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 are fed into the training process for building a model to be used by an end consumer. A different set of hyperparameters will yield a different model. Thus, it is important to try different hyperparameter configurations to see which models end up performing better for a given application. Hyperparameter tuning is an art and a science. Richard Liaw is an engineer and researcher, and the creator of Tune, a library for scalable hyperparameter tuning. Richard joins the show to talk through hyperparameters and the software that he has built for tuning them. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Hyperparameter Tuning with Richard Liaw appeared first on Software Engineering Daily.

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 sold for several hundred million dollars. Today, data labeling has only grown in volume and scope. But Lukas has moved on to a different part of the machine learning stack: tooling for hyperparameter search and machine learning monitoring. Lukas Biewald joins the show to talk about the problems he was solving with CrowdFlower, the solutions that he developed as part of that company, and the efforts with his current focus: Weights and Biases, a machine learning tooling company. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Machine Learning Labeling and Tooling with Lukas Biewald appeared first on Software Engineering Daily.

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 better, more streamlined chatbot creation. Dialogue software enables the creation of sophisticated chatbots. ParlAI is a dialogue platform built inside of Facebook. It allows for the development of dialogue models within Facebook. These chatbots can “remember” information from session to session, and continually learn from user input. Stephen Roller is an engineer who helped build ParlAI, and he joins the show to discuss the history of chatbot applications and what the Facebook team is trying to accomplish with the development of ParlAI. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post ParlAI: Facebook Dialogue Platform with Stephen Roller appeared first on Software Engineering Daily.

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 will annotate images in bulk for you. If you have an application that requires lots of annotation, such as self-driving cars, then you might be compelled to outsource this annotation to such a company. SuperAnnotate is an image annotation platform that can be used by these image annotation outsourcing firms. This episode explores SuperAnnotate, and the growing niche of image annotation. Vahan and Tigran Petrosyan are the founders of SuperAnnotate, and join the show for today’s interview. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post SuperAnnotate: Image Annotation Platform with Vahan and Tigran Petrosyan appeared first on Software Engineering Daily.

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 a drug will not kill humans. Animal testing is unavoidable, but the extent to which testing needs to occur can be reduced by inserting machine learning models which simulate the effects of a drug on the human body. If the simulated effect is negative enough, animal testing doesn’t need to be run, thus no animals need to be harmed. Bryan Vicknair and Jason Walsh work at VeriSIM Life, a company which makes software simulations of animals. These simulations can be used to model drug testing, and change the workflow for drug trials. They join the show to talk through the mechanics of drug testing, and how VeriSIM Life fits into that workflow. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Drug Simulations with Bryan Vicknair and Jason Walsh appeared first on Software Engineering Daily.

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 today, AWS has such an expanse of services that it can be used as a platform to build custom tools. Metaflow is an open source machine learning platform built on top of AWS that allows engineers at Netflix to build directed acyclic graphs for training models. These DAGs get deployed to AWS as Step Functions, a serverless orchestration platform. Savin Goyal is a machine learning engineer with Netflix, and he joins the show to talk about the machine learning challenges within Netflix, and his experience working on Metaflow. We also talk about DAG systems such as AWS Step Functions and Airflow. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Metaflow: Netflix Machine Learning Platform with Savin Goyal appeared first on Software Engineering Daily.

Developing machine learning models is not easy. From the perspective of the machine learning researcher, there is the iterative process of tuning hyperparameters and selecting relevant features. From the perspective of the operations engineer, there is a handoff from development to production, and the management of GPU clusters to parallelize model training. In the last five years, machine learning has become easier to use thanks to point solutions. TensorFlow, cloud provider tools, Spark, Jupyter Notebooks. But every company works differently, and there are few hard and fast rules for the workflows around machine learning operations. Determined AI is a platform that provides a means for collaborating around data prep, model development and training, and model deployment. Neil Conway is a co-founder of Determined, and he joins the show to discuss the challenges around machine learning operations, and what he has built with Determined. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Determined AI: Machine Learning Ops with Neil Conway appeared first on Software Engineering Daily.

Deepgram is an end-to-end deep learning platform for speech recognition. Unlike the general purpose APIs from Google or Amazon, Deepgram models are custom-trained for each customer. Whether the customer is a call center, a podcasting company, or a sales department, Deepgram can work with them to build something specific to their use case. Sound data is incredibly rich. Consider all the features in a voice recording: volume, intonation, inflection. And once the speech is transcribed, there are many more features that can be discovered from the text transcription. Scott Stephenson is the CEO of Deepgram, and he joins the show to talk through end-to-end deep learning for speech, as well as the dynamics of the business and the deployment strategy for working with customers. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Deepgram: End-to-End Speech Recognition with Scott Stephenson appeared first on Software Engineering Daily.

At a customer service center, thousands of hours of audio are generated. This audio provides a wealth of information to transcribe and analyze. With the additional data of the most successful customer service representatives, machine learning models can be trained to identify which speech patterns are associated with a successful worker. By identifying these speaking patterns, a customer service center can continuously improve, with the different representatives learning the different patterns. The same is true for other speech-based tasks, such as sales calls. Cresta is a company that builds systems to ingest high volumes of speech data in order to discover features that correlate with high performance human workers. Zayd Enam is a co-founder of Cresta, and joins the show to talk about the domain of speech data and what he and his team are building at Cresta. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Cresta: Speech ML for Calls with Zayd Enam appeared first on Software Engineering Daily.

Originally published October 8, 2019. We are taking a few weeks off. We’ll be back soon with new episodes. Video surveillance impacts human lives every day.  On most days, we do not feel the impact of video surveillance. But the effects of video surveillance have tremendous potential. It can be used to solve crimes and find missing children. It can be used to intimidate journalists and empower dictators. Like any piece of technology, video surveillance can be used for good or evil. Video recognition lets us make better use of video feeds. A stream of raw video doesn’t provide much utility if we can’t easily model its contents. Without video recognition, we must have a human sitting in front of the video to manually understand what is going on in that video. Veronica Yurchuk and Kosh Shysh are the founders of Traces.ai, a company building video recognition technology focused on safety, anonymity, and positive usage. They join the show to discuss the field of video analysis, and their vision for how video will shape our lives in the future. The post Traces: Video Recognition with Veronica Yurchuk and Kostyantyn Shysh (Summer Break Repeat) appeared first on Software Engineering Daily.