Talk @ Big Data AI Paris, 26/09/2022

1. Reinventing Deep Learning
with Hugging Face Transformers
Julien Simon, Chief Evangelist, Hugging Face
julsimon@huggingface.co

2. 2022: Transformers are eating Deep Learning
BERT
ROBERTA
GPT-2
GPT-3
VISION TRANSFORMER
WAV2VEC2
CLIP
SEGFORMER
STABLE DIFFUSION
"Transformers are emerging as a general-purpose architecture for ML"
https://www.stateof.ai/
RNN and CNN usage down, Transformers usage up!
https://www.kaggle.com/kaggle-survey-2021
RNN
CNN
LSTM

3. Transformer models in the wild

4. Neural networks
Building
large
datasets
GPUs
Expert
tools
Deep Learning: how it started

5. Deep Learning: how it's going
87% of data science
projects never
make it into produc
ti
on
https://venturebeat.com/2019/07/19/
why-do-87-of-data-science-projects-
never-make-it-into-production
Only 25% of
companies report
widespread adop
ti
on
https://www.pwc.com/us/en/tech-
effect/ai-analytics/ai-
predictions.html

6. Transformers
Transfer
Learning
ML Hardware
Developer
tools
Deep Learning 2.0

7. 7
Hugging Face: one of the fastest-growing open source projects
https://github.com/huggingface/transformers/

8. The Hugging Face Hub: The Github of Machine Learning
73,000+ models
10,000+ datasets
10,000+ organiza
ti
ons
100,000+ users
1M+ downloads daily
h
tt
ps://huggingface.co

9. Example: zero-shot classification

10. Machine Learning Hardware
• A new generation of chips designed for ML workloads
• Faster training
• 6-hour training 2x faster? Several iterations in a single day!
• Faster prediction
• Lower latency, increased throughput
• Better cost-performance ratio
• Hugging Face partners with hardware innovators
https://github.com/huggingface/optimum
•

11. Developer Tools
Model in
production
10,000+ datasets
on the hub
73,000+ models
on the hub
No-code AutoML
HW-accelerated
managed API
HW-accelerated
inference
Hosted ML
applications
HW-accelerated training
Amazon SageMaker
Optimum
Datasets
Models
Hugging Face Endpoints
for Azure
Transformers
Accelerate
Optimum

12. Demos

13. Summing things up
• ML is complicated because we love to make it complicated
• Make sure your teams focus on the right things
1. Define business goals and KPIs
2. Save time with pre-trained models and transfer learning
3. Don't reinvent tools, platform, infrastructure
4. Collaborate on models and datasets

14. Learn more about Hugging Face and Transformers
https://huggingface.co/tasks
https://huggingface.co/course
https://github.com/huggingface
Hugging Face Endpoints launch event (27/9): https://shorturl.at/gJQSX
Learn more about our solutions
https://huggingface.co/support - Expert Acceleration Program, from modeling to production
https://huggingface.co/platform - Private Hub, running on your infrastructure (on prem or cloud)
Getting started Stay in touch!
@julsimon
julsimon.medium.com
youtube.com/c/juliensimonfr