Shows
WeblineIndia | Tech Talks Unplugged: Code, Coffee, Collaborate, and CreateThe AutoML Advantage: Faster, Cheaper, More Accessible AIDiscover how AutoML is transforming the future of AI—making it faster, more affordable, and accessible to all. In this episode, we explore how automation is reshaping machine learning development and its impact across industries.
What’s covered in this podcast:
What AutoML means and why it matters
Key benefits: speed, cost-efficiency, and accessibility
Real-world use cases and business advantages
How AutoML supports better, smarter AI models
Whether you're a tech enthusiast or a business leader exploring AI possibilities, this episode is for you.
Looking to implement smart AI/ML solutions tailored to your...2025-05-2110 min
The AutoML PodcastNyckel - Building an AutoML StartupOscar Beijbom is talking about what it's like to run an AutoML startup: Nyckel. Beyond that, we chat about the differences between academia and industry, what truly matters in application and more.Check out Nyckel at: https://www.nyckel.com/2025-03-071h 20
AI with ShailyAutoML Unleashed: Will It Replace Data Scientists?In this engaging episode of "AI with Shaily," host Shailendra Kumar dives into the fascinating world of AutoML, exploring its rising significance across various industries and the ongoing debate about its potential to replace data scientists. 🎙️✨
Shailendra opens the discussion by highlighting the current buzz surrounding AutoML, particularly its transformative impact on sectors like finance and healthcare. He paints a vivid picture of banks and hospitals, once bogged down by extensive paperwork, now thriving with AI-driven decision-making processes. This sets the stage for a thought-provoking question: Could AutoML make data scientists obsolete? 🤔
The conversation takes a lively turn as Shailendra compares t...2025-02-2602 min
AI with ShailyAutoML Unleashed: The Future of Machine Learning Without the CodeWelcome to another episode of "AI with Shaily!" 🎉 Your host, Shailendra Kumar, is here to guide you through the exciting world of artificial intelligence. Today, we're diving into the intriguing concept of Automated Machine Learning, or AutoML. This area is full of innovation and potential, making it a hot topic in the tech world.
Imagine being at a lively tech expo, where every booth is showcasing flashy technology and jargon. In the midst of all that, there's a simple stand promising machine learning without the need for complex coding. That's the essence of AutoML! 🚀 The market for AutoML is booming, with p...2025-02-2503 min
CyberSecurity SummaryAutomated Machine Learning with Microsoft Azure: Build highly accurate and scalable end-to-end AI solutions with Azure AutoMLThis Book is an excerpt from Dennis Sawyers' Book, "Automated Machine Learning with Microsoft Azure," which provides a comprehensive guide to using automated machine learning (AutoML) on Microsoft's Azure platform. The book covers AutoML concepts, implementation using Azure Machine Learning Studio and Python, building various AutoML models (regression, classification, and forecasting), and deploying these models for real-time and batch scoring solutions. It also explores integrating AutoML with other Microsoft tools like Azure Data Factory and explains strategies for achieving better model performance and gaining end-user trust. Finally, the book includes details about the book's authors and publishing information.2025-02-2337 min
Data Plumbers UnpluggedTransforming the Future of Feature Engineering with AutoML: A Collaborative JourneyThis podcast explores how advanced AutoML tools can augment human expertise in feature engineering, fostering innovation while maintaining critical thinking. Whether you're a seasoned developer or just starting out, this episode offers insights into navigating these powerful tools and understanding their limitations. Let’s explore the future of AI together—where human ingenuity meets machine learning at unprecedented scales. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit dataplumbers.substack.com2025-01-3108 min
Industrial AI PodcastAutoML: Did we promise ourselves too much?We spoke with Prof. Dr. Marius Lindauer, one of Europe's AutoML experts. We ask ourselves, what does AutoML need and where are new applications?
Thanks for listening. We welcome suggestions for topics, criticism and a few stars on Apple, Spotify and Co.
Our guest: Prof. Dr. Marius Lindauer
We thank our partner HANNOVER MESSE2025-01-3025 min
AI HOUSE PodcastВикористання AutoML: від Формули-1 до води в Африці | Юрій Гуц, DataRobot | AI HOUSE Podcast #31У 31-му епізоді AI HOUSE Podcast багато говоримо про AutoML. Адже в гостях Юрій Гуц — Principal ML Engineer у DataRobot.
Юрій працює в компанії вже 8 років і займається ядром машинного навчання. Він розповів, що таке AutoML, які у нього перспективи, в чому його відмінність від Data Engineering, які найважливіші риси гарного AutoML-продукту та як поява LLM вплинула на AutoML.
Також він поділився цікавими кейсами застосування AutoML, як от: виявлення синдикату російських хакерів, predictive maintenance для підтримки свердловини питної води у Сьєрра-Леоне та покращення спортивних результатів для команди Формули-1 McLaren.
Долучайтесь до нашого AI-ком’юніті: https://t.me/AIHOUSE
Посилання на благодійну організацію «Реактивна пошта»: https://reactivepost.org/
AI HOUSE — це найбільше та найпотужніше АІ-комʼюніті в Україні. Ми обмінюємося досвідом і знаннями, здобуваємо навички, реалізовуємо нові технологічні та бізнесові ідеї, розвиваємо індустрію ШІ та сприяємо народженню продуктових AI-стартапів вдома, в Україні.
