Alexy Khrabrov
where else to find them
what they talk about
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where they keep going back
- Scale By the Bay5 editions2017–2023
- AI By the Bay2 editions2017–2025
all 7 in the filters
on a stage with
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who talks about the same things
- Tim Bonnemann5 talks
- Jonathan Starr8 talks
- Stephen Chin54 talks
- Daniel Izquierdo Cortázar16 talks
- Shino Iwami5 talks
- Gopi Krishnan Rajbahadur6 talks
| title | conference | date |
|---|---|---|
| Full-Stack AI Engineering from AI-Native Coding to Knowledge, Models, Infrastructure & OSS | AI By the Bay 2025 | 2025-11-17 |
Conference lays out concrete claims and failures: modern dev tools like copilots and repet can produce clickable code that breaks test workflows when dynamic… | ||
| How to Write a Strong Conference Proposal? | AI By the Bay 2025 | 2025-11-17 |
Practical checklist: start with a one‑sentence thesis like X because Y results in Z, state the specific problem and the concrete takeaway, and name exact tools… | ||
| Making OSS AI Real with Knowledge Graphs | PyData NYC 2024 | 2024-11-06 |
Clear claim: combining Knowledge Graphs with retrieval (the GraphRAG pattern) gives grounded, long‑range context that pure vector RAG misses. Concrete tools… | ||
| Open-Source AI: Community is the Way. | Scale By the Bay 2023 | 2023-11-13 |
Chair of the new Generative AI Commons at Linux Foundation for AI & Data announces an open-badging plan (bronze/silver/gold) tied to weights, training scripts… | ||
| PANEL - Data, Code, and Life with LLMs. | Scale By the Bay 2023 | 2023-11-13 |
One-hour debate argues whether new billion‑dollar startups in five years come from open or closed models, citing concrete evidence: Llama 2’s released weights… | ||
| Map of Science: A UI for Open-Source Science | Open AI + Data Forum @ Open Source Summit Europe 2023 | 2023-09-19 |
Map of Science builds an interactive, queryable index linking people, labs, papers, datasets and OSS used in domains; a live Kumu prototype already visualizes… | ||
| Map of Science: A UI for Open-Source Science | Open Source Summit Europe 2023 | 2023-09-19 |
MOSS maps papers, labs, OSS projects, contributors and grants to show which tools actually drive science; a live prototype uses kumu demo plus automated… | ||
| Open-Source Science: Working with OSPOs to Accelerate Research in... | OSPOCon @ Open Source Summit North America 2023 | 2023-05-10 |
Open-Source Science is a NumFOCUS-hosted initiative co-founded with IBM that organizes communities around scientific OSS, standing up interest groups… | ||
| Startups and the ML landscape | Scale By the Bay 2021 | 2021-10-28 |
Panel argues practical paths: prioritize ship a de-risking prototype (example: a faces makeup mobile app before full storytelling), test on consumer channels… | ||
| Panel: Programming Languages in the Era of the Cloud | Scale By the Bay 2020 | 2020-11-12 |
Panel argues cloud choices hinge on concrete tradeoffs: Go wins for fast startup and tiny static binaries and cross-compilation; Rust wins for memory control… | ||
| Panel: Were Microservices a Huge Mistake? | Scale By the Bay 2020 | 2020-11-12 |
Panel argues specific tradeoffs: one claim predicts a multi‑billion dollar cleanup industry from misapplied microservices; another warns of distributed… | ||
| Fireside Chat with Richard Socher, Chief Scientist, Salesforce | Scale By The Bay 2018 | 2018-11-15 |
A research group integrates published neural techniques into products, improves accuracy by 20% on a sequence-tagging task for contact/email matching, and… | ||
| Marius Eriksen Interview | Scale By the Bay 2017 | 2017-11-16 |
GRAIL detects cancers early and pipelines generate up to a terabyte per sample, forcing investment in storage, network and cost-aware compute. Analysis runs on… | ||
| Manish Pandit Interview | Scale By the Bay 2017 | 2017-11-16 |
