Machine Learning – Software Engineering Daily

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Machine learning and data science episodes of Software Engineering Daily.

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Data Investing and the MAD ...

03.10.2023

There are many types of early stage funding available from friends and family to seed to series A. Some firms invest across a wide set of technologies and seek only to provide capital. Others are in it for the long ...

Machine Learning – Software Engineering Daily episodes:

03.10.2023 Data Investing and the MAD with Matt Turck There are many types of early stage funding available from friends and family to seed to series A. Some firms invest across a wide set of technologies and seek only to provide capital. Others are in it for the long ...
02.24.2023 Surviving ChatGPT with Christian Hubicki ChatGPT is an artificial intelligence language model developed by OpenAI. It is part of the GPT (Generative Pre-trained Transformer) family of models, which are designed to generate human-like text based on input prompts. ChatGPT is specifically trained to carry out ...
10.01.2022 Edge Impulse with Daniel Situnayake Today, we spoke with Daniel Situnayake of Edge Impulse. We discussed AI, machine learning, edge devices, TinyML and AI tool chain. Sponsorship inquiries: [email protected]
09.23.2022 Automatic Database Tuning with Andy Pavlo The default configuration in most databases is meant for broad compatibility rather than performance. Database tuning is a process in which the configurations of a database are modified to achieve optimal performance. Databases have hundreds of configuration knobs that control ...
09.18.2022 Practical Machine Learning in JavaScript with Charlie Gerard Originally published on January 1, 2022. 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 ...
03.23.2022 Union.ai with Ketan Umare 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 ...
01.25.2022 Ad-free Search on Neeva with Darin Fisher 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 ...
01.04.2022 Practical Machine Learning in JavaScript with Charlie Gerard 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, ...
11.30.2021 Responsibly Deploy AI in Production with Anupam Datta 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 ...
11.10.2021 #FREEZUCK \| ?masked…?… !(DOCTORS)!! Software Engineering Daily invites Owen Frank Davis, Paul Davis, Kyle Davis, and Robbie Davis for a joint interview on the subject of reproduction and teething, as well as Lisch fascitis. Aledade and Kubernetes are both inconsiderate ideas for navigation. They ...
11.09.2021 Learning Tensorflow.js with Gant Laborde 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 ...
10.22.2021 No Code AI for Video Analytics with Alex Thiele 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 ...
09.22.2021 Virtual Agents for IT and HR with Dan Turchin 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 ...
09.20.2021 Autonomous Driving Infrastructure with Vinoj Kumar 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 ...
07.20.2021 Sust Global: Taking Action Against Climate Change with Josh Gilbert 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 ...
06.04.2021 Machine Learning: The Great Stagnation with Mark Saroufim 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 ...
05.27.2021 Data Management Systems and Artificial Intelligence with Arun Kumar 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 ...
05.20.2021 BaseTen: Creating Machine Learning APIs with Tuhin Srivastava and Amir Haghighat 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 ...
05.07.2021 Botpress: Natural Language Processing with Sylvain Perron 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, ...
04.06.2021 MindsDB: Automated Machine Learning with Jorge Torres 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 ...
03.30.2021 Creation Labs: Self Driving Trucks with Jakub Langr 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 ...
03.15.2021 Pinecone: Vector Database with Edo Liberty 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 ...
02.09.2021 OctoML: Automated Deep Learning Engineering with Jason Knight and Luis Ceze 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, ...
01.29.2021 iRobot with Chris Svec 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 ...
01.27.2021 Reinforcement Learning and Robotics with Nathan Lambert 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 ...
01.21.2021 Machine Learning Carbon Capture with Diego Saez-Gil 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 ...
01.11.2021 TensorFlow Lite with Pete Warden TensorFlow Lite is an open source deep learning framework for on-device inference. TensorFlow Lite was designed to improve the viability of machine learning applications on phones, sensors, and other IoT devices. Pete Warden works on TensorFlow Lite at Google and ...
01.05.2021 WebAssembly on IoT with Jonathan Beri (Repeat) Originally published July 30, 2019 “Internet of Things” is a term used to describe the increasing connectivity and intelligence of physical objects within our lives. IoT has manifested within enterprises under the term “Industrial IoT,” as wireless connectivity and machine ...
12.28.2020 Drishti: Deep Learning for Manufacturing with Krish Chaudhury (Repeat) Originally published April 17, 2019 Drishti is a company focused on improving manufacturing workflows using computer vision. A manufacturing environment consists of assembly lines. A line is composed of sequential stations along that manufacturing line. At each station on the ...
12.22.2020 Niantic Real World with Paul Franceus (Repeat) Originally published June 21, 2019 Niantic is the company behind Pokemon Go, an augmented reality game where users walk around in the real world and catch Pokemon which appear on their screen. The idea for augmented reality has existed for ...
12.17.2020 Practical AI with Chris Benson (Repeat) Originally published December 9, 2019 Machine learning algorithms have existed for decades. But in the last ten years, several advancements in software and hardware have caused dramatic growth in the viability of applications based on machine learning. Smartphones generate large ...