Більше про нас тут: https://bit.ly/m/AI-HOUSE2024-12-051h 12The AutoML PodcastNeural Architecture Search: Insights from 1000 PapersColin White, head of research at Abacus AI, takes us on a tour of Neural Architecture Search: its origins, important paradigms and the future of NAS in the age of LLMs. If you're looking for a broad overview of NAS, this is the podcast for you!2024-12-031h 15
The Thinking Machine ShowShaping the Future of AI: Forward-Forward, LowCoder, and AutoMLIn this episode, we dive deep into cutting-edge tools transforming AI and machine learning development. Discover how the Forward-Forward algorithm compares to traditional backpropagation, explore LowCoder's innovative blend of visual programming and natural language interfaces, and uncover the potential of AutoML in democratizing AI. Finally, we speculate on the convergence of these technologies and their implications for the future of AI. Perfect for researchers, developers, and tech enthusiasts shaping tomorrow’s AI landscape!source --> AI for Low-Code for AI Hosted on Acast. See acast.com/privacy for more information.2024-11-2419 min
What's Up with Tech?Revolutionizing Semiconductor Industry with AI: STMicroelectronics' Edge Computing Strategy, AutoML Advancements, and Future InnovationsInterested in being a guest? Email us at admin@evankirstel.comEver wondered how artificial intelligence is transforming the very core of the semiconductor industry? Find out with Marc Dupaquier from STMicroelectronics as he unveils the incredible journey of integrating AI into various sectors. You'll gain insight into how the acquisition of Cartesian propelled STMicroelectronics into the forefront of AutoML for embedded AI, making products smarter and more efficient. From enhancing predictive maintenance in industrial equipment to revolutionizing safety features in consumer tools, Marc breaks down the strategies driving this shift towards edge computing, a move that...2024-11-0126 minThe AutoML PodcastQuick-Tune: Quickly Learning Which Pretrained Model to Finetune and HowThere are so many great foundation models in many different domains - but how do you choose one for your specific problem? And how can you best finetune it? Sebastian Pineda has an answer: Quicktune can help select the best model and tune it for specific use cases. Listen to find out when this will be a Huggingface feature and if hyperparameter optimization is even important in finetuning models (spoiler: very much so)!2024-08-0853 minThe AutoML PodcastDiscovering Temporally-Aware Reinforcement Learning AlgorithmsDesigning algorithms by hand is hard, so Chris Lu and Matthew Jackson talk about how to meta-learn them for reinforcement learning. Many of the concepts in this episode are interesting to meta-learning approaches as a whole, though: "how expressive can we be and still perform well?", "how can we get the necessary data to generalize?" and "how do we make the resulting algorithm easy to apply in practice?" are problems that come up for any learning-based approach to AutoML and some of the topics we dive into.2024-06-2451 min
Papers Read on AILightAutoML: AutoML Solution for a Large Financial Services EcosystemWe present an AutoML system called LightAutoML developed for a large European financial services company and its ecosystem satisfying the set of idiosyncratic requirements that this ecosystem has for AutoML solutions. Our framework was piloted and deployed in numerous applications and performed at the level of the experienced data scientists while building high-quality ML models significantly faster than these data scientists. We also compare the performance of our system with various general-purpose open source AutoML solutions and show that it performs better for most of the ecosystem and OpenML problems. We also present the lessons that we learned while developing...2024-05-2754 minThe AutoML PodcastX Hacking: The Threat of Misguided AutoMLAutoML can be a tool for good, but there are pitfalls along the way. Rahul Sharma and David Selby tell us about how AutoML systems can be used to give us false impressions about explainability metrics of ML systems - maliciously, but also on accident. While this episode isn't talking about a new exciting AutoML method, it can tell us a lot about what can go wrong in applying AutoML and what we should think about when we build tools for ML novices to use.2024-05-2754 minThe AutoML PodcastIntroduction To New Co-Host, Theresa EimerIn today's episode, we're introducing the very special Theresa Eimer to the show. Theresa will be taking over the hosting of many of the future episodes. Theresa has already recorded multiple episodes and we are stoked to air those shortly.We also spend a few moments explaining my relative absence in the last few months (since the war in the middle east erupted) and what I'm up to now.Theresa, we are all so excited to be doing this together!To learn more about Theresa,Follow her on Twitter here...2024-05-2713 min
Industrial AI PodcastBeckhoff launches an AutoML toolRobert Weber talked to Dr. Fabian Bause from Beckhoff about their new AutoML tool. The first step will focus on vision, followed by timeseries.
Thanks for listening. We welcome suggestions for topics, criticism and a few stars on Apple, Spotify and Co.
We thank our partner HANNOVER MESSE
https://www.hannovermesse.de/de/
Our guest: Dr. Fabian Bause more2024-04-2226 min![[I\'ML]](https://podster.fm/image-storage/158844/conversions/n1jy6gKNrtlzw8x0-3xl.jpg)
[I'ML][I'ML] AutoML в банкингеКак ML и DS помогают строить финансовое будущее людям и крупным банкам? Выясняем в выпуске про автоматизированное машинное обучение.
Обсуждаем области применения AutoML в банках: персональные ассистенты, CV, документооборот, скоринги и боты для торговли на биржах. Рассматриваем Python-библиотеки для факторного анализа, LAMA и других задач, в которых хорош AutoML.
