diff --git a/conferences/2025/3BVEKT.html b/conferences/2025/3BVEKT.html new file mode 100644 index 0000000..8225042 --- /dev/null +++ b/conferences/2025/3BVEKT.html @@ -0,0 +1,237 @@ + + +
+ + +Plenary Session [Organizers]
+Lightning Talks are short, 5-minute presentations open to all attendees. They’re a fun and fast-paced way to share ideas, showcase projects, spark discussions, or raise awareness about topics you care about — whether technical, community-related, or just inspiring. + +No slides are required, and talks can be spontaneous or prepared. It’s a great chance to speak up and connect with the community!
+ + +None
+ + +⚡ Lightning Talk Rules
+Community Event Announcements
+All other LTs:
+Giampaolo Casolla is a Senior Data Scientist at GetYourGuide, leveraging advanced machine learning and Generative AI to solve complex travel industry challenges. With expertise spanning areas like Safety, Risk, and Security, and strong skills in stats, Python, R, and cloud tech, he brings a diverse background to the role. Prior to GetYourGuide, Giampaolo developed award-winning ML solutions at Amazon and has a background in research with publications and conference presentations. At GetYourGuide, he's focused on integrating LLMs and GenAI into data products to drive innovation in travel technology.
+ +Ansgar Grüne is a Senior Data Scientist at GetYourGuide in Berlin. His work focuses on ML/AI approaches to improve the users search and discovery experience on the platform. He holds a Ph.D. in Theoretical Computer Science and has 10 years of experience as a Data Scientist in the travel industry following several years as software engineer.
+ +Tim is a Software Development Consultant at Netlight with a track record of experience in diverse industries, including MedTech, E-Mobility, FinTech, E-Commerce, EdTech and IoT. With a passion for technology and a relentless pursuit of excellence, he is dedicated to continuously push the boundaries of innovation while crafting clean, well-architected solutions and streamlining processes for efficiency. Currently, Tim is supporting Bayer in the Research and Development domain by visualising extensive cell painting image data in early drug discovery.
+ +As a Machine Learning Engineer at Bayer, Matthias Orlowski has contributed to various projects, focusing on natural language processing in pharmacovigilance and medical image processing in radiology and early drug discovery. Matthias studied in Konstanz, Nottingham (UK), Durham (North Carolina, USA), and Berlin, where he earned a PhD from Humboldt University in 2015. Prior to joining Bayer, Matthias gained diverse experience in multiple roles and organizations, tackling projects in consumer targeting, campaigning, and recommender systems.
+ +Talk [Sponsored]
+In the fast-paced realm of travel experiences, GetYourGuide encountered the challenge of maintaining consistent, high-quality content across its global marketplace. Manual content creation by suppliers often resulted in inconsistencies and errors, negatively impacting conversion rates. To address this, we leveraged large language models (LLMs) to automate content generation, ensuring uniformity and accuracy. This talk will explore our innovative approach, including the development of fine-tuned models for generating key text sections and the use of Function Calling GPT API for structured data. A pivotal aspect of our solution was the creation of an LLM evaluator to detect and correct hallucinations, thereby improving factual accuracy. Through A/B testing, we demonstrated that AI-driven content led to fewer defects and increased bookings. Attendees will gain insights into training data refinement, prompt engineering, and deploying AI at scale, offering valuable lessons for automating content creation across industries.
+ + +Openai fine-tuning: https://platform.openai.com/docs/guides/fine-tuning +Openai function calling: https://platform.openai.com/docs/guides/function-calling?api-mode=chat +Evaluating model performance with LLM: https://platform.openai.com/docs/guides/evals
+ + +GetYourGuide, a global marketplace for travel experiences, needs to provide structured and inspiring content for every activity in its marketplace.
+Before the release of our AI models, suppliers would create their content fully manually. The manual approach led to several issues in production, such as content inconsistencies, incorrect grammar, non-English language, and poor adherence to our content guidelines.
