AI Engineering Podcast
This show is your guidebook to building scalable and maintainable AI systems. You will learn how to architect AI applications, apply AI to your work, and the considerations involved in building or customizing new models. Everything that you need to know to deliver real impact and value with machine learning and artificial intelligence.
Episodes
Kubernetes, Compliance, and Control: The Operational Backbone of AI Sovereignty
Summary In this episode of the AI Engineering Podcast, Steven Watt, leader of the Office of the CTO at Red Hat, discusses practical paths to achieving AI sovereignty for organizations. He shares his two-decade experience in AI, highlighting how governments are building GPU platforms and protected data hubs to maintain control over AI workloads. Steve emphasizes why self-managed infrastructure
From Blind Spots to Observability: Operationalizing LLM Apps with OpenLit
Summary In this episode of the AI Engineering Podcast, Aman Agarwal, creator of OpenLit, discusses the operational foundations required to run LLM-powered applications in production. He highlights common early blind spots teams face, including opaque model behavior, runaway token costs, and brittle prompt management, emphasizing that strong observability and cost tracking must be established
Taming Voice Complexity with Dynamic Ensembles at Modulate
Summary In this episode of the AI Engineering Podcast, Carter Huffman, co-founder and CTO of Modulate, discusses the engineering behind low-latency, high-accuracy Voice AI. He explains why voice is a uniquely challenging modality due to its rich non-textual signals like tone, emotion, and context, and how simple speech-to-text-to-speech pipelines can't capture the necessary nuance. Carter int
GPU Clouds, Aggregators, and the New Economics of AI Compute
Summary In this episode I sit down with Hugo Shi, co-founder and CTO of Saturn Cloud, to map the strategic realities of sourcing and operating GPUs across clouds. Hugo breaks down today’s provider landscape—from hyperscalers to full-service GPU clouds, bare metal/concierge providers, and emerging GPU aggregators—and how to choose among them based on security posture, managed services, and cos
The Future of Dev Experience: Spotify’s Playbook for Organization‑Scale AI
Summary In this episode of the AI Engineering Podcast Niklas Gustavsson, Chief Architect at Spotify, talks about scaling AI across engineering and product. He explores how Spotify's highly distributed architecture was built to support rapid adoption of coding agents like Copilot, Cursor, and Claude Code, enabled by standardization and Backstage. The conversation covers the tension between bot
Generative AI Meets Accessibility: Benchmarks, Breakthroughs, and Blind Spots with Joe Devon
Summary In this episode Joe Devon, co-founder of Global Accessibility Awareness Day (GAAD), talks about how generative AI can both help and harm digital accessibility — and what it will take to tilt the balance toward inclusion. Joe shares his personal motivation for the work, real-world stakes for disabled users across web, mobile, and developer tooling, and compelling stories that illustrat
Beyond the Chatbot: Practical Frameworks for Agentic Capabilities in SaaS
Summary In this episode product and engineering leader Preeti Shukla explores how and when to add agentic capabilities to SaaS platforms. She digs into the operational realities that AI agents must meet inside multi-tenant software: latency, cost control, data privacy, tenant isolation, RBAC, and auditability. Preeti outlines practical frameworks for selecting models and providers, when to se
MCP as the API for AI‑Native Systems: Security, Orchestration, and Scale
Summary In this episode Craig McLuckie, co-creator of Kubernetes and founder/CEO of Stacklok, talks about how to improve security and reliability for AI agents using curated, optimized deployments of the Model Context Protocol (MCP). Craig explains why MCP is emerging as the API layer for AI‑native applications, how to balance short‑term productivity with long‑term platform thinking, and why
Context as Code, DevX as Leverage: Accelerating Software with Multi‑Agent Workflows
Summary In this episode Max Beauchemin explores how multiplayer, multi‑agent engineering is reshaping individual and team velocity for building data and AI systems. Max shares his journey from Airflow and Superset to going all‑in on AI coding agents, describing a pragmatic “AI‑first reflex” for nearly every task and the emerging role of humans as orchestrators of agents. He digs into shifting
Inside the Black Box: Neuron-Level Control and Safer LLMs
