Jingtian Peng
Co-Founder & CTO at Pinlan
Huangpu District, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolJingtian Peng is a co-founder and CTO with a decade of engineering experience building cloud-native AI platforms and production ML pipelines, currently leading R&D at Pinlan where he applies AI to architecture design. A Google Developer Expert and active open-source maintainer, he contributes to Kubeflow and TensorFlow and helped improve the official Kubernetes Python client, bridging model development with deployment. He led engineering at Caicloud to ship a Kubeflow-based data-model-service product and earlier worked on distributed TensorFlow and GPU acceleration at Huawei. Author of two bestselling TensorFlow books used for practical coursework, he combines deep hands-on ML engineering with developer advocacy and community-driven tooling. Based in Shanghai, he pairs academic training from Zhejiang University and UC San Diego with entrepreneurial grit to move GenAI projects from prototypes into scalable services.
10 years of coding experience
3 years of employment as a software developer
University of California San DiegoBachelor of Engineering (B.Eng.)(Top 5%), Computer Science, Bachelor of Engineering (B.Eng.)(Top 5%), Computer Science at Zhejiang University
Chinese, English
Github Skills (16)
neural-network10
python-client10
kubernetes10
machine-learning10
deploying10
tensorflow10
py10
python10
mnist10
k8s10
kubernetes-pods10
standard-library10
apidoc9
api8
dockers5
Programming languages (13)
Github contributions (5)
DjangoPeng/tensorflow-101
Jan 2019 - Jan 2023
《TensorFlow 快速入门与实战》和《TensorFlow 2 项目进阶实战》课程代码与课件
Role in this project:
ML Engineer
ML EngineerContributions:62 commits, 2 PRs, 49 pushes in 4 years
Contributions summary:Jingtian added notebook examples demonstrating the implementation of a multi-layer perceptron model using TensorFlow for MNIST dataset classification. These examples include code for data loading, model definition, loss function, optimizer, and training loop. The commits also show examples of using variables, saving and restoring models, indicating a focus on practical TensorFlow implementation and model training.
tensorflow-2tensorflowmachine-learningpython
kubernetes-client/python
Sep 2017 - Oct 2017
Official Python client library for kubernetes
Role in this project:Back-end Developer
Back-end DeveloperContributions:6 commits, 1 PR, 26 comments in 19 days
Contributions summary:Jingtian primarily contributed to the official Python client library for Kubernetes, focusing on creating and modifying example deployments. Their work involved writing Python code using the `kubernetes-client/python` library to create, update, roll back, and delete Kubernetes deployments, demonstrating proficiency in interacting with the Kubernetes API. They also implemented PEP8 style updates and fixed syntax errors, improving the code's readability and maintainability.
pythonclient-librarypython-clientk8skubernetes
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