+These content defects negatively impact the conversion rate of activities.
+At the same time, with the large scale of new activity generation, our internal teams could only review a very small fraction of the submitted content.
With our LLM solution, suppliers can now automatically generate optimal content for their activities. Our feature allows users to simply copy-paste any existing raw text of their activity, and our models would then prefill most of the content sections. Suppliers then have the opportunity to review and edit the content.
+We chose two different methods to generate free text content and structured information.
For free text, we used the OpenAI fine-tune API to create two different models generating the relevant sections of our travel activities, i.e. the title, the highlights, the short and full descriptions.
+For structured information, we used the Function Calling gpt API to prefill the different activities tags and categories that have fixed values constraints in our database, such as the transport used or the type of the guide.
In order to validate our models, as well as for production monitoring, we developed a dedicated LLM evaluator that identifies hallucinations for our specific case, that is our models generating information that is not factually correct as compared to the input supplier text. With this hallucination evaluator, we were able to score the performance of different models and unlock key learnings and iterations. The evaluator also enables our internal team to detect and correct the hallucinations in production.
+After several AB experiments, the new automated content creation feature is fully released to all our suppliers. The activities with content generated via AI showed significantly fewer content defects and a significant increase in bookings, with only a small fraction of hallucinations that can be reviewed and corrected manually.
+In this talk, we will share our long journey consisting of several training data iterations to build our fine-tuned models, the prompt engineering challenges in building our evaluator and our function call model. We will also cover the different experiments and the operational challenges in training the models and deploying the service in production.
+The talk will provide some concrete ideas and tools to automate the generation of optimal content with LLMs, which is a common use case in many industries.
Senior Data Scientist
+ + +With over a decade of experience in data science and analytics, I am a Senior Data Scientist at GetYourGuide, where I lead initiatives in leveraging large language models (LLMs) to enhance content quality and conversion rates. My expertise includes fine-tuning LLMs for custom text generation and classification, developing NLP models for discovering new travel interests, and automating predictive models for global travel demand. I have a robust background in machine learning, natural language processing, and AI-driven content automation, which has significantly improved operational efficiencies and business outcomes. +Prior to moving to Data Science, I was a Senior Data Analyst at GetYourGuide, where I developed key metrics for availability and loyalty, built automated forecasting for our travel activities, performed impact analyses for sales and marketing, and automated data analyses with custom libraries. +Before joining GetYourGuide, I worked as Data Analyst in Foodpanda, an online food delivery platform, where I optimized restaurant ranking algorithms and developed recommendation systems. +My analytical journey began at Wealth-X in Budapest, where I worked as a Business Analyst, and later as Research Consultant in Millward Brown Vermeer, where I applied statistical techniques to report insights to external customers. +I hold a Master's degree in Marketing from Rotterdam School of Management, Erasmus University, graduated cum laude, and a Bachelor's degree in Business/Managerial Economics from Università di Pisa. +Driven by a passion for data-driven decision-making, I am committed to advancing AI technologies to solve complex business challenges. At PyData 2025 Berlin, I aim to share insights into deploying AI at scale, refining training data, and mastering prompt engineering to automate content creation across industries.
+ + +Talk
Talk (long)
+Keynote
Jessica Greene is a self/community-taught developer who came to tech by way of the film industry and specialty coffee roasting. She is now a Senior Machine Learning Engineer at Ecosia.org, where she explores how ML and generative AI can support climate action. Passionate about ethical, sustainable, and inclusive technology, Jessica co-leads PyLadies Berlin, serves on the board of the Python Software Verband (PySV), and is part of the Python Software Foundation’s Conduct Working Group. In 2024, she was honored with the inaugural Outstanding PyLadies Award and the PSF Community Service Award for her contributions to the Python ecosystem.