Summary In this episode of the AI Engineering Podcast Vinay Kumar, founder and CEO of Arya.ai and head of Lexsi Labs, talks about practical strategies for understanding and steering AI systems. He discusses the differences between interpretability and explainability, and why post-hoc methods can be misleading. Vinay shares his approach to tracing relevance through deep networks and LLMs using
Building the Internet of Agents: Identity, Observability, and Open Protocols
SummaryIn this episode Guillaume de Saint Marc, VP of Engineering at Cisco Outshift, talks about the complexities and opportunities of scaling multi‑agent systems. Guillaume explains why specialized agents collaborating as a team inspire trust in enterprise settings, and contrasts rigid, “lift-and-shift” agentic workflows with fully self-forming systems. We explore the emerging Internet of Agents,
Agents, IDEs, and the Blast Radius: Practical AI for Software Engineers
SummaryIn this episode of the AI Engineering Podcast Will Vincent, Python developer advocate at JetBrains (PyCharm), talks about how AI utilities are revolutionizing software engineering beyond basic code completion. He discusses the shift from "vibe coding" to "vibe engineering," where engineers collaborate with AI agents through clear guidelines, iterative specs, and tight guardrails. Will share
From MRI to World Models: How AI Is Changing What We See
SummaryIn this episode of the AI Engineering Podcast Daniel Sodickson, Chief of Innovation in Radiology at NYU Grossman School of Medicine, talks about harnessing AI systems to truly understand images and revolutionize science and healthcare. Dan shares his journey from linear reconstruction to early deep learning for accelerated MRI, highlighting the importance of domain expertise when adapting m
Specs, Tests, and Self‑Verification: The Playbook for Agentic Engineering Teams
SummaryIn this episode Andrew Filev, CEO and founder of ZenCoder, takes a deep dive into the system design, workflows, and organizational changes behind building agentic coding systems. He traces the evolution from autocomplete to truly agentic models, discusses why context engineering and verification are the real unlocks for reliability, and outlines a pragmatic path from “vibe coding” to AI‑fir
From Probabilistic to Trustworthy: Building Orion, an Agentic Analytics Platform
SummaryIn this episode of the AI Engineering Podcast Lucas Thelosen and Drew Gillson talk about Orion, their agentic analytics platform that delivers proactive, push-based insights to business users through asynchronous thinking with rich organizational context. Lucas and Drew share their approach to building trustworthy analysis by grounding in semantic layers, fact tables, and quality-assurance
Building Production-Ready AI Agents with Pydantic AI
SummaryIn this episode of the AI Engineering Podcast Samuel Colvin, creator of Pydantic and founder of Pydantic Inc, talks about Pydantic AI - a type-safe framework for building structured AI agents in Python. Samuel explains why he built Pydantic AI to bring FastAPI-like ergonomics and production-grade engineering to agents, focusing on strong typing, minimal abstractions, and reliability, observ
From GPUs to Workloads: Flex AI’s Blueprint for Fast, Cost‑Efficient AI
SummaryIn this episode of the AI Engineering Podcast Brijesh Tripathi, CEO of Flex AI, talks about revolutionizing AI engineering by removing DevOps burdens through "workload as a service". Brijesh shares his expertise from leading AI/HPC architecture at Intel and deploying supercomputers like Aurora, highlighting how access friction and idle infrastructure slow progress. He discusses Flex AI's in
Right-Sizing AI: Small Language Models for Real-World Production
SummaryIn this episode of the AI Engineering Podcast Steven Huels, Vice President of AI Engineering & Product Strategy at Red Hat, talks about the practical applications of small language models (SLMs) for production workloads. He discusses how SLMs offer a pragmatic choice due to their ability to fit on single enterprise GPUs and provide model selection trade-offs. The conversation cove
AI Agents and Identity Management
SummaryIn this episode of the AI Engineering Podcast Julianna Lamb, co-founder and CTO of Stytch, talks about the complexities of managing identity and authentication in agentic workflows. She explores the evolving landscape of identity management in the context of machine learning and AI, highlighting the importance of flexible compute environments and seamless data exchange. The conversation cov
Revolutionizing Production Systems: The Resolve AI Approach
SummaryIn this episode of the AI Engineering Podcast, CEO of Resolve AI Spiros Xanthos shares his insights on building agentic capabilities for operational systems. He discusses the limitations of traditional observability tools and the need for AI agents that can reason through complex systems to provide actionable insights and solutions. The conversation highlights the architecture of Resolve AI
