Show Notes

• (1:45) Willem discussed his undergraduate degree in Mechatronic Engineering at Stellenbosch University in the early 2010s.
• (2:34) Willem recalled his entrepreneurial journey founding and selling a networking startup that provides internet access to private residents on campus.
• (5:37) Willem worked for two years as a Software Engineer focusing on data systems at Systems Anywhere in Capetown after college.
• (6:49) Willem talked about his move to Bangkok working as a Senior Software Engineer at INDEFF, a company in industrial control systems.
• (9:52) Willem went over his decision to join Gojek, a leading Indonesian on-demand multi-service platform and digital payment technology group.
• (12:16) Willem mentioned the engineering challenges associated with building complex data systems for super-apps.
• (14:50) Willem dissected Gojek’s ML platform, including these four solutions for various stages of the ML life cycle: Clockwork, Merlin, Feast, and Turing.
• (19:24) Willem recapped the lessons from designing the ML platform to meet Gojek’s scaling requirements — as delivered at Cloud Next 2018.
• (23:09) Willem briefly went through the key design components to incorporate Kubeflow pipelines into Gojek’s existing ML platform — as delivered at KubeCon 2019.
• (26:21) Willem explained the inception of Feast, an open-source feature store that bridges the gap between data and models.
• (32:20) Willem talked about prioritizing the product roadmap and engaging the community for an open-source project.
• (35:07) Willem recapped the key lessons learned and envisioned Feast's future to be a lightweight modular feature store.
• (37:29) Willem explained the differences between commercial and open-source feature stores (given Tecton’s recent backing of Feast).
• (41:36) Willem reflected on his experience living and working in Southeast Asia.
• (44:33) Closing segment.

Willem’s Contact Info

• Twitter
• LinkedIn
• GitHub

Mentioned Content

Feast

• Feast Project website: feast.dev
• Feast Slack community: #Feast
• Feast Documentation: docs.feast.dev
• Feast GitHub repository: feast-dev/feast
• Feast on StackOverflow: stackoverflow.com/questions/tagged/feast
• Feast Wiki: wiki.lfaidata.foundation/display/FEAST/Feast+Home
• Feast Twitter: @feast_dev

Article

• An Introduction to Gojek’s Machine Learning Platform (2019)
• Introducing Feast: An Open-Source Feature Store For Machine Learning (2019)
• A State of Feast (2020)
• Why Tecton is Backing The Feast Open-Source Feature Store (2020)

Talks

• Lessons Learned Scaling Machine Learning at GoJek on Google Cloud (Cloud Next 2018)
• Accelerating Machine Learning App Development with Kubeflow Pipelines (Cloud Next 2019)
• Moving People and Products with Machine Learning on Kubeflow (KubeCon 2019)

People

• David Aronchick (Open-Source ML Strategy at Azure, Ex-PM for Kubernetes at Google, Co-Founder of Kubeflow, Advisor to Tecton)
• Jeremy Lewi (Principal Engineer at Primer.ai, Co-Founder of Kubeflow)
• Felipe Hoffa (Developer Advocate for BigQuery, Data Cloud Advocate for Snowflake)

Book

• Cal Newport’s “Deep Work”

Willem will be a speaker at Tecton’s apply() virtual conference (April 21-22, 2021) for data and ML teams to discuss the practical data engineering challenges faced when building ML for the real world. Participants will share best practice development patterns, tools of choice, and emerging architectures they use to successfully build and manage production ML applications. Everything is on the table from managing labeling pipelines, to transforming features in real-time, and serving at scale. Register for free now: https://www.applyconf.com/!

