RNCP CERTIFICATION

Certification AI Project Manager

Overview

The "AI Project Manager" certification, offered by Albert School, is awarded by Ascencia. It is a Level 7 certification with NSF codes 326t and 326p, and is registered in the National Directory of Professional Certifications (RNCP) under number 36129, by decision of France Compétences dated January 26, 2022.

Certification Objectives

The "AI Project Manager" certification aims to train students to lead artificial intelligence projects. The program is designed to equip students with project management skills in AI, covering competencies such as project management, solution development and integration, team management, risk management, and adherence to ethics and regulations. This certification meets the growing demand in a rapidly evolving sector, where new technologies play a key role. It was created based on three main factors: the increase in available data, advancements in powerful computing via GPUs, and the development of advanced algorithms such as convolutional neural networks. Companies need to effectively integrate AI into their processes, which requires mastering both technical and organizational aspects. The AI project manager is essential for bridging these technological and organizational sides, combining business management and technical skills. This profile is rare in the market, but demand is high.

Competency Blocks

The academic program of the certification is organized into four main competency blocks, listed and detailed below:

  • Develop an AI Solution Using Design Thinking
    To identify AI projects, it is crucial to follow international innovations, especially in Machine Learning, Deep Learning, and neural networks. This involves gathering stakeholder needs to align innovations with business requirements of a company or sector. It is also important to determine solutions that facilitate the use of these projects, considering regulations, to ensure accessibility, particularly for people with disabilities. Workshops can be organized to generate new ideas based on these innovations, using collaborative tools and educating a non-technical audience on machine learning concepts. The collected ideas should be formalized by assessing their business relevance and technical feasibility, then selected based on budgetary and logistical constraints. Project management includes transitioning the solution from alpha to beta and then to an acceptable version, improving algorithms. Finally, the implementation of a limited solution should be overseen using prototypes and testing different models to analyze results and make recommendations.
  • Manage an AI Project
    To develop the specifications for an AI project, it is essential to define and plan activities, identify risks, and propose actions to mitigate them. A budget should be established considering the cost of necessary human and technical resources, while respecting financial constraints. Project team management, including recruiting key profiles like data scientists and software engineers, is crucial to ensure smooth project execution. It is also important to select and unify providers to ensure project success. Implementing an additional training plan for the team is essential to develop their skills in chosen technologies. Finally, collaborating on the preparation of training materials for users and effectively communicating on results and challenges informs stakeholders and facilitates change.
  • Develop an AI Solution (Machine and Deep Learning)
    Data processing involves using Data Mining and Data Analysis techniques, implementing a massive data collection strategy from exploitable databases or methods such as Mechanical Turk. The collected data is then aggregated with technical tools to apply relevant statistical models and make recommendations. Data modeling consists of transforming, normalizing, and structuring data from various sources (text, image, sound) to ensure its quality, relevance, and optimize its storage and processing. To design an AI model, it is necessary to develop the design of IT architecture, define performance objectives, and select suitable algorithms, whether supervised or unsupervised. Model optimization involves interpreting initial results, checking the quality of predictive models with test scenarios, and verifying the reliability of algorithms against expectations to improve performance based on evaluations.
  • Deploy an AI Solution
    To define an effective user experience, it is important to design a simple and accessible interface by organizing design workshops, prototyping, and testing, to optimize functional and graphic aspects. Ensuring GDPR compliance is essential by establishing a data collection policy aligned with regulations and company values, while monitoring relevant regulatory developments for the sector. Cybersecurity should be prioritized to prevent intrusions and misuse of data, protecting the integrity and authenticity of personal data with appropriate techniques. It is also crucial to measure the impact of AI on the environment, society, and the individual, and to develop ethical and collaborative solutions. Finally, it is necessary to present the technological challenges of the solution to a non-specialist audience, proposing applications in related fields to showcase and promote AI to various organizational stakeholders.

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    Prerequisites

    We seek curious and determined minds.
    To join our program, you must hold a Level 6 degree or RNCP title, or 180 ECTS credits. Submit your application and convince us in a motivational interview.

    More specifically, entry prerequisites are:

    • The candidate must hold a Level 6 degree or RNCP title, or 180 ECTS credits.
    • A candidate without the aforementioned degree or title, but with over two years of relevant professional experience, may be admitted. In such cases, a request must be sent to the certifier, who has the authority to approve the application.

    Admission is based on application, tests, and a motivational interview.

    Jobs / Sectors Targeted by the Certification

    AI job offers mainly come from consulting firms and data science solution providers, as well as their clients. In France, several reports identify key sectors where AI plays a major role. These sectors are heavily impacted by digitalization and AI.

    Some examples include:

    • In financial services, AI optimizes predictive market analysis, credit operations, and enhances customer experience via chatbots.
    • In legal services, AI facilitates document search, contract management, and legal operations monitoring.
    • Retail uses AI to personalize customer experience and optimize operations.
    • Industry benefits from robotics and predictive maintenance.
    • In healthcare, AI plays a crucial role in diagnosis, treatment, and preventive medicine. These sectors illustrate how AI transforms and energizes the economy.

    Job titles associated with the AI Project Manager role vary in the job market, including, for example:

    • AI Project Manager
    • AI Engineer
    • AI Project Director
    • AI Team Manager
    • AI Expert
    • AI Consultant
    • Machine Learning Project Manager etc.

    Evaluation Methods

    We measure your success with case studies, simulations, and oral and written exams. This is how we ensure that you are ready to face tomorrow’s challenges.

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    Start your journey

    Are you ready to apply for the AI Project Manager Certification and develop your skills? Just send an email to admission@albertschool.com including your CV and a motivational letter to express your interest. If your application is successful, you will be invited for an online interview to complete the process.