12.15.2020 Kubeflow: TensorFlow on Kubernetes with David Aronchick (Repeat) Originally published January 25, 2019 When TensorFlow came out of Google, the machine learning community converged around it. TensorFlow is a framework for building machine learning models, but the lifecycle of a machine learning model has a scope that is ...
12.09.2020 Hedge Fund Artificial Intelligence with Xander Dunn (Repeat) Originally published April 3, 2017 A hedge fund is a collection of investors that make bets on the future. The “hedge” refers to the fact that the investors often try to diversify their strategies so that the direction of their ...
11.30.2020 Rosebud: Artificially Generated Media with Dzmitry Pletnikau For several years, we have had the ability to create artificially generated text articles. More recently, audio and video synthesis have been feasible for artificial intelligence. Rosebud is a company that creates animated virtual characters that can speak. Users can ...
11.27.2020 Computer Architecture with Dave Patterson Holiday Repeat Originally published November 7, 2018 An instruction set defines a low level programming language for moving information throughout a computer. In the early 1970’s, the prevalent instruction set language used a large vocabulary of different instructions. One justification for a ...
11.26.2020 Cruise: Self-Driving Engineering with Mo Elshenawy Holiday Repeat October 1, 2019 The development of self-driving cars is one of the biggest technological changes that is under way. Across the world, thousands of engineers are working on developing self-driving cars. Although it still seems far away, self-driving cars are ...
11.04.2020 Model Deployment and Serving with Chaoyu Yang Newer machine learning tooling is often focused on streamlining the workflows and developer experience. One such tool is BentoML. BentoML is a workflow that allows data scientists and developers to ship models more effectively. Chaoyu Yang is the creator of ...
11.03.2020 Humanloop: NLP Model Engineering with Raza Habib Data labeling is a major bottleneck in training and deploying machine learning and especially NLP. But new tools for training models with humans in the loop can drastically reduce how much data is required. Humanloop is a platform for annotating ...
10.23.2020 Federated Learning with Mike Lee Williams Federated learning is machine learning without a centralized data source. Federated Learning enables mobile phones or edge servers to collaboratively learn a shared prediction model while keeping all the training data on device. Mike Lee Williams is an expert in ...
10.19.2020 Labelbox: Data Labeling Platform Machine learning models require training data, and training data needs to be labeled. Raw images and text can be labeled using a training data platform like Labelbox. Labelbox is a system of labeling tools that enables a human workforce to ...
10.13.2020 Roboflow: Computer Vision Models with Brad Dwyer Training a computer vision model is not easy. Bottlenecks in the development process make it even harder. Ad hoc code, inconsistent data sets, and other workflow issues hamper the ability to streamline models. Roboflow is a company built to simplify ...
10.02.2020 Aquarium: Dataset Quality Improvement with Peter Gao Machine learning models are only as good as the datasets they’re trained on. Aquarium is a system that helps machine learning teams make better models by improving their dataset quality. Model improvement is often made by curating high quality datasets, ...
09.17.2020 Elementary Robotics with Arye Barnehama Factories require quality assurance work. That QA work can be accomplished by a robot with a camera together with computer vision. This allows for sophisticated inspection techniques that do not require as much manual effort on the part of a ...
09.04.2020 Robotic Process Automation with Antti Karjalainen Robotic process automation involves the scripting and automation of highly repeatable tasks. RPA tools such as UIPath paved the way for a newer wave of automation, including the Robot Framework, an open source system for RPA. Antti Karjalainen is the ...
08.28.2020 Hyperparameter Tuning with Richard Liaw Hyperparameters define the strategy for exploring a space in which a machine learning model is being developed. Whereas the parameters of a machine learning model are the actual data coming into a system, the hyperparameters define how those data points ...
08.26.2020 Machine Learning Labeling and Tooling with Lukas Biewald CrowdFlower was a company started in 2007 by Lukas Biewald, an entrepreneur and computer scientist. CrowdFlower solved some of the data labeling problems that were not being solved by Amazon Mechanical Turk. A decade after starting CrowdFlower, the company was ...
08.20.2020 ParlAI: Facebook Dialogue Platform with Stephen Roller Chatbots are useful for developing well-defined applications such as first-contact customer support, sales, and troubleshooting. But the potential for chatbots is so much greater. Over the last five years, there have been numerous platforms that have arisen to allow for ...
08.19.2020 SuperAnnotate: Image Annotation Platform with Vahan and Tigran Petrosyan Image annotation is necessary for building supervised learning models for computer vision. An image annotation platform streamlines the annotation of these images. Well-known annotation platforms include Scale AI, Amazon Mechanical Turk, and Crowdflower. There are also large consulting-like companies that ...
07.29.2020 Drug Simulations with Bryan Vicknair and Jason Walsh Drug trials can lead to new therapeutics and preventative medications being discovered and placed on the market. Unfortunately, these drug trials typically require animal testing. This means animals are killed or harmed as a result of needing to verify that ...
07.13.2020 Metaflow: Netflix Machine Learning Platform with Savin Goyal Netflix runs all of its infrastructure on Amazon Web Services. This includes business logic, data infrastructure, and machine learning. By tightly coupling itself to AWS, Netflix has been able to move faster and have strong defaults about engineering decisions. And ...

