Iron.io at The Machine Learning Conference

Attending MLconf in San Francisco on November 10th? If so, come say hello!

We’ve been seeing more and more customers hiring machine learning talent in order to tackle operational efficiencies and hone in on their forecasting. Iron’s platform is helping in almost all phases of the process, from ETL operations helping with the build phase to building models through distributed, hybrid and on-prem, IronWorker deployments.  We’ve never thought of ourselves as a machine learning as a service (MLaaS) company, but we’re apparently getting a lot of traction in the industry which is music to our ears!

The speakers this year are incredible and we’re looking forward to the entire event. From Xavier Amatriain’s background with ML driven medicine to Franziska Bell’s work on uncertainty estimations at Uber, we’re pretty awestruck at the lineup.

The event is being held at the Nikko Hotel in San Francisco on the 10th of November, and you can find more details here:  https://mlconf.com/events/san-francisco-ca-2/

We’ll be following up with a great recap, so stay tuned.  We hope to see you there!

Massive Content, Validation & Serverless: Cloud Expo 2016 Recap

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The Cloud Expo was held June 7-9, 2016 in New York City, and Iron.io sent a team to present our vision for the future, collaborate with other attendees and answer questions. Below is a summary of three technical sessions representative of the Containers track at the conference:

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Introducing Lambda support on Iron.io

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Serverless computing has become a compelling model for companies to add business value without their development teams having to worry about provisioning, managing and scaling infrastructure. The concept is that developers write code that performs business logic based on  some specific input data, and the platform handles the details of:

  • Where to run it: Use some machine with available capacity in its pool
  • When to run it: Either event-driven or scheduled
  • How to run it: Decouple your developers from your runtime. You do not have to be concerned about whether your program is running on bare metal, in a VM or in some sort of container

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