dimanche 19 mai 2024

Hard and soft skills of IA generative jobs by ChatGPT (GPT-4o) [mai 2024]

ChatGPT (GPT-4o) reveals the list of jobs in an IA Generative team [1] as well as hard and soft skills. 

The "Language" competency features : 

"Pre-training lead","Post-training lead", "Architecture lead","Optimization lead","Long-context lead","Pre-training Data lead","Human data lead",(...)


Pre-training lead

"a Pre-training Lead focuses on the critical phase of preparing data and models before they undergo intensive training. 

The pre-training phase is crucial because the quality and relevance of the data fed into the model significantly impact its final performance. The Pre-training Lead ensures that the data is meticulously prepared and that the initial conditions for training are optimal. This role bridges the gap between raw data and the model training phase, providing a strong foundation for successful AI model development.

By focusing on these preparatory tasks, the Pre-training Lead plays a pivotal role in the overall success of AI projects, ensuring that models are trained on high-quality, well-prepared data, leading to better performance and more reliable outcomes.


    Data Collection and Curation:
        Expertise in sourcing and aggregating large datasets.

    Domain Expertise:
        Deep understanding of the specific domain for which the AI model is being trained.
    Machine Learning and AI Knowledge:

    Project Management:
        Strong organizational and project management skills.
        Ability to coordinate with cross-functional teams, including data engineers, data scientists, and software developers.

    Analytical Skills:
Skills in using tools and languages such as Python, R, SQL, and data visualization tools.

    Data Preparation:
        Leading efforts to collect, clean, and preprocess the data needed for model training.

    Model Initialization:
        Overseeing the selection and initialization of model architectures.

    Coordination and Collaboration:
        Collaborating with data engineers to ensure the infrastructure supports large-scale data handling.
        Working closely with data scientists to refine data and model requirements.
        Coordinating with ethical and compliance experts to ensure data privacy and ethicalconsiderations are met.

    Quality Assurance:
        Implementing procedures for continuous monitoring and validation of data quality.
        Conducting preliminary training runs to validate data suitability and model performance.

    Documentation and Reporting

        Reporting progress and findings to stakeholders and team members.

        Continuously seeking ways to improve data processing efficiency and effectiveness.

Source : GPT-4o ; prompt :  Tru Do-Khac

[1] OpenAI
Nouveaux métiers de IA selon ChatGPT (GPT-4o), Gouvernance numérique de l'entreprise créative, mai 2024
Générique ChatGPT  (GPT-4o), X-Propriété-Intellectuelle, mai 2024
Hard and soft skills of IA generative  jobs by ChatGPT (GPT-4o), X-SoftSkills, mai 2024