Designing Scalable AI Systems with FastMCP: Challenges and Innovations
SummaryIn this episode of the AI Engineering Podcast Jeremiah Lowin, founder and CEO of Prefect Technologies, talks about the FastMCP framework and the design of MCP servers. Jeremiah explains the evolution of FastMCP, from its initial creation as a simpler alternative to the MCP SDK to its current role in facilitating the deployment of AI tools. The discussion covers the complexities of designing
Proactive Monitoring in Heavy Industry: The Role of AI and Human Curiosity
SummaryIn this episode of the AI Engineering Podcast Dr. Tara Javidi, CTO of KavAI, talks about developing AI systems for proactive monitoring in heavy industry. Dr. Javidi shares her background in mathematics and information theory, influenced by Claude Shannon's work, and discusses her approach to curiosity-driven AI that mimics human curiosity to improve data collection and predictive analytics
Navigating the AI Landscape: Challenges and Innovations in Retail
SummaryIn this episode of the AI Engineering Podcast machine learning engineer Shashank Kapadia explores the transformative role of generative AI in retail. Shashank shares his journey from an engineering background to becoming a key player in ML, highlighting the excitement of understanding human behavior at scale through AI. He discusses the challenges and opportunities presented by generative A
The Anti-CRM CRM: How Spiro Uses AI to Transform Sales
SummaryIn this episode of the AI Engineering podcast Adam Honig, founder of Spiro AI, about using AI to automate CRM systems, particularly in the manufacturing sector. Adam shares his journey from running a consulting company focused on Salesforce to founding Spiro, and discusses the challenges of traditional CRM systems where data entry is often neglected. He explains how Spiro addresses this iss
Unlocking AI Potential with AMD's ROCm Stack
SummaryIn this episode of the AI Engineering podcast Anush Elangovan, VP of AI software at AMD, discusses the strategic integration of software and hardware at AMD. He emphasizes the open-source nature of their software, fostering innovation and collaboration in the AI ecosystem, and highlights AMD's performance and capability advantages over competitors like NVIDIA. Anush addresses challenges and
Applying AI To The Construction Industry At Buildots
SummaryIn this episode of the Machine Learning Podcast Ori Silberberg, VP of Engineering at Buildots, talks about transforming the construction industry with AI. Ori shares how Buildots uses computer vision and AI to optimize construction projects by providing real-time feedback, reducing delays, and improving efficiency. Learn about the complexities of digitizing the construction industry, the te
The Future of AI Systems: Open Models and Infrastructure Challenges
SummaryIn this episode of the AI Engineering Podcast Jamie De Guerre, founding SVP of product at Together.ai, explores the role of open models in the AI economy. As a veteran of the AI industry, including his time leading product marketing for AI and machine learning at Apple, Jamie shares insights on the challenges and opportunities of operating open models at speed and scale. He delves into the
The Rise of Agentic AI: Transforming Business Operations
SummaryIn this episode of the AI Engineering Podcast, host Tobias Macey sits down with Ben Wilde, Head of Innovation at Georgian, to explore the transformative impact of agentic AI on business operations and the SaaS industry. From his early days working with vintage AI systems to his current focus on product strategy and innovation in AI, Ben shares his expertise on what he calls the "continuum"
Protecting AI Systems: Understanding Vulnerabilities and Attack Surfaces
SummaryIn this episode of the AI Engineering Podcast Kasimir Schulz, Director of Security Research at HiddenLayer, talks about the complexities and security challenges in AI and machine learning models. Kasimir explains the concept of shadow genes and shadow logic, which involve identifying common subgraphs within neural networks to understand model ancestry and potential vulnerabilities, and emph
Understanding The Operational And Organizational Challenges Of Agentic AI
SummaryIn this episode of the AI Engineering podcast Julian LaNeve, CTO of Astronomer, talks about transitioning from simple LLM applications to more complex agentic AI systems. Julian shares insights into the challenges and considerations of this evolution, emphasizing the importance of starting with simpler applications to build operational knowledge and intuition. He discusses the parallels bet
The Power of Community in AI Development with Oumi