Гость выпуска:
Андрей Сухань — CDO. Больше 10 лет использует цифры, чтобы искать неочевидное и объяснять происходящее в бизнесе простыми словами
Ведущий:
Александр Толмачев — директор по машинному обучению и анализу данных в Ozon Fintech.2024-04-0358 min
Industrial AI PodcastIndustrial AI and AutoML - 2 days "AI in the forest"Together with 30 decision-makers from industry, we spent two days discussing AutoML and TabPFN with Prof. Dr. Frank Hutter and his team and Prof. Dr. Marco Huber and discovered quite a few new approaches.
Thanks for listening. We welcome suggestions for topics, criticism and a few stars on Apple, Spotify and Co.
We thank our partner Siemens
AI and patents
https://videos.insa-strasbourg.fr/aiard-artificial-intelligence-augmented-rd/
**OUR EVENT IN JANUARY **
https://www.hannovermesse.de/de/rahmenprogramm/special-events/ki-in-der-industrie/
We thank our team:
Barbara, Anne and Simon!2023-11-0230 min
momit.fm49. Roblox知育 / コマンドブロックとChatGPT / AutoML Translation卒乳最近のロブロックス事情ライブトピアビルドアボートビルドアボートの知育面マイクラのコマンドブロックexecuteコマンドのコマンド変換をChatGPTで生成した話AutoML Translation子ども向けテキストの翻訳の難しさカスタム翻訳モデルTeachable Machineクリーニング店の無人AI受け付けの話※※訂正: エピソードで触れているクリーニング店のAIの話は”TensorFlow”の誤りでした。お詫びして訂正いたします。フィードバックは #momitfm で募集しています 📣Podcastクライアントでのフォロー、レビューもお待ちしております 📣See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.2023-09-2538 min
TAG Data TalkSuccessfully Leveraging AutoML to Solve Complex ProblemsIn this episode of TAG Data Talk, Dr. Beverly Wright discusses with Chad Harness:
describe automl to the business person
how do these types of tools work
what are the right ways to leverage automl
expectations for the future of automl tools
2023-09-0721 minThe AutoML PodcastAutoGluon: The StoryToday we're talking with Nick Erickson from AutoGluon.We discuss AutoGluon's fascinating origin story, its unique point of view, the science and engineering behind some of its unique contributions, Amazon's Machine Learning University, AutoGluon's multi-layer stack ensembler in all its detail, their feature preprocessing pipeline, their feature type inference, their adaptive approach to early stopping, controlling for inference speeds, the different multi-modal architectures, the ML culture at Amazon, the unique challenges of time series, the role of competitions, the decision to reject hyperparameter optimization, benchmarking in AutoML, what the research community can do to help industry along...2023-09-053h 13
AI Today PodcastAI Today Podcast: AI Glossary Series – Automated Machine Learning (AutoML)Many organizations want to do AI, but the technical skills needed can present a challenge. Not all organizations have data scientists on hand. Yet, many organizations still want to benefits of AI. In recent years there have been tools and platforms created to help automate many of the aspects of building and developing ML models that previously required very specialized skills and talent.Continue reading AI Today Podcast: AI Glossary Series – Automated Machine Learning (AutoML) at Cognilytica.2023-07-1909 min
Láncreakció#116. Elveszi-e az AutoML a munkánkat?Lemerültünk az AutoML csomagok mélyére, hogy felhozzuk onnan a nemtudás gyöngyeit. A Gyula által megversenyeztetett csomagok (PyCaret, H2O, TPOT) focis adatbázisokon (pl. FiveThirtyEight) dolgoztak és versenybe szálltak a korábban emberi csapatoknak kiírt adattudós-verseny babérjaiért, de nem áruljuk el, hanyadikok lettek. Bezzeg az adásban!2023-07-0637 minThe AutoML PodcastHow to Integrate Logic and Argumentation into Human-Centric AutoMLToday we're talking with Joseph Giovanelli about his work on integrating logic and argumentation into AutoML systems.Joseph is a PhD student at the University of Bologna. He was more recently in Hannover working on ethics and fairness with Marius’ team.The paper he published presents his framework, HAMLET, which stands for Human-centric AutoML via Logic and Argumentation. It allows a user to iteratively specify constraints in a formal manner and, once defined, those constraints become logical premises. Those premises, when combined together, can produce conflicts with one another, thereby reducing the search space and pr...2023-06-2643 min
LeewayHertzDecoding AutoML: Unleashing the future of machine learningAutomated Machine Learning (AutoML) is an innovation that has reshaped the landscape of machine learning, democratizing its potential by automating the intricate, labor-intensive, and expertise-requiring processes involved.
Click here for more information: https://www.leewayhertz.com/automl/
2023-06-0853 min
LeewayHertzDecoding AutoML: Unleashing the future of machine learningAutomated Machine Learning (AutoML) is an innovation that has reshaped the landscape of machine learning, democratizing its potential by automating the intricate, labor-intensive, and expertise-requiring processes involved.