+ + diff --git a/conferences/2025/CAUAZY.html b/conferences/2025/CAUAZY.html new file mode 100644 index 0000000..296002d --- /dev/null +++ b/conferences/2025/CAUAZY.html @@ -0,0 +1,258 @@ + + + + + +Talk
+AI agents are having a moment, but most of them are little more than fragile prototypes that break under pressure. Together, we’ll explore why so many agentic systems fail in practice, and how to fix that with real engineering principles. In this talk, you’ll learn how to build agents that are modular, observable, and ready for production. If you’re tired of LLM demos that don’t deliver, this talk is your blueprint for building agents that actually work.
+ + +Familarity with basic concepts in generative AI like LLMs and prompting +Basic python knowledge
+ + +Let’s face it: most AI agents are glorified demos. They look flashy, but they’re brittle, hard to debug, and rarely make it into real products. Why? Because wiring an LLM to a few tools is easy. Engineering a robust, testable, and scalable system is hard.
+This talk is for practitioners, data scientists, AI engineers, and developers who want to stop tinkering and start shipping. We’ll take a candid look at the common reasons agent systems fail and introduce practical patterns to fix them using Haystack, an open-source Python framework to build custom AI applications.
+You’ll learn how to design agents that are:
+We’ll also cover advanced topics like multimodal inputs and Model Context Protocol (MCP) to push your agents into more capable territory.
+Whether you’re just starting to explore agents or trying to tame an unruly prototype, you’ll leave with a clear, actionable blueprint to build something that’s not just smart, but also reliable.
+Developer Relations Engineer
+ + +Bilge is a developer relations engineer at deepset, where she helps developers build powerful AI applications and teaches the world how to use Haystack. Passionate about RAG, LLMs, and all things Gen AI, she enjoys making complex AI concepts accessible both online and at real-life events
+ + + + +freelance
@@ -219,6 +219,19 @@Self-employed
-Christoph Scheuch is an independent data science and business intelligence expert, currently serving as an external lecturer at Humboldt University of Berlin and as a summer school instructor at the Barcelona School of Economics. He is the co-creator and maintainer of the Tidy Finance project, an open-source initiative promoting transparent and reproducible research in financial economics. +
Christoph Scheuch is an independent data science and business intelligence expert, currently serving as an external lecturer at Humboldt University of Berlin and as a summer school instructor at the Barcelona School of Economics. He is the co-creator and maintainer of the [Tidy Finance](https://www.tidy-finance.org/) project, an open-source initiative promoting transparent and reproducible research in financial economics, and the [EconDataverse](https://www.econdataverse.org/), a universe of open-source packages to work seamlessly with economic data in R and Python. Previously, Christoph held leadership roles at the social trading platform wikifolio.com, including Head of Artificial Intelligence, Director of Product, and Head of BI & Data Science. He has also lectured at the Vienna University of Economics and Business, where he earned his PhD in Finance through the Vienna Graduate School of Finance.
@@ -200,7 +200,7 @@Dr. Michele Dolfi is a technical lead in the AI for Knowledge group at IBM Research, focusing on knowledge engineering and understanding. Michele is one of the researchers who created the Deep Search platform and the Docling open source project. His expertise spans from artificial intelligence to high performance computing and quantum systems.
+ +Jesse Grabowski is a PhD candidate at Paris 1 Pantheon-Sorbonne. He is also a principal data scientist at PyMC labs, and a core developer of PyMC, Pytensor, and related packages. His area of research includes time series modeling, macroeconomics, and finance.
+ + +Jesse Grabowski is a PhD candidate at Paris 1 Pantheon-Sorbonne. He is also a principal data scientist at PyMC labs, and a core developer of PyMC, Pytensor, and related packages. His area of research includes time series modeling, macroeconomics, and finance.
- - -An accomplished technical leader, Tobias brings over two decades of experience in software development, complemented by profound expertise in Data Science and Data Engineering. His career has focused on the end-to-end design and implementation of complex data-intensive applications, spanning the full lifecycle from data ingestion to deployment. In his current role at Lotum he is tackling a data volume of several hundred million events from mobile games per day.
