DFW Full Stack Data Science

Building Scalable Predictive Data Pipelines.
Data DevOps way of realizing the Data Pipelines.

We care about trends, tools, languages, platforms, practices, patterns, frameworks that aid in solution-ing, designing, architect-ing, developing, implementing, deploying, maintaining scalable Predictive Data Pipelines.

The list will only give a glimpse of areas we care about, but the list can go really long..
Ingestion, Cleansing, EDA, Feature Engineering, Statistical Analysis, Model Building, Auto Training/Re-Training, Model Performance monitoring, Dash-boarding Model Predictions, Pattern Recognition and so on.

Crunching variety of data kinds and building Pipelines around -
tabular, semi-structured, time-series, streams, images, audio/speech, video, graphData, text, geoData etc

This is open to any data enthusiasts.

First meeting will be an introduction and setting the expectations for this meetup.
The moderator will pick a topic for the next meetup based on majority vote. Members are requested to come with topics of interest.

Example Topics can be as follows-

Kubernetes based Data Processing/ML frameworks.
Building a basic Fraud Detection DataPipeline on Stream Data.
Best Practices in building a scalable Data Pipelines.
Building a Fault tolerant Data Pipeline Stages.
Raise of Functional Languages/Functional Constructs for Distributed Systems.
Building a text summarization pipeline.
Product-ionizing a deep learning model.
Picking a language for Predictive Pipelines.
Building Docker Images for ML solutions.

Meetup will be more discussion-based rather than instruction-based because of sheer vastness of the area we are indulging in.

We can do demos, discussions, teaching, compete/debate on solution-ing/architect-ing Data Pipelines. Any new ideas are appreciated.