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Show Notes

• (1:56) Jim went over his education at Trinity College Dublin in the late 90s/early 2000s, where he got early exposure to academic research in distributed systems.
• (4:26) Jim discussed his research focused on dynamic software architecture, particularly the K-Component model that enables individual components to adapt to a changing environment.
• (5:37) Jim explained his research on collaborative reinforcement learning that enables groups of reinforcement learning agents to solve online optimization problems in dynamic systems.
• (9:03) Jim recalled his time as a Senior Consultant for MySQL.
• (9:52) Jim shared the initiatives at the RISE Research Institute of Sweden, in which he has been a researcher since 2007.
• (13:16) Jim dissected his peer-to-peer systems research at RISE, including theoretical results for search algorithm and walk topology.
• (15:30) Jim went over challenges building peer-to-peer live streaming systems at RISE, such as GradientTV and Glive.
• (18:18) Jim provided an overview of research activities at the Division of Software and Computer Systems at the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology.
• (19:04) Jim has taught courses on Distributed Systems and Deep Learning on Big Data at KTH Royal Institute of Technology.
• (22:20) Jim unpacked his O’Reilly article in 2017 called “Distributed TensorFlow,” which includes the deep learning hierarchy of scale.
• (29:47) Jim discussed the development of HopsFS, a next-generation distribution of the Hadoop Distributed File System (HDFS) that replaces its single-node in-memory metadata service with a distributed metadata service built on a NewSQL database.
• (34:17) Jim rationalized the intention to commercialize HopsFS and built Hopsworks, an user-friendly data science platform for Hops.
• (36:56) Jim explored the relative benefits of public research money and VC-funded money.
• (41:48) Jim unpacked the key ideas in his post “Feature Store: The Missing Data Layer in ML Pipelines.”
• (47:31) Jim dissected the critical design that enables the Hopsworks feature store to refactor a monolithic end-to-end ML pipeline into separate feature engineering and model training pipelines.
• (52:49) Jim explained why data warehouses are insufficient for machine learning pipelines and why a feature store is needed instead.
• (57:59) Jim discussed prioritizing the product roadmap for the Hopswork platform.
• (01:00:25) Jim hinted at what’s on the 2021 roadmap for Hopswork.
• (01:03:22) Jim recalled the challenges of getting early customers for Hopsworks.
• (01:04:30) Jim intuited the differences and similarities between being a professor and being a founder.
• (01:07:00) Jim discussed worrying trends in the European Tech ecosystem and the role that Logical Clocks will play in the long run.
• (01:13:37) Closing segment.

Jim’s Contact Info

• Logical Clocks
• Twitter
• LinkedIn
• Google Scholar
• Medium
• ACM Profile
• GitHub

Mentioned Content

Research Papers

• “The K-Component Architecture Meta-Model for Self-Adaptive Software” (2001)
• “Dynamic Software Evolution and The K-Component Model” (2001)
• “Using feedback in collaborative reinforcement learning to adaptively optimize MANET routing” (2005)
• “Building Autonomic Systems Using Collaborative Reinforcement Learning” (2006)
• “Improving ICE Service Selection in a P2P System using the Gradient Topology” (2007)
• “gradienTv: Market-Based P2P Live Media Streaming on the Gradient Overlay” (2010)
• “GLive: The Gradient Overlay as a Market Maker for Mesh-Based P2P Live Streaming” (2011)
• “HopsFS: Scaling Hierarchical File System Metadata Using NewSQL Databases” (2016)
• “Scaling HDFS to More Than 1 Million Operations Per Second with HopsFS” (2017)
• “Hopsworks: Improving User Experience and Development on Hadoop with Scalable, Strongly Consistent Metadata” (2017)
• “Implicit Provenance for Machine Learning Artifacts” (2020)
• “Time Travel and Provenance for Machine Learning Pipelines” (2020)
• “Maggy: Scalable Asynchronous Parallel Hyperparameter Search” (2020)