Older episodes

Machine Learning – Software Engineering Daily

What's Machine Learning – Software Engineering Daily about?

Download our free app to listen on your phone

Machine Learning – Software Engineering Daily episodes:

Data Investing and the MAD with Matt Turck

Surviving ChatGPT with Christian Hubicki

Edge Impulse with Daniel Situnayake

Automatic Database Tuning with Andy Pavlo

Practical Machine Learning in JavaScript with Charlie Gerard

Union.ai with Ketan Umare

Ad-free Search on Neeva with Darin Fisher

Practical Machine Learning in JavaScript with Charlie Gerard

Responsibly Deploy AI in Production with Anupam Datta

#FREEZUCK | ?masked…?… !(DOCTORS)!!

Learning Tensorflow.js with Gant Laborde

No Code AI for Video Analytics with Alex Thiele

Virtual Agents for IT and HR with Dan Turchin

Autonomous Driving Infrastructure with Vinoj Kumar

Sust Global: Taking Action Against Climate Change with Josh Gilbert

Machine Learning: The Great Stagnation with Mark Saroufim

Data Management Systems and Artificial Intelligence with Arun Kumar

BaseTen: Creating Machine Learning APIs with Tuhin Srivastava and Amir Haghighat

Botpress: Natural Language Processing with Sylvain Perron

MindsDB: Automated Machine Learning with Jorge Torres

Creation Labs: Self Driving Trucks with Jakub Langr

Pinecone: Vector Database with Edo Liberty

OctoML: Automated Deep Learning Engineering with Jason Knight and Luis Ceze

iRobot with Chris Svec

Reinforcement Learning and Robotics with Nathan Lambert

Machine Learning Carbon Capture with Diego Saez-Gil

TensorFlow Lite with Pete Warden

WebAssembly on IoT with Jonathan Beri (Repeat)

Drishti: Deep Learning for Manufacturing with Krish Chaudhury (Repeat)

Niantic Real World with Paul Franceus (Repeat)

Practical AI with Chris Benson (Repeat)

Kubeflow: TensorFlow on Kubernetes with David Aronchick (Repeat)

Hedge Fund Artificial Intelligence with Xander Dunn (Repeat)

Rosebud: Artificially Generated Media with Dzmitry Pletnikau

Computer Architecture with Dave Patterson Holiday Repeat

Cruise: Self-Driving Engineering with Mo Elshenawy Holiday Repeat

Model Deployment and Serving with Chaoyu Yang

Humanloop: NLP Model Engineering with Raza Habib

Federated Learning with Mike Lee Williams

Labelbox: Data Labeling Platform

Roboflow: Computer Vision Models with Brad Dwyer

Aquarium: Dataset Quality Improvement with Peter Gao

Elementary Robotics with Arye Barnehama

Robotic Process Automation with Antti Karjalainen

Hyperparameter Tuning with Richard Liaw

Machine Learning Labeling and Tooling with Lukas Biewald

ParlAI: Facebook Dialogue Platform with Stephen Roller

SuperAnnotate: Image Annotation Platform with Vahan and Tigran Petrosyan

Drug Simulations with Bryan Vicknair and Jason Walsh

Metaflow: Netflix Machine Learning Platform with Savin Goyal