Click here for more information: https://www.leewayhertz.com/automl/2023-06-0853 minThe AutoML PodcastHow to Design an AutoML System using Error DecompositionToday we're talking with Caitlin Owen, a post-doc at the University of Otago about her work on error decomposition.She recently published a paper titled "Towards Explainable AutoML Using Error Decomposition" about how a more granular view of the components of error can lead the construction of better AutoML systems. Read her paper here: https://link.springer.com/chapter/10.1007/978-3-031-22695-3_13Follow her on Twitter here: @CaitAshfordOwenConnect with her on LinkedIn here: https://www.linkedin.com/in/caitlin-owen-5b9b08193/2023-06-0428 min
AI Unraveled: Latest AI News & Trends, ChatGPT, Gemini, DeepSeek, Gen AI, LLMs, AI Ethics & BiasLatest AI Trends May 23rd: Why does Geoffrey Hinton believe that AI learns differently than humans?, When will AI surpass Facebook and Twitter as the major sources of fake news?, Is AI Enhancing or Limiting Human Intelligence?Why does Geoffrey Hinton believe that AI learns differently than humans?AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams: 3 Practice Exams, Data Engineering, Exploratory Data Analysis, Modeling, Machine Learning Implementation and Operations, NLP;Is Meta AI's Megabyte architecture a breakthrough for Large Language Models (LLMs)?What does Google's new Generative AI Tool, Product Studio, offer?What is the essence of the webinar on Running LLMs performantly on CPUs Utilizing Pruning and Quantization?When will AI surpass Facebook and Twitter as the major sources of fake news?AI...2023-05-2414 minThe AutoML PodcastThe Semantic Layer and AutoMLToday we're talking with Gaurav Rao, the EVP & GM of Machine Learning and AI at AtScale, a company centered around the semantic layer.For some time now, I've been feeling that there is a deep connection between a formal articulation of business context and the realization of the dream of AutoML, so I searched for people in the space who can help shine light on this direction.Gaurav is one of the few who can speak about this. As you'll hear, he's extremely pedagogic and he's walking us through the origins of the concept, how...2023-05-1657 min
GOTO - The Brightest Minds in TechHow AutoML & Low Code Empowers Data Scientists • Linda Stougaard Nielsen & Moez AliThis interview was recorded for GOTO Unscripted at GOTO Copenhagen. gotopia.techRead the full transcription of this interview hereLinda Stougaard Nielsen - Director of Data Science & Data Engineering at AVA WomenMoez Ali - Creator of PyCaretDESCRIPTIONOver the past decade, AutoML has revolutionized the world of data science, propelling it several layers forward in terms of abstraction. This powerful technology has paved the way for a new era of democratization, empowering experts from all fields to harness the power of data through the concept...2023-05-1215 minThe AutoML PodcastFoundation Models: The term and its originsToday Ankush Garg is speaking with Rishi Bommasani, PhD student at Stanford and one of the originator of the term Foundation Models.They’re talking about the origins of the term Foundation Model, which he and his group advanced, in the paper "On the Opportunities and Risks of Foundation Models".They’ll talk about self-supervision, issues of scale, the motivation behind the terminology, the origins of the Research for Foundation Models Institute at Stanford, outcome homogenization, emergence and phase transitions, and some of the social consequences to look out for.Thank you both for...2023-04-291h 10The AutoML PodcastThe Business and Engineering of AutoML Products with Raymond PeckToday we're talking with Raymond Peck, a senior engineer and director in the AutoML space. He spent time at H2O, dotData, Alteryx and many other places.This is a fascinating conversation about the business, engineering, and science of machine learning automation in production. Learning about his experience is crucial for understanding the biography of the space.We discuss the early motivations behind AutoML, the initial value propositions that propelled the first movers in the market, the market dynamics that operated in the early days, the evolution of the relevant engineering and science, how customers...2023-04-062h 01
Secrets of Data Analytics LeadersAutoML And Declarative Machine Learning: Comparing Use Cases - Audio BlogAutoML and the emerging approach of declarative ML help simplify the process of creating and refining ML models.
Published at:
https://www.eckerson.com/articles/automl-and-declarative-machine-learning-comparing-use-cases2023-04-0610 minThe AutoML PodcastTabPFN: A Revolution in AutoML?Today we’re talking to Noah Hollmann and Samuel Muller about their paper on TabPFN - which is an incredible spin on AutoML based on Bayesian inference and transformers.[Quick note on audio quality]: Some of the tracks have not recorded perfectly but I felt that the content there was too important not to release. Sorry for any ear-strain!In the episode, we spend some time discussing posterior predictive probabilities before discussing how exactly they’ve pre-fitted their network, how they got their training data, what the network looks like, and how the system is perf...2023-03-021h 16The AutoML PodcastHow financial institutions manage model riskToday we’re talking to Sean Sexton, the Director of Modeling and Analytics Consulting at KPMG, about the role of models in financial institutions and how the risks associated with them is managed.This turned out to be an incredibly deep and interesting topic, and we really only scratched the surface of it.Sean has a unique ability to summarize developments in an entire space. If you're interested to learn more about modeling in financial institutions and about the history of how we got here, you should definitely study his dissertation, on managing model risk, he...2023-02-071h 12The AutoML PodcastHow to solve dynamical systems by fusing data and mechanismToday we’re talking to Matt Levine. Matt is a PhD student in computing and mathematical sciences at Caltech, and he focuses on improving the prediction and inference of physical systems by blending together both mechanistic modeling and machine learning.This episode is one of my favorites: we go pretty deep into dynamical systems, and into Matt's new framework for solving them by blending traditional, mechanistic, approaches with machine learning. This is a fascinating use of machine learning, and hopefully gets us one step closer to the automation of science, in general.A Fra...2023-01-121h 09
Data-poddenAvsnitt 33 - Alla Cloud Warehouse-releaser under 2022 och en titt på AutoMLTHE STATE OF CLOUD DATA WAREHOUSES – 2022 EDITIONhttps://www.recordlydata.com/blog/the-state-of-cloud-data-warehouses-2022-editionFyra cio:er om 2023: Ekonomisk osäkerhet och cyberhot sätter avtryckhttps://cio.idg.se/2.1782/1.774386/fyra-cioer-om-2023-ekonomisk-osakerhet-och-cyberhot-satter-avtryckAutoML- The Future of Machine Learninghttps://insidebigdata.com/2022/12/28/automl-the-future-of-machine-learning/ Hosted on Acast. See acast.com/privacy for more information.2023-01-1257 minThe AutoML PodcastDASH: How to Search Over ConvolutionsToday we’re chatting with Junhong Shen, a PhD student at Carnegie Mellon.Junhong and her team are working on the generalizability of NAS algorithms across a diverse set of tasks.Today we'll be talking about DASH, a NAS algorithm that takes diversity of tasks at its center. In order to implement DASH, Junhong and her team implemented three clever ideas that she'll share with us.Efficient Architecture Search for Diverse Tasks - https://arxiv.org/pdf/2204.07554.pdfTackling Diverse Tasks with Neural Architecture Search - https://blog.ml.cmu.edu/2022/10/14/ta...2022-12-201h 18
Деньги любят техноСколько дата-сайентистов может заменить AutoMLКак необходимо развиваться сегодня, чтобы AutoML не заменил вас завтра?