Articles

• “Distributed TensorFlow” (2017)
• “Reflections on AWS’s S3 Architectural Flaws” (2017)
• “Meet Michelangelo: Uber’s Machine Learning Platform” (2017)
• “Feature Store: The Missing Data Layer in ML Pipelines” (2018)
• “What Is Wrong With European Tech Companies?” (2019)
• “ROI of Feature Stores” (2020)
• “MLOps With A Feature Store” (2020)
• “ML Engineer Guide: Feature Store vs. Data Warehouse” (2020)
• “Unifying Single-Host and Distributed Machine Learning with Maggy” (2020)
• “How We Secure Your Data With Hopsworks” (2020)
• “One Function Is All You Need For ML Experiments” (2020)
• “Hopsworks: World’s Only Cloud-Native Feature Store, now available on AWS and Azure” (2020)
• “Hopsworks 2.0: The Next Generation Platform for Data-Intensive AI with a Feature Store” (2020)
• “Hopsworks Feature Store API 2.0, a new paradigm” (2020)
• “Swedish startup Logical Clocks takes a crack at scaling MySQL backend for live recommendations” (2021)

Projects

• Apache Hudi (by Uber)
• Delta Lake (by Databricks)
• Apache Iceberg (by Netflix)
• MLflow (by Databricks)
• Apache Flink (by The Apache Foundation)

People

• Leslie Lamport (The Father of Distributed Computing)
• Jeff Dean (Creator of MapReduce and TensorFlow, Lead of Google AI)
• Richard Sutton (The Father of Reinforcement Learning — who wrote “The Bitter Lesson”)

Programming Books

• C++ Programming Languages books (by Scott Meyers)
• “Effective Java” (by Joshua Bloch)
• “Programming Erlang” (by Joe Armstrong)
• “Concepts, Techniques, and Models of Computer Programming” (by Peter Van Roy and Seif Haridi)

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Show Notes

• (2:20) Pier shared his college experience at the University of Southampton studying Electronic Engineering.
• (3:46) For his final undergraduate project, Pier developed a suite of games and used machine learning to analyze brainwaves data that can classify whether a child is affected or not by autism.
• (11:26) Pier went over his favorite courses and involvement with the AI Society during his additional year at the University of Southampton to get a Master’s in Artificial Intelligence.
• (13:40) For his Master’s thesis called “Causal Reasoning in Machine Learning,” Pier created and deployed a suite of Agent-Based and Compartmental Models to simulate epidemic disease developments in different types of communities.
• (26:51) Pier went over his stints as a developer intern at Fidessa and a freelance data scientist at Digital-Dandelion.
• (29:21) Pier reflected on his time (so far) as a data scientist at SAS Institute, where he helps their customers solve various data-driven challenges using cloud-based technologies and DevOps processes.
• (33:37) Pier discussed the key benefits that writing and editing technical content for Towards Data Science to his professional development.
• (36:31) Pier covered the threads that he kept pulling with his blog posts.
• (38:50) Pier talked about his Augmented Reality Personal Business Card created in HTML using the AR.js library.
• (41:12) Pier brought up data structures in two other impressive JavaScript projects using TensorFlow.js and ml5.js.
• (44:19) Pier went over his experience working with data visualization tools such as Plotly, R Shiny, and Streamlit.
• (47:27) Pier talked about his work on a chapter for a book called “Applied Data Science in Tourism” that is going to be published with Springer this year.
• (48:37) Pier shared his thoughts regarding the tech community in London.
• (49:19) Closing segment.

Pier’s Contact Info

• Website
• LinkedIn
• Twitter
• GitHub
• Medium
• Patreon
• Kaggle

Mentioned Content

• “Alleviate Children’s Health Issues Through Games and Machine Learning”
• “Causal Reasoning in Machine Learning”
• Andrej Karpathy (Director of AI and Autopilot at Tesla)
• Cassie Kozyrkov (Chief Decision Scientist at Google)
• Iain Brown (Head of Data Science at SAS)
• “The Book Of Why” (By Judea Pearl)
• “Pattern Recognition and Machine Learning” (by Christopher Bishop)