A fintech payments platform moves most infrastructure to Amazon and relies on Lambda functions and auto scaling groups to shrink footprint while handling high… | ||
| Kuassi Mensah Interview | Scale By the Bay 2017 | 2017-11-16 |
Oracle product management discusses scaling Oracle + Spark access by partitioning a single relational table so 100 Spark nodes can read in parallel, using… | ||
| Keita Broadwater Interview | Scale By the Bay 2017 | 2017-11-16 |
Describes building a pipeline that glues sensors, messaging systems and Spark to get usable inputs and repeatedly hits roadblocks around cleaning… | ||
| John A. De Goes Interview | Scale By the Bay 2017 | 2017-11-16 |
SlamData builds an advanced analytics compiler that translates workflows to MongoDB, MarkLogic, Couchbase, Spark or Postgres and insists on 100% correct… | ||
| Jacek Kunicki Interview | Scale By the Bay 2017 | 2017-11-16 |
Works in Scala and confronts real concurrent-execution bugs: thread pools and execution contexts were causing the app to block until the team separated… | ||
| Andrew Kuhnhausen Interview | Scale By the Bay 2017 | 2017-11-16 |
Describes concrete engineering choices at Domino Data Lab: balancing rapid feature work with enterprise-grade stability, supporting small-to-medium data… | ||
| Adam Pingel Interview | Scale By the Bay 2017 | 2017-11-16 |
Builds end-to-end products from data science through front-end visualizations and struggles to integrate those layers quickly, aiming for well under 2s… | ||
| Sijie Guo Interview | Scale By the Bay 2017 | 2017-11-16 |
Co-founder of Streamlio who co-created Apache DistributedLog and chairs Apache BookKeeper focuses on making a messaging+storage stack that scales hundreds or… | ||
| Sanjeev Kulkarni Interview | Scale By the Bay 2017 | 2017-11-16 |
Streamlio claims a messaging architecture that scales to millions of topics in a single seamless subsystem instead of dozens of clusters, and it handles… | ||
| Rustem Feyzkhanov Interview | Scale By the Bay 2017 | 2017-11-16 |
Describes porting real ML stacks — TensorFlow/Keras/Sklearn on AWS Lambda — using the lambda-packs repo, and why packaging native binaries and cold-start… | ||
| Rebecca Ely Interview | Scale By the Bay 2017 | 2017-11-16 |
Works on the Spark and NoSQL platform at Bloomberg, which claims to own and process more financial data than any other organization, so scale is a concrete… | ||
| Pathikrit Bhowmick Interview | Scale By the Bay 2017 | 2017-11-16 |
Coatue Management runs Scala and Spark pipelines on multi‑billion‑row datasets and faces severe skew where some nodes overload and others sit idle, breaking… | ||
| Neville Li Interview | Scale By the Bay 2017 | 2017-11-16 |
Spotify runs 200 to 300 developers on Scala data pipelines and faces concrete scaling pain points: immutable collections create lots of tiny object allocation… | ||
| Fred Reiss Interview | Scale By the Bay 2017 | 2017-11-16 |
Work on Apache Spark focuses on scaling transformations for very wide tables, for example recoding string columns when there are millions of distinct values… | ||
| Evan Chan Interview | Scale By the Bay 2017 | 2017-11-16 |
Built FiloDB and Spark Job Server, and describes a columnar real‑time distributed query engine using Storm, Spark, Kafka, Cassandra and Scala/Akka. Scaling… | ||
| Devin Loftis Interview | Scale By the Bay 2017 | 2017-11-16 |
Company authenticates email traffic and reports 3 billion messages validated over three years, requiring sub‑second lookups to avoid phishing delivery… | ||
| Alex Kudriashova Interview | Scale By the Bay 2017 | 2017-11-16 |
Platform handles 5–6 petabytes of satellite imagery growing ~500 TB/year, so processing targets only requested windows to avoid moving whole scenes. First-day… | ||
| Adelbert Chang Interview | Scale By the Bay 2017 | 2017-11-16 |
Lead engineer at Target describes building automated deployment infrastructure that scales services and batch jobs, using Kubernetes Nomad envoy to move… | ||
