AI News, Using data science to build better products
- On Sunday, June 3, 2018
- By Read More
Using data science to build better products
There are competitions to build the best predictive algorithms, tons of data blogs/tutorials, and a number of fast-growing (and hugely successful) professional education platforms for teaching data science skills (Insight Data Science, Zipfian Academy, General Assembly, Coursera, Udacity).
Data science has even taken a seat at the big kids table, with many of the most prestigious colleges and universities now offering undergrad and graduate level degrees in data science.
Companies of all industries and sizes are finding new ways to use data to streamline processes, reach audiences more effectively, and build more useful, personal, and customized products, services, and experiences.
From the most exciting new startups to the biggest and most timeless brands, companies must now communicate and serve customers in an omnichannel world where the bar for superior customer experience is raised everyday.
Making sure that data insights are useful to people who don't think about machine learning all day is super important, since the beneficiary of data science work is often a front-line employee, a customer/user or another non-technical stakeholder.
Raw byproducts of data science (scripts, plots, code, prose, pickle files, whatever else) are interesting in an academic sort of way, but there's nothing more motivating than Spotify Radio serving up 10 winners in a row or using iTranslate's English-to-Finnish interpretor feature to communicate w/ an airbnb host successfully (really...magic).
Yhat ScienceOps is designed to tackle the challenges involved in integrating data scientists' work into apps used by employees and customers quickly, reliably, and without much (sometimes any) coding beyond your team's existing R and Python scripts.
Let's layout our feature/engineering requirements before we get rolling: 1: User should be able to enter data into fields detailing a home's characteristics 2: The web server should take the users inputs and send them to the prediction server (ScienceOps) 3: The prediction server should predict a price with the 'HouseValuePredictor' algorithm (detailed above) 4: The web server should then get passed the predicted price and return it to the user Sounds simple enough!
2: The web server should take the users inputs and send them to the prediction server (Yhat ScienceOps) 3: The prediction server should predict a price with the 'HouseValuePredictor' algorithm (detailed above) 4: The web server should then get passed the predicted price and return it to the user We have to capture the form data and send it to the server.
- On Sunday, August 18, 2019
Deploy and Manage R and Python Models | Yhat Webinar Feb 2016
Yhat (pronounced Y-hat) provides and end-to-end data science platform for developing, deploying, and managing real-time decision APIs. In this webinar, Yhat ...
Deploy R and Python Models | Yhat Fintech Use Case + ScienceOps Demo | Dec 2015
Charlie Hecht, Director of Sales & Customer Development and Colin Ristig, Product Manager discuss "Real Time Decision Making & Predictive Analytics" using ...
Применение языка R в корпоративной среде — Максим Брегеда
Чаще всего мы используем язык R для научных расчётов в университетах, для анализа данных на соревнованиях...
DevOps for data science | BRK3277
Most data science discussions start with everything in place, data loaded, and ready to be analyzed. But in the real world, it's hardly that simple. There are many ...
Deploy from RStudio to Azure
DISCLAIMER** There have been several changes made to AML since this video was created over a year ago and there are going to significant changes in the ...
Productionalization of Analytical R model
Integration of Analytical R model on to the Production Environment.
Alex Chamberlain: Deploying a model to production
Prepare yourself for a whirlwind tour of what it takes to run a model 24/7. We'll be looking at Bloomberg's infrastructure for running utility market models. By the ...
Gradient Boosted Trees Model: deploying R models into production environments*
R is the tool of choice for analyzing data and training sophisticated models, but large scale systems are usually implemented in more traditional languages like ...
Yhat for Rapid Deployment of Predictive Models - Greg Lamp - PAPIs.io '14
Building the predictive aspect of applications is the fun, sexy part. New tools like scikit-learn, pandas, and R have made building models less painful, but ...
How to build a FinTech app Part 5 - Deploy your application
This is the fifth and final video in a series on how to build a FinTech app. In Part 5, we will fix various bugs and deploy our application so that it can be accessed ...