The articles set is a summary of the workshop I gave at Europython2019.
The main motivation is to show how to conduct data science and machine learning projects and give a hands-on walk through on how to build and integrate machine learning service into software product with microservice oriented architecture.
You may find these articles useful, if you are software engineer working on machine learning project, or data scientist/machine learning engineer/data engineer considering ways to improve your work flow and your models delivery, or project manager trying to connect the dots and improve data product delivery process, or executive manager working towards employing data science and machine learning in your organisation.
The set…
Many data professionals are strict on the language to be used for ANN models limiting their dev. environment exclusively to Python. I decided to test performance of Python vs. R in terms of time required to train a convolutional neural network based model for image recognition. As the starting point, I took the blog post by Dr. Shirin Elsinghorst on how easy it is to build a CNN model in R using Keras.
A few words about Keras. It is a Python library for artificial neural network ML models which provides high level fronted to various deep learning frameworks with Tensorflow being the default one.
Keras has many pros with the following among the others:
- Easy to build complex models just in few lines of code => perfect for dev. cycle to quickly experiment and check your ideas
- Code recycling: one can easily swap the backend framework (let’s say from CNTK to Tensorflow or vice versa) => DRY principle
- Seamless use of GPU => perfect for fast model tuning and experimenting
Dmitry Kisler
A husband, data scientist/engineer/team lead, XP practitioner, consultant and ML enthusiast with physics background. My passion is data driven products.
