How to enable warp speed for Machine Learning modeling Machine Learning frameworks undeniably went through a mind-blowing evolution over the last 4 years. The multiple iterations created various trends, one of which has put a bright spotlight on the importance of the framework’s interface. Developers want the flexibility and transparency of low-level APIs in their work with high-level APIs. TensorFlow 2.0, Keras and PyTorch are making steps to solve this nontrivial puzzle, however wrapping the low-level APIs makes it tedious to write boilerplate code for visualizations of different variables during runtime. We are introducing a unique flexible ML platform that gives developers full transparency into the process of machine learning models' development, and enables fast model iteration.

1. How to enable warp speed for
Machine Learning modeling
Martin Isaksson | Robert Lundberg

2. History of the framework interface
TensorFlow PerceptiLabs
(initial concept)
TensorFlow v1.0
PerceptiLabs
(company founded)
TensorFlow v1.X TensorFlow v2.0
2015 2016 2017 2018 2019

3. 2016
Hackathon
ML model that tracks
fish swarms from
satellite images

4. 2016
Hackathon
Fight for Arctic

5. Lessons learned
Building a model is a long process.
After building a few models, we realized that
we needed to write boilerplate code to better
understand, debug, and test the model.

6. Platform
benefits
Fast modeling
Increased transparency
Flexibility

7. Fast
modeling
Drag and drop
Instant feedback
I/O shape fitting

8. Increased
transparency
Visualizations
High level abstraction
Low-level code

9. Flexibility
Custom environment
Dataflow driven
Custom edit code

11. Try our platform
perceptilabs.com/download