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Timestamps

• (1:55) Alba shared her background growing up interested in studying Physics and pivoting into quantum mechanics.
• (3:33) Alba went over her Bachelor’s in Fundamental Physics at The University of Barcelona.
• (4:54) Alba continued her education with an M.S. degree that specialized in Particle Physics and Gravitation.
• (6:40) Alba started her Ph.D. in Physics in 2015 and discussed her first publication, “Operational Approach to Bell Inequalities: Application to Qutrits.”
• (9:48) Alba also spent time as a visiting scholar at the University of Oxford and the University of Madrid during her Ph.D.
• (11:25) Alba explained her second paper to understand the connection between maximal entanglement and the fundamental symmetries of high-energy physics.
• (13:27) Alba dissected her next work titled “Multipartite Entanglement in Spin Chains and The Hyperdeterminant.”
• (18:56) Alba shared the origin of Quantic, a quantum computation joint effort between the University of Barcelona and the Barcelona Supercomputing Center.
• (22:27) Alba unpacked her article “Quantum Computation: Playing The Quantum Symphony,” making a metaphor between quantum computing and musical symphony.
• (27:47) Alba discussed the motivation and contribution of her paper “Exact Ising Model Simulation On A Quantum Computer.”
• (32:51) Alba recalled creating a tutorial that ended up winning the Teach Me QISKit challenge from IBM back in 2018.
• (35:01) Alba elaborated on her paper “Quantum Circuits For the Maximally Entangled States,” which designs a series of quantum circuits that generate absolute maximally entangled states to benchmark a quantum computer.
• (38:54) Alba dissected key ideas in her paper “Data Re-Uploading For a Universal Quantum Classifier.”
• (43:51) Alba explained how she leveled up her knowledge of classical neural networks.
• (47:40) Alba shared her experience as a Postdoctoral Fellow at The Matter Lab at the University of Toronto — working on quantum machine learning and variational quantum algorithms (checked out the Quantum Research Seminars Toronto that she has been organizing).
• (52:18) Alba explained her work on the Meta-Variational Quantum Eigensolver algorithm capable of learning the ground state energy profile of a parametrized Hamiltonian.
• (59:23) Alba went over Tequila, a development package for quantum algorithms in Python that her group created.
• (01:04:49) Alba presented a quantum calling for new algorithms, applications, architectures, quantum-classical interface, and more (as presented here).
• (01:08:59) Alba has been active in education and public outreach activities about encouraging scientific vocations for young minds, especially in Catalonia.
• (01:12:07) Closing segment.

Her Contact Info

• Website
• Twitter
• LinkedIn
• Google Scholar
• GitHub

Her Recommended Resources

• Ewin Tang (Ph.D. Student in Theoretical Computer Science at the University of Washington)
• Alán Aspuru-Guzik (Professor of Chemistry and Computer Science at the University of Toronto, Alba’s current supervisor)
• José Ignacio Latorre (Professor of Theoretical Physics at the University of Barcelona, Alba’s former supervisor)
• Quantum Computation and Quantum Information (by Michael Nielsen and Isaac Chuang)
• Quantum Field Theory and The Standard Model (by Matthew Schwarz)
• The Structure of Scientific Revolutions (by Thomas Kuhn)
• Against Method (by Paul Feyerabend)
• Quantum Computing Since Democritus (by Scott Aaronson)

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Timestamps

• (2:07) JY discussed his college time studying Computer Science and Applied Math at Ecole Polytechnique — a leading French institute in science and technology.
• (3:04) JY reflected on time at Stanford getting a Master’s in Management Science and Engineering, where he served as a Teaching Assistant for CS 229 (Machine Learning) and CS 246 (Mining Massive Datasets).
• (6:14) JY walked over his ML engineering internship at LiveRamp — a data connectivity platform for the safe and effective use of data.
• (7:54) JY reflected on his next three years at Databricks, first as a software engineer and then as a tech lead for the Spark Infrastructure team.
• (10:00) JY unpacked the challenges of packaging/managing/monitoring Spark clusters and automating the launch of hundreds of thousands of nodes in the cloud every day.
• (14:48) JY shared the founding story behind Data Mechanics, whose mission is to give superpowers to the world's data engineers so they can make sense of their data and build applications at scale on top of it.
• (18:09) JY explained the three tenets of Data Mechanics: (1) managed and serverless, (2) integrated into clients’ workflows, and (3) built on top of open-source software (read the launch blog post).
• (22:06) JY unpacked the core concepts of Spark-On-Kubernetes and evaluated the benefits/drawbacks of this new deployment mode — as presented in “Pros and Cons of Running Apache Spark on Kubernetes.”
• (26:00) JY discussed Data Mechanics’ main improvements on the open-source version of Spark-On-Kubernetes — including an intuitive user interface, dynamic optimizations, integrations, and security — as explained in “Spark on Kubernetes Made Easy.”
• (28:35) JY went over Data Mechanics Delight, a customized Spark UI which was recently open-sourced.
• (35:40) JY shared the key ideas in his thought-leading piece on how to be successful with Apache Spark in 2021.
• (38:42) JY went over his experience going through the Y Combinator program in summer 2019.
• (40:56) JY reflected on the key decisions to get the first cohort of customers for Data Mechanics.
• (42:26) JY shared valuable hiring lessons for early-stage startup founders.
• (44:34) JY described the data and tech community in France.
• (47:19) Closing segment.