| Ben Edwards Interview | Scale By the Bay 2017 | 2017-11-16 |
A team at Starbucks migrates many developers from a Microsoft stack onto Scala and learns that small config choices break production: aggressive connection… | ||
| Ryan Knight Interview | Scale By the Bay 2017 | 2017-11-16 |
Argues that strong consistency is more than read-after-write and must include compare-and-swap, so consistency belongs in the database layer not the… | ||
| Thayne McCombs Interview | Scale By the Bay 2017 | 2017-11-16 |
Company moves to continuous delivery and faces test coverage, backward-compatibility and release-safety gaps while shipping more often. Relational data is… | ||
| Yuhao Yang Interview | Scale By the Bay 2017 | 2017-11-16 |
A library called BigDL runs deep learning on Spark clusters using Scala to give JVM shops model training and inference without moving to Python frameworks. It… | ||
| Paul Cleary Interview | Scale By the Bay 2017 | 2017-11-16 |
Comcast describes migrating from on‑prem virtualized stacks to hybrid clouds while juggling OpenStack workloads, Docker/OpenShift containerization, and… | ||
| Thomas Gerber Interview | Scale By the Bay 2017 | 2017-11-16 |
Salesforce Einstein builds a platform to democratize AI, ingesting CRM data to predict, recommend and automate while wrestling with data processing, automated… | ||
| Vladimir Kostyukov Interview | Scale By the Bay 2017 | 2017-11-16 |
Senior engineer at Twitter describes concrete operational work: building and using Finagle to handle massive request loads, running internal trainings to teach… | ||
| Stepan Pushkarev Interview | Scale By the Bay 2017 | 2017-11-16 |
Hydrosphere.io focuses on shipping ML pipelines into production and reports concrete engineering choices: using a functional paradigm so models are delivered… | ||
| Long Cao Interview | Scale By the Bay 2017 | 2017-11-16 |
Builds scalable data pipelines in Scala and Spark to consume alternative data for market signals, describes production failures that only appear at large scale… | ||
| Leif Wickland Interview | Scale By the Bay 2017 | 2017-11-16 |
Performance is the limiter: Python prototypes are orders of magnitude too slow for real-time ad tech, so the stack relies on Scala and a Spark shell pointed at… | ||
| Haeley Yao Interview | Scale By the Bay 2017 | 2017-11-16 |
Builds a microservice backend for a Chinese-targeted shopping bot and app using Scala, Cats, microservice architecture. Describes switching from Java to Scala… | ||
| Chris Rupley Interview | Scale By the Bay 2017 | 2017-11-16 |
Salesforce faces integration headaches from many acquisitions, so the work focuses on unifying disparate systems and handling surprises when scaling beyond… | ||
| Feynman Liang Interview | Scale By the Bay 2017 | 2017-11-16 |
Gigster aggregates telemetry from Slack, Trello, GitHub, Gmail across hundreds of concurrent projects to produce real‑time status and outcome predictions, and… | ||
| David Cleaver Interview | Scale By the Bay 2017 | 2017-11-16 |
Discusses two years of building services in Scala and ongoing work creating self-service infrastructure that keeps adding clients and multiplying request… | ||
| David Massart Interview | Scale By the Bay 2017 | 2017-11-16 |
Company builds a lightweight stream routing layer to connect multiple live feeds into distinct processing pipelines, because existing tools lack an obvious way… | ||
| Anya Bida Interview | Scale By the Bay 2017 | 2017-11-16 |
Works as an SRE supporting the Einstein platform and says regular social demos every Friday break down silos by forcing cross-training and visibility — they… | ||
| Alexandre Bergeron Interview | Scale By the Bay 2017 | 2017-11-16 |
Builds Domino Data Lab’s terminal platform to let data scientists build, train and productionize arbitrary Python and R models while keeping services isolated… | ||
