage in various sectors, including software development.

This is where green code comes into play! Green code, or eco-coding, is a software development approach that emphasizes energy efficiency. It involves writing programs that require fewer hardware resources (CPU, memory, hard disk), thus leading to reduced energy consumption. This practice is based on several principles, including:

  • Code minimization: Concise code is generally more efficient. This involves eliminating redundant code and optimizing program logic.
  • Memory footprint reduction: Accessing memory is energy-intensive. Optimized data structures and better memory control can minimize these costs.
  • Database query optimization: Database operations often consume significant energy. Optimizing queries can greatly reduce their energy cost.
  • Efficient algorithm selection: Some algorithms are more resource-efficient than others. Developers can save energy by choosing the right algorithms for their needs.
And where does artificial intelligence fit in? AI can play a key role in promoting green code. AI tools can be used to analyze codebases and suggest improvements in terms of energy efficiency. These tools could identify redundant code segments, propose more efficient data structures, optimize database queries, and suggest more performant algorithms.

Furthermore, AI can contribute to the design of new eco-coding methods. For example, reinforcement learning could be used to design algorithms that learn to write code in a more eco-efficient manner over time.

In conclusion, AI and green code represent two complementary strategies to reduce the energy consumption of ICT. By combining an eco-coding approach with AI tools, we can hope to create a new generation of environmentally friendly software. The future of computing could be green, and AI will have a key role to play in this transition. iPepper is here to help by offering a comprehensive audit and support to improve your environmental footprint. To learn more, contact greencode@ipepper.fr.


Date :

7th June 2023


IA, green-code, ecology


iPepper Group

> AI and recruitment

Today, we would like to talk to you about the close relationship iPepper has with AI.

Our goal is to make life easier for our amazing recruiters by using AI to find the best candidates. And why not? After all, AI is capable of doing things that humans shouldn't have to do anymore. It can analyze thousands of resumes in minutes, objectively assess candidates' skills, and identify profiles that perfectly match the needs of our impact-driven clients.

In fact, at iPepper, we are so confident in the effectiveness of AI that we have decided to use it for recruiting in our own teams. And how did it go? It's quite simple—our perfect matching AI managed to find the best candidates in no time!

It's so effortless that one could even say our AI is like a professional matchmaker, finding the right person for the right mission. It's as if it can read the candidates' minds and determine if they are a perfect fit for our organization.

Just imagine if this technology were applied in all recruitment processes! No more hours spent sifting through resumes, conducting phone interviews, and organizing recruitment days. With AI, recruiters can dedicate more time to analyzing candidates' social and human skills and identifying our clients' deeper needs.

However, we all know that AI will never completely replace humans. Our recruiters will always be best positioned to assess candidates' soft skills and ensure that our employees' values are respected.

In conclusion, iPepper is a perfect example of how AI can help companies recruit faster and more efficiently. But let's never forget that technology doesn't replace humans; it assists them. So, the next time you're looking for a job, don't be afraid to see AI being part of the recruitment process. It might help you find the job or candidate of your dreams!

And if you ever see a coffee machine with a resume attached to it at iPepper, don't be surprised. It might be their next recruitment move!


4th May 2023


Recruitment, IA, efficiency


iPepper Group

> Artificial Intelligence and machine learning

According to the European Parliament, artificial intelligence (AI) "refers to the ability of a machine to replicate human behaviors such as reasoning, planning, and creativity."

Although the concept has existed for over 60 years, AI has made significant advancements in the past decade due to computing power that is over a trillion times greater than it was 60 years ago. AI is ubiquitous in our daily lives today (chatbots, recommendation engines, Siri/Cortana, etc.). Some machines are even capable of surpassing humans, like AlphaGo, which defeated the Go champion Lee Sedol, or competing with expert ophthalmologists in identifying potential eye diseases. However, we are still far from achieving general AI (or strong AI), which represents the cognitive abilities of a human being to solve unfamiliar tasks. Currently, AI tools are designed for specific problem-solving tasks.

Machine learning is a field of AI that aims to provide machines with the ability to learn from data using mathematical and statistical models. Specifically, it involves determining model parameters through the three stages of "training," "testing," and "validation" before deploying the model in production.

One of the key technologies in machine learning is deep learning. These are algorithms capable of mimicking human brain actions through artificial neural networks. Deep learning models are particularly well-suited for handling large volumes of data and are a major driving force behind the AI boom today.

At iPepper, we closely follow all these technologies and actively contribute to their advancement. Our clients and talents innovate in AI every day, and we take great pride in that. Follow our actions on our iPepper | LinkedIn page or visit www.ipepper.fr.


16th February 2023

Subjetcs :

IA, machine learning, deep learning

Writer :

By Anh Quan NGUYEN, Head IA R&D at iPepper Group

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