His Contact Info

• Twitter
• LinkedIn
• Data Mechanics

His Recommended Resources

• Jure Leskovec (Associate Professor of Computer Science at Stanford / Chief Scientist at Pinterest)
• Jeff Bezos (Founder of Amazon)
• Matei Zaharia (CTO of Databricks and creator of Apache Spark)
• “Designing For Data-Intensive Applications” (by Martin Kleppmann)

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Timestamps

• (2:55) Chris went over his experience studying Computer Science at the University of Southern California for undergraduate in the late 90s.
• (5:26) Chris recalled working as a Software Engineer at NASA Jet Propulsion Lab in his sophomore year at USC.
• (9:54) Chris continued his education at USC with an M.S. and then a Ph.D. in Computer Science. Under the guidance of Dr. Nenad Medvidović, his Ph.D. thesis is called “Software Connectors For Highly-Distributed And Voluminous Data-Intensive Systems.” He proposed DISCO, a software architecture-based systematic framework for selecting software connectors based on eight key dimensions of data distribution.
• (16:28) Towards the end of his Ph.D., Chris started getting involved with the Apache Software Foundation. More specifically, he developed the original proposal and plan for Apache Tika (a content detection and analysis toolkit) in collaboration with Jérôme Charron to extract data in the Panama Papers, exposing how wealthy individuals exploited offshore tax regimes.
• (24:58) Chris discussed his process of writing “Tika In Action,” which he co-authored with Jukka Zitting in 2011.
• (27:01) Since 2007, Chris has been a professor in the Department of Computer Science at USC Viterbi School of Engineering. He went over the principles covered in his course titled “Software Architectures.”
• (29:49) Chris touched on the core concepts and practical exercises that students could gain from his course “Information Retrieval and Web Search Engines.”
• (32:10) Chris continued with his advanced course called “Content Detection and Analysis for Big Data” in recent years (check out this USC article).
• (36:31) Chris also served as the Director of the USC’s Information Retrieval and Data Science group, whose mission is to research and develop new methodology and open source software to analyze, ingest, process, and manage Big Data and turn it into information.
• (41:07) Chris unpacked the evolution of his career at NASA JPL: Member of Technical Staff -> Senior Software Architect -> Principal Data Scientist -> Deputy Chief Technology and Innovation Officer -> Division Manager for the AI, Analytics, and Innovation team.
• (44:32) Chris dove deep into MEMEX — a JPL’s project that aims to develop software that advances online search capabilities to the deep web, the dark web, and nontraditional content.
• (48:03) Chris briefly touched on XDATA — a JPL’s research effort to develop new computational techniques and open-source software tools to process and analyze big data.
• (52:23) Chris described his work on the Object-Oriented Data Technology platform, an open-source data management system originally developed by NASA JPL and then donated to the Apache Software Foundation.
• (55:22) Chris shared the scientific challenges and engineering requirements associated with developing the next generation of reusable science data processing systems for NASA’s Orbiting Carbon Observatory space mission and the Soil Moisture Active Passive earth science mission.
• (01:01:05) Chris talked about his work on NASA’s Machine Learning-based Analytics for Autonomous Rover Systems — which consists of two novel capabilities for future Mars rovers (Drive-By Science and Energy-Optimal Autonomous Navigation).
• (01:04:24) Chris quantified the Apache Software Foundation's impact on the software industry in the past decade and discussed trends in open-source software development.
• (01:07:15) Chris unpacked his 2013 Nature article called “A vision for data science” — in which he argued that four advancements are necessary to get the best out of big data: algorithm integration, development and stewardship, diverse data formats, and people power.
• (01:11:54) Chris revealed the challenges of writing the second edition of “Machine Learning with TensorFlow,” a technical book with Manning that teaches the foundational concepts of machine learning and the TensorFlow library's usage to build powerful models rapidly.
• (01:15:04) Chris mentioned the differences between working in academia and industry.
• (01:16:20) Chris described the tech and data community in the greater Los Angeles area.
• (01:18:30) Closing segment.