| Chris Phelps Interview | Scale By the Bay 2017 | 2017-11-16 |
Platform teams at Tendril wrestle with analytics at scale by balancing heavy batch jobs that spike load against live, low-latency user queries, and describe… | ||
| Adriaan Moors Interview | Scale By the Bay 2017 | 2017-11-16 |
Discussion centers on keeping Scala useful by prioritizing balance between features and simplicity and sometimes removing complex additions rather than… | ||
| Vladimir Bacvanski Interview | Scale By the Bay 2017 | 2017-11-16 |
Distributed processing at scale improves throughput but networked microservices introduce latency, so the next optimization is to group services onto the same… | ||
| Ryan Delucchi Interview | Scale By the Bay 2017 | 2017-11-16 |
Verizon Labs uses free monads but finds trampoline overhead makes them too slow on hot paths, so teams replace them with finally tagless in… | ||
| Rob Norris Interview | Scale By the Bay 2017 | 2017-11-16 |
Gemini Observatory software team reports a gradual migrated from Java to Scala over six years and now relies on functional programming to tame a large legacy… | ||
| Rajesh Muppalla Interview | Scale By the Bay 2017 | 2017-11-16 |
At Indix built a catalog and machine learning facility and address the last mile problem in machine learning: reproducibility and self-serve deployment remain… | ||
| Petr Zapletal Interview | Scale By the Bay 2017 | 2017-11-16 |
Focuses on cost and state at scale: uses the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka), targets systems with millions of concurrent users, and… | ||
| Nikola Kasev Interview | Scale By the Bay 2017 | 2017-11-16 |
Software engineer at ING describes tackling long lived application complexity by designing for change and reasoning about composability, drawing on Scala and… | ||
| Michael Pilquist Interview | Scale By the Bay 2017 | 2017-11-16 |
Works on control systems at Comcast that manage tens of millions of cable devices and are intended to last 10–20 years. Describes using Scala libraries like… | ||
| Martin Zapletal Interview | Scale By the Bay 2017 | 2017-11-16 |
Builds globally distributed, elastic systems that tolerate catastrophic data center failures and common node losses while keeping latency and infrastructure… | ||
| Vlad Patryshev Interview | Scale By the Bay 2017 | 2017-11-16 |
A production system on AWS processes incoming data as streams and requires layered aggregations per minute, hour, week and year, forcing a choice between store… | ||
| Vincent Marquez Interview | Scale By the Bay 2017 | 2017-11-16 |
Work centers on designing safe APIs and choosing back pressure trade-offs versus fully asynchronous behavior, with no one-size answer and decisions driven by… | ||
| The Real Future of AI Panel | AI By the Bay 2017 | 2017-03-06 |
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| AI VC Panel | AI By the Bay 2017 | 2017-03-06 |
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| AI: from an Idea to the Customer Panel | AI By the Bay 2017 | 2017-03-06 |
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| Data and Society | Data By the Bay 2016 | 2016-05-16 |
Panel documents concrete wins and hard limits: a startup Lit IQ trains computational linguistics on annotated contracts and case law to surface drafting bugs… | ||
| Human Intent and Text Understanding | Data By the Bay 2016 | 2016-05-16 |
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| Panel: Future of Data Services | Data By the Bay 2016 | 2016-05-16 |
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| Panel: Artificial Intelligence: Who is Winning?Panel d4 | Data By the Bay 2016 | 2016-05-16 |
Panel discussion weighs why big firms buy startups: a war for talent and for Salesforce acquisitions of Metamind and others, not magic models. Speakers… | ||
| Panel: Living Data | Data By the Bay 2016 | 2016-05-16 |
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