SummaryIn this episode of the AI Engineering Podcast Emmanouil (Manos) Koukoumidis, CEO of Oumi, about his vision for an open platform for building, evaluating, and deploying AI foundation models. Manos shares his journey from working on natural language AI services at Google Cloud to founding Oumi with a mission to advance open-source AI, emphasizing the importance of community collaboration and
Arch Gateway: Add AI To Your Apps Without Custom Development
SummaryIn this episode of the AI Engineering Podcast Adil Hafiz talks about the Arch project, a gateway designed to simplify the integration of AI agents into business systems. He discusses how the gateway uses Rust and Envoy to provide a unified interface for handling prompts and integrating large language models (LLMs), allowing developers to focus on core business logic rather than AI complexit
The Role Of Synthetic Data In Building Better AI Applications
SummaryIn this episode of the AI Engineering Podcast Ali Golshan, co-founder and CEO of Gretel.ai, talks about the transformative role of synthetic data in AI systems. Ali explains how synthetic data can be purpose-built for AI use cases, emphasizing privacy, quality, and structural stability. He highlights the shift from traditional methods to using language models, which offer enhanced capabilit
Optimize Your AI Applications Automatically With The TensorZero LLM Gateway
SummaryIn this episode of the AI Engineering podcast Viraj Mehta, CTO and co-founder of TensorZero, talks about the use of LLM gateways for managing interactions between client-side applications and various AI models. He highlights the benefits of using such a gateway, including standardized communication, credential management, and potential features like request-response caching and audit loggin
Harnessing The Engine Of AI
SummaryIn this episode of the AI Engineering Podcast Ron Green, co-founder and CTO of KungFu AI, talks about the evolving landscape of AI systems and the challenges of harnessing generative AI engines. Ron shares his insights on the limitations of large language models (LLMs) as standalone solutions and emphasizes the need for human oversight, multi-agent systems, and robust data management to sup
The Complex World of Generative AI Governance
SummaryIn this episode of the AI Engineering Podcast Jim Olsen, CTO of ModelOp, talks about the governance of generative AI models and applications. Jim shares his extensive experience in software engineering and machine learning, highlighting the importance of governance in high-risk applications like healthcare. He explains that governance is more about the use cases of AI models rather than the
Building Semantic Memory for AI With Cognee
SummaryIn this episode of the AI Engineering Podcast, Vasilije Markovich talks about enhancing Large Language Models (LLMs) with memory to improve their accuracy. He discusses the concept of memory in LLMs, which involves managing context windows to enhance reasoning without the high costs of traditional training methods. He explains the challenges of forgetting in LLMs due to context window limit
The Impact of Generative AI on Software Development
SummaryIn this episode of the AI Engineering Podcast, Tanner Burson, VP of Engineering at Prismatic, talks about the evolving impact of generative AI on software developers. Tanner shares his insights from engineering leadership and data engineering initiatives, discussing how AI is blurring the lines of developer roles and the strategic value of AI in software development. He explores the current
ML Infrastructure Without The Ops: Simplifying The ML Developer Experience With Runhouse
SummaryMachine learning workflows have long been complex and difficult to operationalize. They are often characterized by a period of research, resulting in an artifact that gets passed to another engineer or team to prepare for running in production. The MLOps category of tools have tried to build a new set of utilities to reduce that friction, but have instead introduced a new barrier at the tea
Building AI Systems on Postgres: An Inside Look at pgai Vectorizer
SummaryWith the growth of vector data as a core element of any AI application comes the need to keep those vectors up to date. When you go beyond prototypes and into production you will need a way to continue experimenting with new embedding models, chunking strategies, etc. You will also need a way to keep the embeddings up to date as your data changes. The team at Timescale created the pgai Vect
Running Generative AI Models In Production
SummaryIn this episode Philip Kiely from BaseTen talks about the intricacies of running open models in production. Philip shares his journey into AI and ML engineering, highlighting the importance of understanding product-level requirements and selecting the right model for deployment. The conversation covers the operational aspects of deploying AI models, including model evaluation, compound AI,