+ +Talk (long)
+Keynote
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+From: Chrono-Regulatory Commission, Temporal Enforcement Division +To: PyData Berlin Organising Committee +Subject: Citation #TMP-2077-091 - Unauthorised Spacetime Disturbance + +Dear Committee, +Our temporal monitoring systems have detected an unauthorised chronological anomaly emanating from your facility (Berliner Congress Center, coordinates 52.52068°N, 13.416451°E) scheduled to manifest on September 1st at 9:20 a.m.
Sed mattis dui quis porttitor bibendum. Cras laoreet sollicitudin velit quis mollis. Ut vitae eleifend ex, eu suscipit lorem. Pellentesque eget faucibus lorem. Sed porta commodo pellentesque. Sed feugiat lectus sed nisl venenatis vestibulum. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Maecenas porttitor fringilla leo, eget finibus orci. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia curae; Aliquam auctor urna ut diam hendrerit rutrum et et mi. Nulla tortor sem, venenatis vulputate lacus convallis, pulvinar condimentum eros.
+VIOLATION DETAILS:
+- Unauthorized temporal incursion detected
+- Speakers identified as: Kitchen, A. & Summers, L. (baseline timeline)
+- Anomalous data signatures suggest retrospective analysis from non-contemporaneous source
+- Evidence of information leakage: late 21st-century technological practices and standards
+- Risk assessment: Moderate timeline contamination potential
REGULATORY COMPLIANCE REQUIRED:
+Per Temporal Code Section 2077.3, you are hereby notified that failure to contain this spacetime disturbance will result in fines of up to 50,000 temporal credits. You must ensure adequate attendance at the specified coordinates to properly observe and contain the anomaly as it unfolds.
+WARNING: Preliminary scans indicate the transmission contains advanced analytical frameworks and critical commentary on primitive early-21st-century data science practices. Attendees may experience paradigm shifts, changes to mental models, or sudden clarity regarding field trajectories.
Sincerely,
+Compliance Officer Z-7749
+Chrono-Regulatory Commission
+"Keeping Yesterday Safe for Tomorrow"
Lead Design Engineer
+ + +Laura is a very technical designer™️, working at Pydantic as Lead Design Engineer. Her side projects include Sweet Summer Child Score (summerchild.dev) and Ethics Litmus Tests (ethical-litmus.site). Laura is passionate about feminism, digital rights and designing for privacy. She speaks, writes and runs workshops at the intersection of design and technology.
+ + + + +AI Researcher
+ + +Andy Kitchen is a hacker, startup founder and AI/Neuroscience researcher. Let's grab a beer and talk about philosophy, computer science and society (and science fiction while we're at it!)
+ + + + +Yashasvi Misra is a Data Engineer at Pure Storage and Chair of the NumFOCUS Code of Conduct Working Group, where she helps foster inclusive practices across the open-source ecosystem. She has contributed to foundational projects like NumPy and has been an active part of the Python community since her college days. Yashasvi is also a passionate advocate for diversity and inclusion in tech. She introduced a period leave policy at a previous organisation and continues to work toward building more equitable workplaces. She has shared her work and insights at conferences around the world, including PyCon India, PyCon Europe, PyLadiesCon, and PyData Global.
+ +Senior Machine learning engineer
@@ -233,6 +233,21 @@I have worked in the healthcare industry for more than 10 years, currently a senior machine learning at Owkin. Committed to open source and open science principles, I aspire to leverage Python and data science for social good, focusing on health, inclusion, and projects that make a meaningful difference in people's lives.
+ +Talk (long)
+Keynote
Talk
+Data visualizations often exclude users with visual impairments and temporary or situational constraints. Many regulations (European Accessibility Act, American Disabilities Act) now mandate inclusive digital content. Our research provides practical solutions — optimized color palettes, supplementary patterns, and alternative formats — implemented in popular libraries like Bokeh and Vega-Altair. These techniques, available through our open-source cusy Design System, create visualizations that reach broader audiences while meeting compliance requirements and improving comprehension for all users.