His Contact Info

• Wikipedia
• NASA Page
• Google Scholar
• USC Page
• Twitter
• LinkedIn
• GitHub

His Recommended Resources

• Doug Cutting (Founder of Lucene and Hadoop)
• Hilary Mason (Ex Data Scientist at bit.ly and Cloudera)
• Jukka Zitting (Staff Software Engineer at Google)
• "The One Minute Manager" (by Ken Blanchard and Spencer Johnson)

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Show Notes

• (2:09) Marcello described his academic experience getting a Master’s Degree in Computer Science from the Universita di Catania in the early 2000s, where his thesis is called Evolutionary Randomized Graph Embedder.
• (6:14) Marcello commented on his career phase working as a web developer across various places in Europe.
• (9:18) Marcello discussed his time working as a software engineer at INPS, a government-owned company that now handles most Italian citizens' pubic-related data.
• (10:42) Marcello talked about his time as a data visualization engineer at SwiftIQ. He created a data visualization library that allows the inclusion of dynamic charts in HTML pages with just a few JavaScript lines.
• (13:40) Marcello went over his projects while working as a full-stack software engineer for Twitter’s User Services Engineering team in Dublin.
• (17:19) Marcello reflected on his time at Microsoft Zurich’s Social and Engagement team, contributing to machine learning infrastructure and tools.
• (21:28) Marcello briefly touched on his one-year stint at Apple Zurich as a Senior Applied Research Engineer.
• (23:49) Marcello talked about the challenges while writing “Algorithms and Data Structures in Action,” which introduces a diverse range of algorithms used in web apps, systems programming, and data manipulation.
• (27:11) Marcello expanded upon part 1 of the book, including advanced data structures such as D-ary Heaps, Randomized Treaps, Bloom Filters, Disjoint Sets, Tries/Radix Trees, and Cache.
• (34:51) Marcello brought up data structures to perform efficient multi-dimensional queries, including various nearest neighbor searches and clustering techniques, in part 2 of the book.
• (39:21) Marcello briefly described the algorithms in part 3 of the book — graph embeddings, gradient descent, simulated annealing, and genetic algorithms.
• (48:28) Marcello talked about his work on jsgraph — a lightweight library to model graphs, run graphs algorithms, and display them on screen.
• (52:06) Marcello compared Python, Java, and JavaScript programming languages.
• (54:13) Marcello discussed his current interest in quantum computing.
• (56:18) Marcello shared his thoughts regarding Dublin, Zurich, and Rome's tech communities.
• (57:37) Closing segment.

