Systems | Development | Analytics | API | Testing

Git-Based CI CD for Machine Learning & MLOps - MLOps Live #3 - With Microsoft & GitHub

The session — featuring David Aronchick, Head of OSS ML Strategy at Microsoft; Marvin Buss, Azure Customer Engineer at Microsoft; Zander Matheson, Senior Data Scientist at GitHub; and Yaron Haviv, Co-Founder and CTO at Iguazio — goes beyond theory, with industry leaders sharing challenges and practical solutions that involve running AI experiments at scale, versioning, delivery to production, reproducibility, and data access.

MLOps Automation From A to Z | Jupyter + KubeFlow + MLRun + Nuclio

Short but comprehensive end-to-end pipeline demo using the Iguazio real-time data science platform. MLOps (also known as DevOps for machine learning) is the practice of collaboration and communication between data scientists and data engineers to help manage the production machine learning (ML) lifecycle. Presented by Yaron Haviv, CTO & Co-Founder of Iguazio.

Why Every Web Developer Should Explore Machine Learning

If software's been eating the world for the past twenty years, it's safe to say machine learning has been eating it for the past five. But what exactly is machine learning? Why should a web developer care? This article by Julie Kent answers these questions. I don't have kids yet, but when I do, I want them to learn two things: Whether or not you believe that the singularity is near, there's no denying that the world runs on data.

How to Gather Data for Machine Learning

Unless you’ve been living in a cave these last few months (a cave that somehow carries sufficient WiFi coverage to reach our blog), you’ll doubtless have heard about machine learning. If you’re a developer, chances are you’re intrigued. The machine learning algorithm, which solves problems without requiring detailed instructions, is one of the most exciting technologies on the planet.

Part 4: How machine learning, AI and automation could break the BI adoption barrier

In the last three parts of this four-part series, we have looked at: research on the state of analytics today and the lack of BI adoption; the history of BI and how we have arrived at the augmented era; and the four main blockers to BI adoption that is stunting the growth your business data culture. Today, let's take a look at how AI and machine learning (ML) can close that adoption gap.