DataOps

Data operations is the pick axe in the AI gold rush. Without the right data and the equipment to mine it, the promise of AI for many companies will be left unrealized. This is especially true for companies in finance, retail, healthcare, and more where you create value with the algorithms and analysis you do on data and not how you access and manage it. These companies would be wise to work with trusted software partners to build up their data operations teams. The data challenges that come with AI can’t be solved by more job listings. We’re going to need real technology to help our data engineering kings and queens process the next 180 zettabytes.

When we talk about exciting innovations like self driving cars, or ask Amazon’s Alexa to…

This first-of-its-kind survey asks data engineers and other data professionals about the State of Data Operations. The resultant report will provide a snapshot of the DataOps function in today’s companies. It will explore what is working, and where improvements need to be made to help companies most effectively use their data. Immediately after taking the survey, you’ll receive an assessment your company’s current DataOps based on our model and your responses.

Today Nexla is announcing a solution for companies to run Machine learning models in their own data centers. Nexie is an ingenious piece of hardware designed by Nexla which brings machine learning into your own data center. The device can be powered on in any data center across the globe. Nexie can connect with existing storage solutions via multiplexed universal ports. Nexie comes with two ports, In & Out. The In port allows you to receive data, Out port outputs the results of the model. Multiple Nexies can be joined together to create portable clusters!

In hundreds of conversations with customers, investors, and other data professionals, we’ve found that everyone believes they have heard the term before, but isn’t quite sure what it means, exactly. When asked to describe DataOps, most people intuitively understood it had something to do with moving data to the right place in the right format. To move the conversation forward, we need a clear definition we can all use. At Nexla, we believe:

DataOps is the function within an organization that controls the data journey from source to value.

The Internet Archive, also known as the Wayback Machine, has been capturing snapshots of the World Wide Web for over 20 years. But a new effort to save a specific type of Internet content- scientific data- has emerged among some scientists and professors. Groups like the Penn Program in Environmental Humanities and its offshoot Data Refuge are committed to preserving “…the facts we need at a time of ongoing climate change.”

These groups encourage others to download and store public scientific data. They’ve acted as a catalyst for groups around the country to host data meetups. At Nexla, we are committed to building tools that make it easier to collaborate with data. In that spirit, we’d like to share two of the methods we’ve found for archiving important data sets.

At Nexla, we think of APIs are belonging to one of two categories: service or data. A service API is a building block for a developer, a way to hook into another application’s functionality. It’s how developers can build apps in Slack, or add google maps to a web app. Data APIs on the other hand are a bit more limited. They allow developers to pull data from a source and then use that data however they see fit. They don’t offer any additional functionality or services. And that’s why they’re facing extinction.

Film buffs will remember the above scene from Stanley Kubrick’s iconic Dr. Strangelove, in which President Muffley says, “Gentlemen, you can’t fight in here! This is the War Room!” The absurd, satirical line is oddly applicable to what’s happening today in machine learning teams. The machine learning folks shouldn’t be data wrangling- they should be focused on machine learning. But because we don’t often receive our data in a usable format, valuable time is spent transforming, moving, and cleaning data.