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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Arun Kumar is an Assistant Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute at the University of California, San Diego. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics. Systems and ideas based on his research have been released as part
The post Data Management Systems and Artificial Intelligence with Arun Kumar appeared first on Software Engineering Daily.
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Application Programming Interfaces (APIs) are interfaces that enable multiple software applications to send and retrieve data from one another. They are commonly used for retrieving, saving, editing, or deleting data from databases, transmitting data between apps, and embedding third-party services into apps. The company BaseTen helps companies build and deploy machine learning APIs and applications.
The post BaseTen: Creating Machine Learning APIs with Tuhin Srivastava and Amir Haghighat appeared first on Software Engineering Daily.
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Natural Language Processing (NLP) is a branch of artificial intelligence concerned with giving computers the ability to understand text and spoken words. “Understanding” includes intent, sentiment, and what’s important in the message. NLP powers things like voice-operated software, digital assistants, customer service chat bots, and many other academic, consumer and enterprise tools. The company Botpress
The post Botpress: Natural Language Processing with Sylvain Perron appeared first on Software Engineering Daily.
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Using artificial intelligence and machine learning in a product or database is traditionally difficult because it involves a lot of manual setup, specialized training, and a clear understanding of the various ML models and algorithms. You need to develop the right ML model for your data, train the model, evaluate it, optimize it, analyze it
The post MindsDB: Automated Machine Learning with Jorge Torres appeared first on Software Engineering Daily.
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Creation Labs is helping bring Europe 1 step closer to fully autonomous long haul trucking. They have developed an AI Driver Assistance System (AIDAS) that retrofits to any commercial vehicle, starting with VW Crafters and MAN TGE trucks. Their system uses camera hardware mounted to the vehicle to capture video data that is processed with
The post Creation Labs: Self Driving Trucks with Jakub Langr appeared first on Software Engineering Daily.
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Vectors are the foundational mathematical building blocks of Machine Learning. Machine Learning models must transform input data into vectors to perform their operations, creating what is known as a vector embedding. Since data is not stored in vector form, an ML application must perform significant work to transform data in different formats into a form
The post Pinecone: Vector Database with Edo Liberty appeared first on Software Engineering Daily.
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The incredible advances in machine learning research in recent years often take time to propagate out into usage in the field. One reason for this is that such “state-of-the-art” results for machine learning performance rely on the use of handwritten, idiosyncratic optimizations for specific hardware models or operating contexts. When developers are building ML-powered systems
The post OctoML: Automated Deep Learning Engineering with Jason Knight and Luis Ceze appeared first on Software Engineering Daily.
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Embedded Software Engineering is the practice of building software that controls embedded systems- that is, machines or devices other than standard computers. Embedded systems appear in a variety of applications, from small microcontrollers, to consumer electronics, to large-scale machines such as cars, airplanes, and machine tools. iRobot is a consumer robotics company that applies embedded
The post iRobot with Chris Svec appeared first on Software Engineering Daily.
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Reinforcement learning is a paradigm in machine learning that uses incentives- or “reinforcement”- to drive learning. The learner is conceptualized as an intelligent agent working within a system of rewards and penalties in order to solve a novel problem. The agent is designed to maximize rewards while pursuing a solution by trial-and-error. Programming a system
The post Reinforcement Learning and Robotics with Nathan Lambert appeared first on Software Engineering Daily.
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Companies can have a negative impact on the environment by outputting excess carbon. Many companies want to reduce their net carbon impact to zero, which can be done by investing in forests. Pachama is a marketplace for forest investments. Pachama uses satellites, imaging, machine learning, and other techniques to determine how much carbon is being
The post Machine Learning Carbon Capture with Diego Saez-Gil appeared first on Software Engineering Daily.
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