О практической и философской стороне AutoML, изменениях в роли специалиста в Data Science, прошлом и будущем построения моделей и возможностях Искусственного интеллекта рассуждают профи — Денис Суржко, начальник управления перспективных алгоритмов машинного обучения ВТБ и Алексей Натёкин, диктатор ODS.2022-12-1346 min
Деньги любят техноСколько дата-сайентистов может заменить AutoMLКак необходимо развиваться сегодня, чтобы AutoML не заменил вас завтра?
О практической и философской стороне AutoML, изменениях в роли специалиста в Data Science, прошлом и будущем построения моделей и возможностях Искусственного интеллекта рассуждают профи — Денис Суржко, начальник управления перспективных алгоритмов машинного обучения ВТБ и Алексей Натёкин, диктатор ODS.2022-12-1346 minThe AutoML PodcastHuman-Centered AutoML: The New ParadigmToday we're speaking with Marius Lindauer and it is certainly one of my favorite episodes!As you’ll hear, Marius is full of ideas for where AutoML systems can and should go. These ideas are crystallized in a blog-post, published here: https://www.automl.org/rethinking-automl-advancing-from-a-machine-centered-to-human-centered-paradigm/If you’re searching for research directions, this conversation left me with dozens of ideas. Marius and his team are doing phenomenal work to make AutoML systems more trustworthy and more human-centric.We will be reviewing content from the following papers:Bayesian Optimization with a Prior for...2022-12-031h 10The AutoML PodcastBERT-Sort: How to use language models to semantically order categorical valuesToday Ankush Garg is talking to Mehdi Bahrami about his recent project: BERT-Sort.BERT-Sort is an example of how large language models can add useful context to tabular datasets, and to AutoML systems.Mehdi is a Member of Research Staff at Fujitsu and, as he describes, he began using AutoML systems for his research, yet he came across some crucial limitations of existing solutions. The modifications he made highlight a promising future for the relationship between language models and AutoML. This is a direction we're going to continue to explore on the show....2022-11-2440 min
Super Data Science: ML & AI Podcast with Jon Krohn627: AutoML: Automated Machine LearningJon Krohn speaks with Erin LeDell, H2O.ai’s Chief Machine Learning Scientist. They investigate how AutoML supercharges the data science process, the importance of admissible machine learning for an equitable data-driven future, and what Erin’s group Women in Machine Learning & Data Science is doing to increase inclusivity and representation in the field.This episode is brought to you by Datalore (datalore.online/SDS), the collaborative data science platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.In this episode you will learn:• The H2O Auto...2022-11-151h 30The AutoML PodcastSAT: The Peculiar Origins of AutoMLIn today's episode, we’re talking to Lars Kothoff about the fascinating origin story of AutoML (as he sees it), and how it emerged from the SAT community.While talking to many of you, it became clear that this origin story is one that a lot of people have some vague sense about, but not a very concrete knowledge of so hopefully this episode can help to flesh out the narrative with greater clarity.We'll be discussing his survey paper from 2016, "Algorithm selection for combinatorial search problems: A survey", to be found here: ht...2022-11-121h 09The AutoML PodcastHow to use evolutionary strategies for online AutoMLToday we’re talking to Cedric Kulbach about online learning, the challenges of doing it properly, why it is so promising, how it’s connected to evolutionary strategies, and recent advances in the field that can help to unlock these promises.We then discuss the close connection between online learning and AutoML systems, and we explore a recent framework that he recently published, called EvoAutoML.Cedric is a PhD student at Karlsruhe Institute of Technology, in Germany, where was defending his thesis 2 days after we spoke!You can find his paper here: https://hal...2022-10-171h 01
xHUB.AIARCHIVO xTALKS.AI #11 JAVIER MANCILLA : Inteligencia Artificial. Quantum ML. Quantum Coaching. AutoML. Business. Alma. Futuro.En esta xTALK.AI tenemos el placer de entrevistar a Javier Mancilla Quantum Machine Learning Leader. Hablamos como ya es típico en nuestras xTALKS.AI de muchos temas como el estado actual de la inteligencia artificial, ML, QML, computación cuántica, AutoML, impacto social y económico, de business y del alma... de super inteligencias y del destino de la humanidad.Otra xTALK.AI que esperamos sea épica e histórica. Disfrutadla tanto como nosotros al realizarla.Un placer tener el placer de escuchar al maestro Javier.Javier Mancilla Montero : Lin...2022-10-061h 35The AutoML PodcastA Narration of The Bitter LessonThis short episode is a narration of Richard Sutton's The Bitter Lesson.Richard Sutton is a distinguished research scientist at DeepMind and a professor of computing science at the University of Alberta. He is considered one of the founders of modern computational reinforcement learning, having several significant contributions to the field, including temporal difference learning and policy gradient methods.If you haven't yet read his piece, it is definitely worth reading. This episode is a narration of it.I would like to invite more discussion about these thoughts, so if you know of...2022-09-2607 minThe AutoML PodcastExamining Tabular Deep LearningMore drama in the contest between traditional machine learning models and deep learning models when it comes to tabular data.We have on the show Vadim Borisov, a research fellow at the