[Baypiggies] This month's BayPiggies talk: Stock Market ML API with Flask-RESTful AND Keras-Tensor-Flow

[Jeff Fischer]

*Data Science Night: Stock Market ML API with Flask-RESTful AND
Keras-Tensor-Flow*
*Speaker:* Dan Bikle

Thursday August 24, 2017, 7pm to 9pm
Location: LinkedIn, 950 West Maude Ave, Sunnyale, CA
<https://goo.gl/maps/tWfFV7JFb9p>, Unify Conference Room

*Note that we are in a new location, down the street from the old one!*

So we can have an accurate count of people for food, please RSVP on meetup:
https://www.meetup.com/BAyPIGgies/events/239118816/

*Abstract*
This is part 2 of a two part series. Part 1 was presented in early 2016.

In this talk Dan shows you how to build a sophisticated Machine Learning
App from the ground up using Tensor Flow (Wrapped by Keras) and packages in
Anaconda Python.
We start by building a simple API server, with Python package
Flask-RESTful, which responds to GET requests containing parameters which
affect the behavior of a Keras backend server.

We use Keras-Tensor-Flow to create a Deep Learning model and then generate
predictions for any stock symbol of interest. The predictions are affected
by features selected by end-user such as various slopes of price-moving-avg
and/or date features like Day-of-Week and/or Month-of-Year. Building a Deep
Learning model is expensive so we use Postgres to cache each model in case
we encounter another identical request in the near future. Additionally we
cache all predictions which could be generated from each model.
Next in the workflow we use Flask-RESTful to serve predictions to the
end-user as JSON documents.


For deployment, we offer two techniques. Technique 1 is simple. Just deploy
the app to an EC2 server and configure Flask-RESTful to listen on the
public interface. Technique 2 might be more cost-effective. Find some
unused hardware in your office which can run Ubuntu 16. Then deploy the app
to that server. Next, deploy the Flask-RESTful piece to Heroku which should
be free or low cost. Finally, configure both pieces so they communicate
using shared data on a Postgres database hosted at Heroku. Each piece will
connect to Postgres using Python package SQLAlchemy.

Python is awesome technology and it is perfect for building an elegant
Machine Learning Application.

*Speaker Bio *
Dan Bikle is a graduate of Caltech and works as an independent Data
Scientist. He is skilled at extracting knowledge (forecasts mostly) from
data. Also, Dan teaches three classes at Santa Clara Adult Education: Hands
on Python, Time Series Data Science, and Machine Learning Applications.
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