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Best French-speaking freelance ML Engineers to hire in 2025

Looking to hire French-speaking ML Engineers for your next project? Browse the world’s best freelance ML Engineers on Contra.

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Ahmed Bendrioua
Morocco
Cover image for YOURKLN-8B - AI Generation Model for In-Depth AI Analysis
Cover image for RECKLN - Advanced Deep Content-Based Movie Recommendation System
Cover image for Text2SQL - natural language queries to sql
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Michael Tawk
Lebanon
Cover image for Predict Remaining Life of Pipe
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FAQs

Understand the basics of machine learning, such as data, algorithms, and model training. Know what specific problem you want the ML engineer to solve. This helps in setting clear goals and expectations for your project.
Start by outlining the main goals and objectives of the project. Describe what success looks like for you and any constraints you might have, like time or resources. Clear scope helps the engineer understand the project and plan accordingly.
Talk about what you expect as the end product of the project. This could include trained models, visualizations, or documentation. Clear deliverables ensure everyone is on the same page about what needs to be achieved.
Discuss with the engineer the time they estimate for each phase of the project. Break down the timeline into smaller milestones. This makes it easier to track progress and stay on schedule.
Include the project's background, objectives, and what you expect as the outcome. Add any details on the available data, tools or platforms to be used. A good brief gives the engineer a clear understanding of the project.
Set regular check-in meetings or updates to discuss the progress. Use tools that help in easy communication and sharing of updates. Good communication helps in addressing any issues quickly.
Ensure they have access to all data and tools required for the project. Arrange for any software or platform subscriptions they might need. Providing resources helps them work efficiently without delays.
Discuss and agree on metrics that are important for measuring the model's success. Metrics could be accuracy, precision, or recall, depending on your project. Clear metrics help in evaluating and improving the model effectively.
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