Machine Learning – Software Engineering Daily

At Lyft, Ketan Umare worked on Flyte, an orchestration system for machine learning. Flyte provides reliability and APIs for machine learning workflows, and is used at companies outside of Lyft such as Spotify. Since leaving Lyft, Ketan founded Union.ai, a company focused on productionizing Flyte as a service. He joins the show to talk about The post Union.ai with Ketan Umare appeared first on Software Engineering Daily.

Historically, search engines made money by showing sponsored ads alongside organic results. As the idiom goes, if you’re not paying for something, you are the product. Neeva is a new take on search engines. When you search at neeva.com, you get the type of result you’d expect from a search engine minus any advertising. In The post Ad-free Search on Neeva with Darin Fisher appeared first on Software Engineering Daily.

Charlie Gerard is an incredibly productive developer.  In addition to being the author of Practical Machine Learning in JavaScript, her website charliegerard.dev has a long list of really interesting side projects exploring the intersection of human computer interaction, computer vision, interactivity, and art.  In this episode we touch on some of these projects and broadly The post Practical Machine Learning in JavaScript with Charlie Gerard appeared first on Software Engineering Daily.

Once a machine learning model is trained and validated, it often feels like a major milestone has been achieved.  In reality, it’s more like the first lap in a relay race.  Deploying ML to production bears many similarities to a typical software release process, but brings several novel challenges like failing to generalize as expected The post Responsibly Deploy AI in Production with Anupam Datta appeared first on Software Engineering Daily.

Machine learning models must first be trained.  That training results in a model which must be serialized or packaged up in some way as a deployment artifact.  A popular deployment path is using Tensorflow.js to take advantage of the portability of JavaScript, allowing your model to be run on a web server or client. Gant The post Learning Tensorflow.js with Gant Laborde appeared first on Software Engineering Daily.

Imagine a world where you own some sort of building whether that’s a grocery store, a restaurant, a factory… and you want to know how many people reside in each section of the store, or maybe how long did the average person wait to be seated or how long did it take the average factory The post No Code AI for Video Analytics with Alex Thiele appeared first on Software Engineering Daily.

The dream of machines with artificial general intelligence is entirely plausible in the future, yet well beyond the reach of today’s cutting edge technology.  However, a virtual agent need not win in Alan Turing’s Imitation Game to be useful.  Modern technology can deliver on some of the promises of narrow intelligence for accomplishing specific tasks. The post Virtual Agents for IT and HR with Dan Turchin appeared first on Software Engineering Daily.

Interest in autonomous vehicles dates back to the 1920s. It wasn’t until the 1980s that the first truly autonomous vehicle prototypes began to appear. The first DARPA Grand Challenge took place in 2004 offering competitors $1 million dollars to complete a 150-mile course through the Mojave desert. The prize was not claimed. Since then, rapid The post Autonomous Driving Infrastructure with Vinoj Kumar appeared first on Software Engineering Daily.

Governments, consumers, and companies across the world are becoming more aware and attentive to the risks and causes of climate change. From recycling to using solar power, people are looking for ways to reduce their carbon footprint. Markets like the financial sector, governments, and consulting are looking for ways to understand climate data to make The post Sust Global: Taking Action Against Climate Change with Josh Gilbert appeared first on Software Engineering Daily.

Mark Saroufim is the author of an article entitled “Machine Learning: The Great Stagnation”. Mark is a PyTorch Partner Engineer with Facebook AI. He has spent his entire career developing machine learning and artificial intelligence products. Before joining Facebook to do PyTorch engineering with external partners, Mark was a Machine Learning Engineer at Graphcore. Before The post Machine Learning: The Great Stagnation with Mark Saroufim appeared first on Software Engineering Daily.