Martin Gorner
Director Of Product Management AI ML at Axelera AI
Lyon, Auvergne-Rhône-Alpes, France
Summary
🤩
Rockstar🎓
Top School
Top expert inGoogle Cloud Platform Development Essentials
Martin Gorner is an experienced AI/ML product leader and engineer with 11+ years building developer-facing ML platforms and tooling, currently directing Product Management for AI/ML at Axelera AI from Lyon. He spent much of the last decade at Google shaping Keras, TPUs, and developer experiences—launching TPUs on Kaggle and co-authoring an O’Reilly book on practical computer vision—then briefly contributed to Hugging Face’s engineering efforts. Martin combines hands-on model engineering (notable contributions to TensorFlow tutorials and practical ML repos, including RNN and MNIST examples) with product strategy, making complex ML infrastructure accessible to practitioners. His background spans deep technical roots (early work in processor simulation and mobile e‑publishing) to leadership roles at Amazon and Google, giving him a rare mix of systems-level engineering and product empathy. An understated strength is his consistent focus on developer productivity—turning research-grade models into usable, served products.
11 years of coding experience
10 years of employment as a software developer
Diplôme d'ingénieur civil, Diplôme d'ingénieur civil at Mines Paris - PSL
Stackoverflow
13reputation
2kreached
2answers
1question
Github Skills (38)
continuous-deployment10
convolutional-neural-networks10
batch-normalization10
python10
data-science10
image-classification10
model-driven10
parameter-tuning10
machine-learning10
rnn-model10
xception10
n10
ml-deployment10
linear-regression10
model-building10
Programming languages (5)
Github contributions (5)
GoogleCloudPlatform/tensorflow-without-a-phd
Sep 2017 - Jun 2020
A crash course in six episodes for software developers who want to become machine learning practitioners.
Role in this project:
ML Engineer
ML EngineerContributions:289 commits, 11 PRs, 196 pushes in 2 years 9 months
Contributions summary:Martin implemented the initial version of an export function and prediction mechanism. They also modified the model to include serving input functions, allowing for the model to be used for online predictions. The user demonstrated familiarity with the trainer module, and model architecture, specifically the training data input functions, and the model definition within the context of TensorFlow and Keras for a machine learning project. The user also introduced various hyperparameters.
practitionerspythoncrash-coursedata-sciencedeep-learning
martin-gorner/tensorflow-mnist-tutorial
May 2016 - May 2018
Sample code for "Tensorflow and deep learning, without a PhD" presentation and code lab.
Role in this project:ML Engineer
ML EngineerContributions:133 commits, 15 PRs, 93 pushes in 2 years
Contributions summary:Martin primarily contributed to the development and refinement of a convolutional neural network (CNN) for the MNIST handwritten digit recognition task. Their work involved implementing and modifying the model architecture, including experimenting with different layer configurations, such as the number of convolutional layers, fully connected layers, and dropout layers. The user's contributions aimed at improving the model's accuracy and overall performance on the MNIST dataset through hyperparameter tuning and model adjustments.
presentationdeep-learningtensorflowphdlab
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.