University of Tubingen as well as Kathrin Sessler, a PhD student from the same university.This episode will be led by Ankush Garg, exploring Vadim and Kathrin's recent paper “Deep Neural Networks and Tabular Data: A Survey”.Paper: https://arxiv.org/pdf/2110.01889.pdfSAINT paper: https://arxiv.org/abs/2106.01342Deep learning architectures and the hippocampus paper: https://arxiv.org/abs...2022-09-1942 minThe AutoML PodcastThe Paths to AGIToday we’re speaking with Jeff Clune about a new path towards general artificial intelligence, that he calls AI Generating Algorithms (AI-GAs).Jeff is a Professor of Computer Science at the University of British Columbia. He was previously a Senior Research Scientist at Uber AI, and more recently a Research Team Leader at OpenAI. He's currently a Faculty Member at the Vector Institute.We’re going to be talking about whether it is even possible that humans never create AGI, the enormous potential upside of AGI, the risks of AGI (including existential risks), Jeff’s fascin...2022-09-061h 12The AutoML PodcastHow to evaluate a metalearning systemToday I'm speaking with Jan N. van Rijn about metalearning.Jan is an assistant professor at Leiden University, where he also did his PhD. He is one of the founders of the OpenML Foundation, he previously did a post-doc at Freiburg in the Frank Hutter lab, and he is one of the authors of the metalearning book, which we'll be discussing.We’ll be primarily examining the contents of their chapter titled “Evaluating Recommendations of Metalearning/AutoML Systems”.We'll be covering such topics as OpenML and metalearning, how the space of AutoML has change...2022-08-291h 09The AutoML PodcastActive Dendrites: Brain-inspired multi-task learningToday we’re speaking with three researchers: Karan Grewal, Abhi Iyer and Akash Velu, about multi-task learning and how their new brain-inspired approach can help tackle it.We’ll be discussing what a task is, what exactly we mean by multi-task systems, distances between tasks, the difference between continual learning and multi-task learning, catastrophic forgetting, catastrophic interference and their causes, various approaches out there like context-dependent gating and synaptic intelligence, the role of scale, and sparsity, we’ll cover some basics of the brain like dendrites, proximal and distal dendrites, apical and basal dendrites, how active dendrites can he...2022-08-231h 08The AutoML PodcastSmart NAS via Co-Regulated Shaping ReinforcementToday I’m speaking with Mayukh Das about using neural architecture search for resource-constrained devices and about a new multi-objective reinforcement-learning based framework that he recently published called AUTOCOMET.We’ll be covering such topics as how NAS research is done both at Samsung and at Microsoft, the relationship between NAS and product teams, devices and the various types of constraints they expose, how to featurize hardware contexts, layer-wise latency calculations, surrogate models and the kinds of hardware-aware data they require, the current limitations of NAS, reinforcement learning and NAS, multi-objective optimization in the context of re...2022-08-081h 03
MLOps Weekly PodcastMLOps Week 7: Scaling AutoML with Nirman DaveFor week 7 of the MLOps Weekly Podcast, Simba sat down with Nirman Dave, Co-founder and CEO at Obviously AI, to discuss the practical and operational benefits of AutoML in production.2022-07-2623 minThe AutoML PodcastThe measures of intelligenceToday we’re speaking with José Hernández-Orallo. José is a Professor at the Polytechnic University of València in Spain and a Senior Research Fellow at the Leverhulme Centre for the Future of Intelligence, at Cambridge.We'll be covering an enormous amount of ground surrounding intelligence and its evaluation. We’ll touch on topics such as operating conditions in ML, agent characteristic curves,, the challenge with average performance scores, task-oriented evaluation, capability-oriented evaluation - in humans and other animals, animal-AI Olympics, meta-data annotation, performance robustness as a function of latent attributes, the limitations of aggregat...2022-07-251h 11The AutoML PodcastUpgrading human evaluators with assessor modelsToday I’m talking with Wout Schellaert about assessor models. Wout is a PhD student at the Polytechnic University of Valencia..We’ll be covering a lot of different topics, such as the distributional hypothesis in machine learning, evaluation criteria, the reductive nature of current evaluation methods, task systems, the desiderata of assessor models, how to build assessor models, when to use them, what happens when our models become increasingly complex, how assessor models can help with AI explainability, whether they can help against adversarial scenarios, the challenges of scoring, bias in human evaluators, how assessor models rela...2022-07-2459 minThe AutoML PodcastHow to explain using analogiesToday we’re talking to Karthi Ramamurthy about a novel approach to similarity learning explainability.Karthi is a research staff member in IBM Research at the Watson Research Center.He studies the relationship between humans, machines, data and the societal implications of machine learning.He was involved in the initial development of the open source AI Fairness 360 toolkit, where he’s still an active contributor.His papers won various best paper awards like the 2015 IEEE International Conference on Data Science and Advanced Analytics.He is an associate editor of Digi...2022-07-2458 minThe AutoML PodcastHow is NAS going to evolve?Today I’m speaking with Vasco Lopes, about the state of Neural Architecture Search, NAS, and about a new