Insights from the deep learning industry.
I see the quote “AI is the new electricity” thrown around in about every other blog post. I think there is truth in it, but I also think most people don’t go to the bottom of what it really means for their business. Let’s first define what we mean by AI: in this context, I’m referring to new advances in machine learning and deep learning.
[Valohai success story] Nyris is developing a high-performance visual search engine that understands the content of an image. The visual search engine works as an easy-to-use API that companies can use to inject visual search as a part of their solution.
Valohai is a deep learning platform that helps you execute on-demand experiments in the cloud with full version control. Jupyter Notebook is a popular IDE for the data scientist. It is especially suited for early data exploration and prototyping.
Pointlessly staring at live logs and waiting for a miracle to happen is a huge time sink for data scientists everywhere. Instead, one should strive for an asynchronous workflow. In this article, we define asynchronous workflows, figure out some of the obstacles and finally guide you to a next article to look at a real-life example in action in Jupyter Notebooks.
Part 2: Tips and tricks for running your deep learning executions on Valohai CLI Valohai executions can be triggered directly from the CLI and let you roll up your sleeves and fine-tune your options a bit more hands-on than our web-based UI. In part one, I showed you how to install and get started with Valohai’s command-line interface (CLI). Now, it’s time to take a deeper dive and power up with features that’ll take your daily productivity to new heights.
Ville Tuulos, machine learning infrastructure architect, was the first to publicly dissect Netflix’s Machine Learning infrastructure at QCon in November 2018 in San Francisco. If you haven’t seen the talk yet, read the summary of his talk here! All the pictures used here, are from Ville's presentation. The full talk is 49 minutes long and you can watch it in its entirety on YouTube. From a scattered toolset to a coherent machine learning platform Ville starts by comparing Machine Learning Infrastructure to an online store and how building one was truly a technical problem twenty years ago. Back then you needed to build the whole online shop yourself starting from setting up the servers because the cloud did not exist. New platforms and technologies have since emerged that allow basically anyone to build up an online store and nowadays it is more about knowing the customers than setting up the webshop.
In our series of machine learning infrastructure blog posts, we recently featured Uber’s Michelangelo. Today we’re happy to be interviewing Ville Tuulos from Netflix. Ville is a machine learning infrastructure architect at Netflix’s Los Gatos, CA office.
Part 1: Getting started As new Valohai users get acquainted with the platform, many fall in love our web-based UI - and for good reason. Its responsive, intuitive and gets the job done with just a few clicks. But don’t be fooled into thinking that’s the end of the interface conversation. We know it takes different [key]strokes for different folks, so Valohai also includes a command-line interface (CLI) and the REST API.
One of the core design paradigms of Valohai is technology agnosticism. Building on top of the file system and in our case Docker means that we support running very different kinds of applications, scripts, languages and frameworks on top of Valohai. This means most systems are Valohai-ready because of these common abstractions. The same is true for TensorBoard as well.