Daiyu Hatakeyama
Principal Technical Architect at マイクロソフト
Saitama Prefecture, Japan
Summary
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Senior🎓
Top SchoolDaiyu Hatakeyama is a Principal Technical Architect at Microsoft with roughly 25 years of engineering and architecture experience delivering cloud, media, search, and AI solutions for more than 300 enterprises across Japan and internationally. He blends hands-on prototyping and workshop-led product discovery—having run 100+ hackathons and hands-on sessions—with strategic customer engagements like envisioning and architecture design at Microsoft Innovation Hub. His technical focus spans LLMs, video streaming, full-text search, databases, and ML/DL, and he contributes to Microsoft training materials by tuning model training configurations and experimenting with base models. A frequent keynote and breakout speaker and published author, he actively channels field learnings back into the community via books, blogs, sample code, and partner-led product initiatives. Based in Saitama, he also collaborates with 50+ partners (including Toshiba, SONY, and NTT-Data) to bring new market solutions to life, combining deep technical craft with practical delivery at scale.
11 years of coding experience
14 years of employment as a software developer
社会科学学士, 社会科学, 社会科学学士, 社会科学 at 早稲田大学 WASEDA University
English, Japanese
Github Skills (10)
computer-vision10
machine-learning10
deep-learning10
tensorflow10
trainings10
python10
modeling10
azure-machine-learning9
resnet8
mobilenet8
Programming languages (7)
Github contributions (5)
microsoft/ignite-learning-paths-training-aiml
Dec 2019 - Dec 2019
Microsoft Ignite Learning Path, Train the Trainer materials: Developers Guide to AI
Role in this project:
ML Engineer
ML EngineerContributions:14 commits, 1 PR in 1 day
Contributions summary:Daiyu primarily focused on modifying machine learning model training configurations. Their contributions involved adjusting the batch size used during training, switching between different pre-trained base models (MobileNetV2, ResNet50, and VGG19) within the `train.py` file, and making minor cleanups to the training code. Additionally, they also modified the `seer_pipeline.ipynb` to reflect the changes. These changes suggest an effort to experiment and optimize the model's performance.
ai
dahatake/Azure-Media-Services-Samples
Jun 2015 - Oct 2018
Contributions:28 commits, 26 pushes, 1 branch in 3 years 4 months
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