His Contact Info

• Twitter
• LinkedIn
• GitHub
• Blog

His Recommended Resources

• "Algorithms and Data Structures in Action" (Marcello's book with Manning)
• Andrew Ng
• Geoffrey Hinton
• Francois Chollet
• "Scalability Rules" (by Martin Abbott and Michael Fischer)

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Show Notes

• (2:10) Dave talked briefly about his Electrical Engineering study at Rensselaer Polytechnic Institute back in the late 90s.
• (4:03) Dave commented on his career phase working as a software engineer across various companies in Bozeman, Montana.
• (7:38) Dave discussed his work as a senior architect and tech lead at Expero, a Houston-based startup that develops custom software exclusively for domain-expert users.
• (11:26) Dave briefly defined common big data frameworks (Hadoop, Apache Spark) and databases (Apache Cassandra, Apache Kafka).
• (13:37) Dave went over the challenges during his time as a chief software architect at Gene by Gene, a biotech company focusing on DNA-based ancestry and genealogy.
• (20:00) Dave shared the common patterns and anti-patterns of using graph databases (in reference to his talk “A Practical Guide to Graph Databases”).
• (26:16) Dave walked through the three categories of graph technologies: Graph Computing Engine, RDF TripleStore, and Labeled Property Graph (in reference to his talk “A Skeptics Guide to Graph Databases”).
• (33:03) Dave discussed his move to DataStax’s Global Graph Practice team as a solutions architect and graph database subject matter expert.
• (36:00) Dave explained the design of DataStax’s enterprise solution called Customer 360, which collapses data silos to drive business value.
• (41:16) Dave talked about his current experience as a Senior Graph Architect at AWS.
• (43:51) Dave mentioned the challenges while writing "Graph Databases In Action" (published last October).
• (47:25) Dave explained the open-source Apache TinkerPop framework and the Gremlin language used in the book for the uninitiated.
• (51:04) Dave discussed trends in big data and distributed systems that he is most excited about.
• (55:06) Closing segment.

His Contact Info

• Website
• Twitter
• LinkedIn
• GitHub

His Recommended Resources

• "Graph Databases In Action" (Associated Code Repository)
• Martin Fowler (Founder of ThoughtWorks)
• Martin Kleppmann (Author of "Designing Data-Intensive Applications")
• Andrew Ng (Professor at Stanford, Co-Founder of Google Brain and Coursera, Ex-Chief Scientist at Baidu)
• "Pragmatic Programmer" (by Andy Hunt and Dave Thomas)
• "The Five Dysfunctions Of A Team" (by Patrick Lencioni)
• "How To Observe Scientific Advice for Common Real-World Problems" (by Randall Munroe)

This is the 40% discount code that is good for all Manning's products in all formats: poddcast19.

These are 5 free eBook codes, each good for one copy of "Graph Databases In Action":

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Show Notes

• (2:13) Jason went over his experience studying Computer Science at Loyola College in Baltimore for undergraduate, where he got an early exposure to academic research in image registration.
• (4:31) Jason described his graduate school experience at John Hopkins University, where he completed his Ph.D. on “Techniques for Vision-Based Human-Computer Interaction” that proposed the Visual Interaction Cues paradigm.
• (9:31) During his time as a Post-Doc Fellow at UCLA, Jason helped develop automatic segmentation and recognition techniques for brain tumors to improve the accuracy of diagnosis and treatment accuracy
• (14:27) From 2007 to 2014, Jason was a professor in the Computer Science and Engineering department at SUNY-Buffalo. He covered the content of two graduate-level courses on Bayesian Vision and Intro to Pattern Recognition that he taught.
• (18:20) On the topic of metric learning, Jason proposed an approach to data analysis and modeling for computer vision called "Active Clustering."
• (21:35) On the topic of image understanding, Jason created Generalized Image Understanding - a project that examined a unified methodology that integrates low-, mid-, and high-level elements for visual inference (equivalent to image captioning today).
• (24:51) On the topic of video understanding, Jason worked on ISTARE: Intelligent Spatio-Temporal Activity Reasoning Engine, whose objective is to represent, learn, recognize, and reason over activities in persistent surveillance videos.
• (27:46) Jason dissected Action Bank - a high-level representation of activity in video, which comprises of many individual action detectors sampled broadly in semantic space and viewpoint space.
• (35:30) Jason unpacked LIBSVX - a library of super voxel and video segmentation methods coupled with a principled evaluation benchmark based on quantitative 3D criteria for good super voxels.
• (40:06) Jason gave an overview of AI research activities at the University of Michigan, where he was a professor of Electrical Engineering and Computer Science from 2014 to 2020.
• (41:09) Jason covered the problems and projects in his graduate-level courses on Foundations of Computer Vision and Advanced Topics in Computer Vision at Michigan.
• (44:56) Jason went over his recent research on video captioning and video description.
• (47:03) Jason described his exciting software called BubbleNets, which chooses the best video frame for a human to annotate.
• (51:44) Jason shared anecdotes of Voxel51's inception and key takeaways that he has learned.
• (01:05:25) Jason talked about Voxel51's Physical Distancing Index that tracks the coronavirus global pandemic's impact on social behavior.
• (01:07:47) Jason discussed his exciting new chapter as the new director of the Stevens Institute for Artificial Intelligence.
• (01:11:28) Jason identified the differences and similarities between being a professor and being a founder.
• (01:14:55) Jason gave his advice to individuals who want to make a dent in AI research.
• (01:16:14) Jason mentioned the trends in computer vision research that he is most excited about at the moment.
• (01:17:23) Closing segment.