Enhancing AI Retrieval with Knowledge Graphs: A Deep Dive into GraphRAG
SummaryIn this episode of the AI Engineering podcast, Philip Rathle, CTO of Neo4J, talks about the intersection of knowledge graphs and AI retrieval systems, specifically Retrieval Augmented Generation (RAG). He delves into GraphRAG, a novel approach that combines knowledge graphs with vector-based similarity search to enhance generative AI models. Philip explains how GraphRAG works by integrating
Harnessing Generative AI for Effective Digital Advertising Campaigns
SummaryIn this episode of the AI Engineering podcast Praveen Gujar, Director of Product at LinkedIn, talks about the applications of generative AI in digital advertising. He highlights the key areas of digital advertising, including audience targeting, content creation, and ROI measurement, and delves into how generative AI is revolutionizing these aspects. Praveen shares successful case studies o
Building Scalable ML Systems on Kubernetes
SummaryIn this episode of the AI Engineering podcast, host Tobias Macy interviews Tammer Saleh, founder of SuperOrbital, about the potentials and pitfalls of using Kubernetes for machine learning workloads. The conversation delves into the specific needs of machine learning workflows, such as model tracking, versioning, and the use of Jupyter Notebooks, and how Kubernetes can support these tasks.
Expert Insights On Retrieval Augmented Generation And How To Build It
SummaryIn this episode we're joined by Matt Zeiler, founder and CEO of Clarifai, as he dives into the technical aspects of retrieval augmented generation (RAG). From his journey into AI at the University of Toronto to founding one of the first deep learning AI companies, Matt shares his insights on the evolution of neural networks and generative models over the last 15 years. He explains how RAG a
Barking Up The Wrong GPTree: Building Better AI With A Cognitive Approach
SummaryArtificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains h
Build Your Second Brain One Piece At A Time
SummaryGenerative AI promises to accelerate the productivity of human collaborators. Currently the primary way of working with these tools is through a conversational prompt, which is often cumbersome and unwieldy. In order to simplify the integration of AI capabilities into developer workflows Tsavo Knott helped create Pieces, a powerful collection of tools that complements the tools that develop
Strategies For Building A Product Using LLMs At DataChat
SummaryLarge Language Models (LLMs) have rapidly captured the attention of the world with their impressive capabilities. Unfortunately, they are often unpredictable and unreliable. This makes building a product based on their capabilities a unique challenge. Jignesh Patel is building DataChat to bring the capabilities of LLMs to organizational analytics, allowing anyone to have conversations with
Improve The Success Rate Of Your Machine Learning Projects With bizML
SummaryMachine learning is a powerful set of technologies, holding the potential to dramatically transform businesses across industries. Unfortunately, the implementation of ML projects often fail to achieve their intended goals. This failure is due to a lack of collaboration and investment across technological and organizational boundaries. To help improve the success rate of machine learning pro
Using Generative AI To Accelerate Feature Engineering At FeatureByte
SummaryOne of the most time consuming aspects of building a machine learning model is feature engineering. Generative AI offers the possibility of accelerating the discovery and creation of feature pipelines. In this episode Colin Priest explains how FeatureByte is applying generative AI models to the challenge of building and maintaining machine learning pipelines.AnnouncementsHello and welcome t
Learn And Automate Critical Business Workflows With 8Flow
SummaryEvery 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 aut
Considering The Ethical Responsibilities Of ML And AI Engineers
SummaryMachine 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 nece
Build Intelligent Applications Faster With RelationalAI
SummaryBuilding 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, simplifyi
Building Better AI While Preserving User Privacy With TripleBlind
SummaryMachine 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 current
Enhancing The Abilities Of Software Engineers With Generative AI At Tabnine
SummarySoftware 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
Validating Machine Learning Systems For Safety Critical Applications With Ketryx
