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Incorporating Human In The Loop Processes into Data Pipelines

Human Robot

Even if you're working with 100% machine-created data, more than likely you're performing some amount of manual inspection on your data at different points in the data analysis process, and the output of your machine learning models.

Many companies including Google, GoDaddy, Yahoo! and LinkedIn use what's known as HITL, or Human-In-The-Loop, to improve the accuracy of everything from maps, matching business listings, ranking top search results and referring relevant job postings.

Why are we still at this point? Because the promise of fully-automated end-to-end flows is a false one. So if we have to have a human involved at some point, what’s the best way to go about it?

Join me for a complimentary webinar on Thursday April 14th at 7PM EST where I'll show you multiple ways to implement and leverage HITL processes as part of your data pipelines.

Reserve your seat today >>

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How to Build a Data Pipeline in Data Science Studio

Join me Thursday, March 24th at 7PM EST for a complimentary webinar where you'll learn how to build a data pipeline for cleaning and standardizing data using Data Science Studio (DSS). We all deal with dirty, messy data. I'll show you how to use DSS to clean it up and get it ready for analysis using the super easy to use drag-and-drop interface DSS provides.

Sign up for the webinar today >>

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Examples of Data Pipelines You Can Build Today

PipelineOver the past few months I started hearing the term “data pipeline” more and more at the local data meetups. Curious as to just what that meant, I looked it up. In this post I'm going to tell you what I found, and more importantly provide real-world examples of data pipelines you can use for your data projects. [Read more…]