His Contact Info

• Wikipedia
• Google Scholar
• Website
• Twitter
• LinkedIn

His Recommended Resources

• Bubblenets: Video Object Segmentation for Computer Vision
• Voxel51's FiftyOne Open-Sourced Library
• Jeff Siskind (Professor at Purdue University)
• CJ Taylor (Professor at the University of Pennsylvania)
• Kristen Grauman (Professor at the University of Austin)
• "An Introduction to Mathematical Statistics"

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Show Notes

• (2:23) Barr discussed growing up in Israel and serving as a commander of the Data Analytics unit at the Israeli Air Force.
• (4:10) Barr reflected on her college experience at Stanford studying Math and Computational Science.
• (7:24) Barr walked over the two career lessons learned from being a Management Consultant at Bain and Company.
• (9:51) Barr reflected on her time as VP of Customer Operations at Gainsight, which offers enterprise solutions for Customer Success and Product teams. She helped build and scale a global team covering various functions such as business operations, customer success, professional services.
• (12:32) Barr unpacked the notion of data downtime, introduced in her blog post “The Rise of Data Downtime.”
• (17:25) Barr unveiled the four main steps in the data reliability maturity curve: reactive, proactive, automated, and scalable - as indicated in “Closing The Data Downtime Gap."
• (21:09) Barr shared the founding story behind Monte Carlo, whose mission is to accelerate the world’s adoption of data by reducing data downtime.
• (24:29) Barr explained the five pillars of data observability.
• (27:45) Barr unpacked the rise of data catalogs as a powerful tool for data governance, along with the three categories of data catalog solutions that data teams are adopting - as presented in “What We Got Wrong About Data Governance.”
• (31:32) Barr discussed the benefits of using Data Mesh - a type of data platform architecture that embraces data ubiquity in the enterprise by leveraging a domain-oriented, self-serve design.
• (37:28) Barr went over a framework that looks at the business functions and the nature of the work to score the impact and allocate the ROI of the data team - as proposed in "Measuring the ROI of Your Data Organization."
• (40:39) Barr shared five practices for designing a platform that maximizes data's value and impact inside an organization.
• (43:27) Barr reflected on the key decisions to get the first cohort of customers for Monte Carlo.
• (46:31) Barr shared valuable hiring lessons.
• (48:48) Barr went over helpful resources throughout her journey as a founder.
• (50:13) Barr dropped the final advice for founders on seeking the right investors.
• (51:18) Closing segment.

Her Contact Info

• Twitter
• LinkedIn
• Medium
• Monte Carlo

Her Recommended Resources

• Stanford's "Mathematics and Magic Tricks" course (taught by Persi Diaconis)
• "The Biggest Bluff" by Maria Konnikova
• Snowflake
• DJ Patil (Former U.S. Chief Data Scientist)
• Monte Carlo's Customers

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit datacast.substack.com/subscribe