method that he published that takes a very creative look at how to do NAS.We’ll be discussing the motivation behind NAS, the current state of its deployment, the biggest use-cases today, the three components that make up NAS, the drawbacks to the current NAS paradigm, search spaces and how to design them, search strategies and how to choose them, graph representations of neural architectures, evaluation strategies and zero-cost approximations, bias in search space design, risks, the futu...2022-07-2451 minThe AutoML PodcastHow deep learning can be used for tabular datasetsToday I’m speaking with Yury Gorishniy about the state of the competition between Deep Learning and Gradient Boosted Decision Trees when it comes to tabular datasets, and about a recent paper he published that seems to take a stab at improving the state of deep learning on tabular datasets.We discuss whether or not there exists a gap between deep learning and gradient boosted decision trees, what the future of a gap might look like, and the extent to which the embedding of numerical features can give deep learning architectures a necessary boost in performance.2022-07-231h 25The AutoML PodcastWhen is missing data not a problem?Today we’ll be speaking with Julian Morimoto about missing data, its impact on the reliability of statistical inference, and two theorems that he recently discovered using concepts from real analysis about what guarantees we can expect, at the limit of arbitrarily large data sets.Julian has a background in math, and studied law at Harvard Law School and he speaks about the unique challenges of adopting machine learning in the legal world due to the various mechanisms of missingness in confidential documents.If you're interested in learning more about missing data and statistical inference, he...2022-07-2355 minThe AutoML PodcastWhy this showIn this episode, Adam introduces the show, the motivations for it, and why and how you should participate.2022-07-2304 minThe AutoML PodcastAre your experiments reproducible?Today we're speaking with Luigi Quaranta about the state of reproducibility in machine learning.Luigi published a taxonomy of support for reproducibility by various tools in the space and together we’re exploring the need for reproducibility, challenges and limitations, how to evaluate opportunities for improving your current systems, and what the future might hold.A few papers would be relevant here.The first is A Taxonomy of Tools for Reproducible MachineLearning Experiments: http://ceur-ws.org/Vol-3078/paper-81.pdfand the second is a study of how to make Ju...2022-07-2352 min
Vanishing Gradients9: AutoML, Literate Programming, and Data Tooling Cargo CultsHugo speaks with Hamel Husain, Head of Data Science at Outerbounds, with extensive experience in data science consulting, at DataRobot, Airbnb, and Github.
In this conversation, they talk about Hamel's early days in data science, consulting for a wide array of companies, such as Crocs, restaurants, and casinos in Las Vegas, diving into what data science even looked like in 2005 and how you could think about delivering business value using data and analytics back then.
They talk about his trajectory in moving to data science and machine learning in Silicon Valley, what his expectations were...2022-07-191h 41The AutoML PodcastManipulating Your ReputationIn this episode, Adam speaks with Doctor Torsten Ensslin about simulating reputation networks and their manipulation using Information Theory.Torsten is an Astrophysicist and cosmologist at the Max Plank institute, where he’s held many titles and positions. His current scientific work investigates theoretical cosmology and information field theory. As he discusses in this episode, Torsten co-created a simulation of how reputation propagates through a network of agents and how those agents can manipulate their communication strategies, through lies, targeted deceit, propaganda, and many other tactics, in an effort to achieve domination over their networks.2022-05-3057 minThe AutoML PodcastMulti-Objective AutoMLIn this episode, Adam discusses Multi-objective optimization with Laurent Parmentier.Laurent works at OVHCloud, most recently as a data scientist but previously in various software engineering roles. He published his thesis on AutoML at OVHCloud, and had previously released a paper titled TPOT-SH: A Faster Optimization Algorithm to Solve the AutoML Problem on Large Datasets.The conversation centers around two classic papers in AutoML:Multi-Objective Automatic Machine Learning with AutoxgboostMC by Pfisterer et al: https://arxiv.org/abs/1908.10796An ADMM Based Framework for AutoML Pipeline Configuration by Sijia Liu, Parikshit Yam et...2022-05-2348 minThe AutoML PodcastML Interpretability with Jessica SchrouffThis episode launches us into the deep waters of ML interpretability with Jessica Schrouff.Jessica is a Senior Research Scientist at Google Research working on machine learning for healthcare. Before joining Google in 2019, she was a postdoctoral fellow at University College London (UK) and Stanford University (USA), developing machine learning techniques for neuroscience discovery and clinical predictions.Throughout her career, her interests have lied not only in the technical advancement of machine learning methods, but also in critical aspects of their deployment such as their credibility, fairness, robustness or interpretability.As you’ll he...2022-05-161h 01
