You can always find the most updated version of it on my LinkedIn.

Terrene - Co-Founder & CTO (Techstars IoT '17)

Hello there- I'm Kash (Khashayar) Pourdeilami. I'm currently working on Terrene. Terrene simplifies big data infrastructure by automating the training and deployment of deep learning neural networks. With Terrene, you can load any data from any data sources and run predictive analytics on it.

Let's say I want to load data from a SQL database and model the data inside of it, all I have to achieve that with Terrene is to link my database and specify which columns to use as inputs and labels

from import SQLDatabaseManager
from terrene.transfer import SQLDataManager
from terrene.enrich import PredictiveModelManager

store_manager = SQLDatabaseManager(
    credentials=credentials, workspace=workspace)
transfer = input_dataset_manager.create(
    name="my file", description="training dataset",
    query=query, store=store)
predictive_model_manager = PredictiveModelManager(
    credentials=credentials, workspace=workspace)

query = “SELECT * FROM users;”

store = store_manager.create(
    name="default database",
    description="default warehouse for my workspace")
input_dataset_manager = SQLDataManager(
    credentials=credentials, workspace=workspace)
model = predictive_model_manager.create(
    name="my predictive model", description="predictive model",
    input_variables="col1, col2, col3, col4", output_variables="col5")


The really cool thing about this is that I don't have to do any manual feature engineering. Let's say one my columns in the dataset has values like D56, C32, and etc., Terrene will automatically break that column up into two columns containing the integer and character values from the original column. Another cool thing that Terrene does is that it automatically augment the dataset. (i.e. adding dark spots to images, rotating them, etc.)

One of the big problems a lot of companies currently are facing is around deployment and optimization of their models. Terrene automatically creates REST endpoints to the trained models that can be accessed in Google sheets, Excel, Android, iOS, etc.

Also I can re-train my models on multiple datasets. Lets say my model has made a bunch of predictions and outputted the results in a database, all I have to do is to input the "actual" values into the database and retrain the model = store
endpoint.table = "predictions"

query = “SELECT * FROM predictions;”
new_dataset = input_dataset_manager.create(
    name="my file", description="training dataset",
    query=query, store=store)

model.train(dataset=new_dataset, optimizer={
  "type": "adam", "lr":0.0001})

The newly trained model will immediately be accessible over the REST endpoint without having to redistribute the iOS and Android apps through App Store and Google Play.

Pitstop - Technical Lead (Techstars Mobility '15)

PitStop uses car engine data to predict car engine failures before they happen. As the technical lead, I: 1) Designed and implemented the database and an API to power the Android/iOS/Web apps. 2) Implemented REST endpoints for the ELM327 OBDII scanners to stream real-time car engine data to Pitstop's backend. 3) I lead the iOS, web, and Android development teams to build PitStop's iOS, web, and Android apps.

Full-Stack Developer - PickToRead

Developed a Google Reader alternative called PickToRead which would aggregate news from across the internet and would generate a personalized newsfeed for you based on your prefrences. PickToRead had over 3000 paying members.

Full-Stack Developer - HaigNetwork

Built a social network (web app) for improved communications in a high school with 3000+ students. I also designed the REST API for the mobile apps to communicate with the website. I Used PHP, Twitter Bootstrap, WordPress, BBPress, HTML/CSS and Javascript.


Co-founded and served as an executive member for 900+ member club focused on app development. led group meetings, coordinated guest speaker events and mentored members with app development concepts and best practices.

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