SummarySoftware 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 t
Applying Declarative ML Techniques To Large Language Models For Better Results
SummaryLarge 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.AnnouncementsHello and welcome to the
Surveying The Landscape Of AI and ML From An Investor's Perspective
SummaryArtificial 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 fi
Applying Federated Machine Learning To Sensitive Healthcare Data At Rhino Health
SummaryA 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 capabili
Using Machine Learning To Keep An Eye On The Planet
SummarySatellite 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. ICE
The Role Of Model Development In Machine Learning Systems
SummaryThe 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 s
Real-Time Machine Learning Has Entered The Realm Of The Possible
SummaryMachine 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 ha
How Shopify Built A Machine Learning Platform That Encourages Experimentation
SummaryShopify 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 th
Applying Machine Learning To The Problem Of Bad Data At Anomalo
SummaryAll 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 buildin
Build More Reliable Machine Learning Systems With The Dagster Orchestration Engine
SummaryBuilding 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 Ryz
Solve The Cold Start Problem For Machine Learning By Letting Humans Teach The Computer With Aitomatic
SummaryMachine 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
Convert Your Unstructured Data To Embedding Vectors For More Efficient Machine Learning With Towhee
SummaryData 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 a
Shedding Light On Silent Model Failures With NannyML
SummaryBecause 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 actu
How To Design And Build Machine Learning Systems For Reasonable Scale
SummaryUsing 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 fo
Building A Business Powered By Machine Learning At Assembly AI
SummaryThe 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.AnnouncementsH
Update Your Model's View Of The World In Real Time With Streaming Machine Learning Using River
SummaryThe 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 co
Using AI To Transform Your Business Without The Headache Using Graft
SummaryMachine 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 Ca
Accelerate Development And Delivery Of Your Machine Learning Projects With A Comprehensive Feature Platform
SummaryIn 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
Build Better Models Through Data Centric Machine Learning Development With Snorkel AI
SummaryMachine 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 learni
Declarative Machine Learning For High Performance Deep Learning Models With Predibase
SummaryDeep 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 learn
Stop Feeding Garbage Data To Your ML Models, Clean It Up With Galileo
SummaryMachine learning is a force multiplier that can generate an outsized impact on your organization. Unfortunately, if you are feeding your ML model garbage data, then you will get orders of magnitude more garbage out of it. The team behind Galileo experienced that pain for themselves and have set out to make data management and cleaning for machine learning a first class concern in your workf
Build Better Machine Learning Models With Confidence By Adding Validation With Deepchecks
SummaryMachine learning has the potential to transform industries and revolutionize business capabilities, but only if the models are reliable and robust. Because of the fundamental probabilistic nature of machine learning techniques it can be challenging to test and validate the generated models. The team at Deepchecks understands the widespread need to easily and repeatably check and verify the
Build A Full Stack ML Powered App In An Afternoon With Baseten
SummaryBuilding an ML model is getting easier than ever, but it is still a challenge to get that model in front of the people that you built it for. Baseten is a platform that helps you quickly generate a full stack application powered by your model. You can easily create a web interface and APIs powered by the model you created, or a pre-trained model from their library. In this episode Tuhin Sri
Introducing The Show
Hello, and welcome to the Machine Learning Podcast. I’m your host, Tobias Macey. You might know me from the Data Engineering Podcast or the Python Podcast.__init__. If you work with machine learning and AI, or you’re curious about it and want to learn more, then this show is for you. We’ll go beyond the esoteric research and flashy headlines and find out how machine learning is making an impact on
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