Adventures in Machine LearningAutoML Discovery and Approach - ML 070AutoML (automated machine learning) has become a hot topic over the past few years. Abid Ali Awan joins the show to share his approach to AutoML, when and how to utilize it compared to classic approaches. Ben and Abid also discuss open-source vs. proprietary platforms. What is AutoML? Automated machine learning provides methods and processes to make machine learning available for non-machine learning experts, to improve efficiency of machine learning and to accelerate research on machine learning. 2 levels of implementation: Blackbox AutoML can do one, or all of the things for feature selection with a statistical outset and self optimizing...2022-05-0443 minThe AutoML PodcastContinual Learning with Iman MirzadehThis is a conversation between data scientist Ankush Garg, from Telepath, and fourth-year Ph.D. student Iman Mirzadeh and they’ll be talking about Continual Learning and about Iman’s paper titled “Architecture Matters in Continual Learning”.Iman is interested in Artificial General Intelligence and so he’s researching systems that can over time develop increasingly more complex skills and a richer body of knowledge.Iman received his Bachelor’s degree in Computer Engineering from the University of Tehran and his Master’s degree from Washington State University and that’s where he’s currently a research assista2022-05-0233 minThe AutoML PodcastMLOps: Research and VisionToday we’re talking about MLOps - with our guide Georgios Symeonidis and we’ll be orienting around a recent paper he published titled “MLOps - Definitions, Tools and Challenges”.Georgios studied electrical and computer engineering at Democritus University of Thrace at Xanthi, in Greece. He specialized in information and electronics.He’s also worked as a research engineer at Athena Research and Innovation Center - studying ML systems in production. For his PhD, he’s researching Reliable Machine Learning pipelines for mature MLOps systems at International Hellenic University in Greece.2022-04-2742 minThe AutoML PodcastCurriculum Learning in AutoMLThis episode covers the relationship between Curriculum Learning and AutoML with Lucas Nildaimon dos Santos Silva.Lucas is a data scientist at americanas s.a., in Brazil, and is currently pursuing a Ph.D. in computer science from the Federal University of São Carlos where he also did his master’s. He’s researching machine learning and NLP and has recently published a paper on Curriculum Learning in AutoML systems, found below.https://ieeexplore.ieee.org/document/96801642022-03-0828 minThe AutoML PodcastStatistical Physics and Inference ProblemsIn this episode, we explore the relationship between Machine Learning and Statistical Mechanics with the guidance of Alia Abbara. This conversation centers around her PhD dissertation and we cover such topics as the relationship between statistics and physics, the long legacy of physics on machine learning, and the role of physical intuition in the future of machine learning.Find her original paper here: https://tel.archives-ouvertes.fr/tel-03497407/document2022-02-2734 min
Lights On Data ShowGetting Started with AutoMLDo you want to get started with autoML?
Join us and Nathan George, Author and Author & Data Scientist at Tink
What autoML is and what it is used for
What environments autoML works with
The projects/tasks recommended using autoML for
Best practices and advice
Plus, advice for those wanting to get into data science
Don't forget to check out Nathan George's book on "Practical Data Science with Python": https://packt.link/ngeorge2022-01-1441 min
The Engineered-Mind Podcast | Engineering, AI & TechnologyAutomated Machine Learning (AutoML) - Haifeng Jin | Podcast #66Haifeng Jin is a software engineer on Keras team at Google. He is the creator of AutoKeras, coauthor of Keras Tuner, and a contributor to Keras and TensorFlow. Haifeng got his Ph.D. in computer science at Texas A&M University. His research interest is automated machine learning (AutoML).
—————————————————————————————
🧠 Free Science Community: community.sci-circle.com
👉 Science Academy: academy.jousefmurad.com
📥 Weekly free science insights newsletter: jousef.substack.com
🐤 Follow me on Twitter: @jousefm2
📷 Follow me on Instagram: @jousefmrd
Feel free to support the podcast on Patreon: patreon.com/theengiineer2021-11-2844 min
The Erium Podcast – Data Science & Machine LearningDr. Markus Köster - Industrial Analytics mit Weidmüller Industrial AutoMLDr. Markus Köster verantwortet die Forschung und Entwicklung im Bereich Industrial Analytics bei Weidmüller. Mit „Weidmüller Industrial AutoML“ hat er gemeinsam mit seinem Team eine überdurchschnittlich erfolgreiche Machine Learning Lösung entwickelt. Doch wie funktioniert es, dass Maschinen- und Prozessexperten Machine Learning Modelle benutzen, ohne Vorkenntnisse in Data Science zu besitzen? Welche Entwicklungsentscheidungen und Teamaufstellungen waren dazu nötig? In „The Erium Podcast“ teilt Dr. Markus Köster dazu seine Erfahrungen und Erkenntnisse.
Dr. Markus Köster nimmt auch an unserem Data Science eMeetup am 30.11.2021 teil.
Hier geht`s zum eMeetup
Data Science eMe...2021-11-0248 min
The Python Podcast.__init__Making Automated Machine Learning More Accessible With EvalMLSummary
Building a machine learning model is a process that requires a lot of iteration and trial and error. For certain classes of problem a large portion of the searching and tuning can be automated. This allows data scientists to focus their time on more complex or valuable projects, as well as opening the door for non-specialists to experiment with machine learning. Frustrated with some of the awkward or difficult to use tools for AutoML, Angela Lin and Jeremy Shih helped to create the EvalML framework. In this episode they share the use cases for automated machine...2021-08-2545 min