AI News, Using data science to build better products

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.

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