AI Engineering Podcast

Summary Every business develops their own specific workflows to address their internal organizational needs. Not all of them are properly documented, or even visible. Workflow automation tools have tried to reduce the manual burden involved, but they are rigid and require substantial investment of time to discover and develop the routines. Boaz Hecht co-founded 8Flow to iteratively discover and au...

Summary Machine learning and AI applications hold the promise of drastically impacting every aspect of modern life. With that potential for profound change comes a responsibility for the creators of the technology to account for the ramifications of their work. In this episode Nicholas Cifuentes-Goodbody guides us through the minefields of social, technical, and ethical considerations that are nec...

Summary Building machine learning systems and other intelligent applications are a complex undertaking. This often requires retrieving data from a warehouse engine, adding an extra barrier to every workflow. The RelationalAI engine was built as a co-processor for your data warehouse that adds a greater degree of flexibility in the representation and analysis of the underlying information, simplify...

Summary Machine learning and generative AI systems have produced truly impressive capabilities. Unfortunately, many of these applications are not designed with the privacy of end-users in mind. TripleBlind is a platform focused on embedding privacy preserving techniques in the machine learning process to produce more user-friendly AI products. In this episode Gharib Gharibi explains how the curren...

Summary Software development involves an interesting balance of creativity and repetition of patterns. Generative AI has accelerated the ability of developer tools to provide useful suggestions that speed up the work of engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers. In this episode Eran Yahav shares the journey that he has taken in building...

Summary Software systems power much of the modern world. For applications that impact the safety and well-being of people there is an extra set of precautions that need to be addressed before deploying to production. If machine learning and AI are part of that application then there is a greater need to validate the proper functionality of the models. In this episode Erez Kaminski shares the work...

Summary Large language models have gained a substantial amount of attention in the area of AI and machine learning. While they are impressive, there are many applications where they are not the best option. In this episode Piero Molino explains how declarative ML approaches allow you to make the best use of the available tools across use cases and data formats. Announcements Hello and welcome to t...

Summary Artificial Intelligence is experiencing a renaissance in the wake of breakthrough natural language models. With new businesses sprouting up to address the various needs of ML and AI teams across the industry, it is a constant challenge to stay informed. Matt Turck has been compiling a report on the state of ML, AI, and Data for his work at FirstMark Capital. In this episode he shares his f...

Summary A core challenge of machine learning systems is getting access to quality data. This often means centralizing information in a single system, but that is impractical in highly regulated industries, such as healthchare. To address this hurdle Rhino Health is building a platform for federated learning on health data, so that everyone can maintain data privacy while benefiting from AI capabil...

Summary Satellite imagery has given us a new perspective on our world, but it is limited by the field of view for the cameras. Synthetic Aperture Radar (SAR) allows for collecting images through clouds and in the dark, giving us a more consistent means of collecting data. In order to identify interesting details in such a vast amount of data it is necessary to use the power of machine learning. IC...

Summary The focus of machine learning projects has long been the model that is built in the process. As AI powered applications grow in popularity and power, the model is just the beginning. In this episode Josh Tobin shares his experience from his time as a machine learning researcher up to his current work as a founder at Gantry, and the shift in focus from model development to machine learning...

Summary Machine learning models have predominantly been built and updated in a batch modality. While this is operationally simpler, it doesn't always provide the best experience or capabilities for end users of the model. Tecton has been investing in the infrastructure and workflows that enable building and updating ML models with real-time data to allow you to react to real-world events as they h...

Summary Shopify uses machine learning to power multiple features in their platform. In order to reduce the amount of effort required to develop and deploy models they have invested in building an opinionated platform for their engineers. They have gone through multiple iterations of the platform and their most recent version is called Merlin. In this episode Isaac Vidas shares the use cases that t...

Summary All data systems are subject to the "garbage in, garbage out" problem. For machine learning applications bad data can lead to unreliable models and unpredictable results. Anomalo is a product designed to alert on bad data by applying machine learning models to various storage and processing systems. In this episode Jeremy Stanley discusses the various challenges that are involved in buildi...

Summary Building a machine learning model one time can be done in an ad-hoc manner, but if you ever want to update it and serve it in production you need a way of repeating a complex sequence of operations. Dagster is an orchestration engine that understands the data that it is manipulating so that you can move beyond coarse task-based representations of your dependencies. In this episode Sandy Ry...

Summary Machine learning is a data-hungry approach to problem solving. Unfortunately, there are a number of problems that would benefit from the automation provided by artificial intelligence capabilities that don’t come with troves of data to build from. Christopher Nguyen and his team at Aitomatic are working to address the "cold start" problem for ML by letting humans generate models by sharing...

Summary Data is one of the core ingredients for machine learning, but the format in which it is understandable to humans is not a useful representation for models. Embedding vectors are a way to structure data in a way that is native to how models interpret and manipulate information. In this episode Frank Liu shares how the Towhee library simplifies the work of translating your unstructured data...

Summary Because machine learning models are constantly interacting with inputs from the real world they are subject to a wide variety of failures. The most commonly discussed error condition is concept drift, but there are numerous other ways that things can go wrong. In this episode Wojtek Kuberski explains how NannyML is designed to compare the predicted performance of your model against its act...

Summary Using machine learning in production requires a sophisticated set of cooperating technologies. A majority of resources that are available for understanding how to design and operate these platforms are focused on either simple examples that don’t scale, or over-engineered technologies designed for the massive scale of big tech companies. In this episode Jacopo Tagliabue shares his vision f...

Summary The increasing sophistication of machine learning has enabled dramatic transformations of businesses and introduced new product categories. At Assembly AI they are offering advanced speech recognition and natural language models as an API service. In this episode founder Dylan Fox discusses the unique challenges of building a business with machine learning as the core product. Announcement...

Summary The majority of machine learning projects that you read about or work on are built around batch processes. The model is trained, and then validated, and then deployed, with each step being a discrete and isolated task. Unfortunately, the real world is rarely static, leading to concept drift and model failures. River is a framework for building streaming machine learning projects that can c...

Summary Machine learning is a transformative tool for the organizations that can take advantage of it. While the frameworks and platforms for building machine learning applications are becoming more powerful and broadly available, there is still a significant investment of time, money, and talent required to take full advantage of it. In order to reduce that barrier further Adam Oliner and Brian C...

Summary In order for a machine learning model to build connections and context across the data that is fed into it the raw data needs to be engineered into semantic features. This is a process that can be tedious and full of toil, requiring constant upkeep and often leading to rework across projects and teams. In order to reduce the amount of wasted effort and speed up experimentation and training...

Summary Machine learning is a data hungry activity, and the quality of the resulting model is highly dependent on the quality of the inputs that it receives. Generating sufficient quantities of high quality labeled data is an expensive and time consuming process. In order to reduce that time and cost Alex Ratner and his team at Snorkel AI have built a system for powering data-centric machine learn...

Summary Deep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference models. Along with that power comes a great deal of complexity in determining what neural architectures are best suited to a given task, engineering features, scaling computation, etc. Predibase is building on the successes of the Ludwig framework for declarative deep lear...