+ + +There are no real prerequisites, but the talk might be most interesting to you if you have basic knowledge of data visualizations and some sort of interest for accessibility in combination with data visualizations.
+ + +Accessible data visualizations extend beyond aesthetics to meet established standards and accommodate diverse visual abilities. This presentation demonstrates how to create visualizations that comply with Web Content Accessibility Guidelines (WCAG) contrast requirements, support users with color vision deficiencies, and convey information through multiple encoding channels. The topics in the presentation explore practical techniques using colors, patterns, SVG accessibility features, and alternative data formats.
+This presentation is designed for data scientists, visualization specialists, dashboard designers, and accessibility auditors who need to communicate findings effectively to diverse audiences. Attendees will benefit by:
+Data visualizations must meet WCAG contrast ratios (≥3:1) for distinguishable elements. Our optimized palette features:
+Patterns provide critical secondary encoding when color alone is insufficient, we'll present:
+<pattern>
tagsPractical examples will demonstrate:
+<pattern>
elementsImplementing these practices creates data visualizations that are not only compliant with accessibility regulations but also more effective for all users. The cusy Design System offers open-source resources to implement these techniques across various visualization libraries.
+Working student
+ + +## Junior Dev +- TS/JS, Python, Java, and a teeny bit o' C++ +- WebDev, DataViz, Backend-Buzz + +#a11y
+ + + + +Remote Sensing & Space System Engineer | Innovating AI-Powered Geospatial Solutions | Expert in Satellite Data and Infrastructure Monitoring
+ +Senior Data Scientist
-A studied natural scientist, Johannes tought himself data science skills and now does what he loves: to solve data and tech challenges that generate value.
+A studied natural scientist and expert in power semiconductors, Johannes tought himself data science skills and now does what he loves: to solve data and tech challenges that generate value.
+ +In large organizations, data and AI talent often work in fragmented teams, making cross-pollination of ideas, tools, and best practices a challenge. At Vattenfall, we addressed this by founding the “Data & ML/AI Engineering Guild” — a cross-functional community dedicated to sharing knowledge, aligning on technical standards, and accelerating innovation across business units.
+In large organizations, data and AI talent often work in fragmented teams, making cross-pollination of ideas, tools, and best practices a challenge. At Vattenfall, we addressed this by founding the “Data & ML/AI Engineering Guild”: a cross-functional community dedicated to sharing knowledge, aligning on technical standards, and accelerating innovation across business units.
As data professionals, we talk a lot about breaking down data silos — but the reality is, silos exist not just in our data, but in our teams. At Vattenfall, data and ML/AI engineers are spread across different units, projects, and domains. We often face similar challenges, reinvent similar solutions, and learn the hard way: in parallel, and in isolation.
+As data professionals, we talk a lot about breaking down data silos, but the reality is, silos exist not just in our data, but in our teams. At Vattenfall, data and ML/AI engineers are spread across different units, projects, and domains. We often face similar challenges, reinvent similar solutions, and learn the hard way: in parallel, and in isolation.
To address this, we started something simple but powerful: a community. The "Data & ML/AI Engineering Guild" began as a small group of engineers coming together to share learnings and frustrations. Over time, it evolved into a cross-functional space where we exchange knowledge, run internal talks, and build technical momentum across the organization.
In this talk, I’ll walk through how we built and scaled this guild: what worked, what didn’t, and what we’re still figuring out. I’ll share the formats we use to keep engagement high (even when calendars are packed), how we balance deep technical discussions with accessibility. I’ll also reflect on how this kind of community work complements our day jobs, and how it helps engineers grow beyond the boundaries of their product teams.
If you’re thinking about starting a tech guild, already running one, or just curious how to create more connection and consistency in your data/ML org, this talk is for you. My goal is to share honest lessons (not just success stories) and hopefully spark ideas you can adapt in your own context.
@@ -180,7 +190,7 @@VP Labs
@@ -206,6 +206,19 @@Adrin is VP Labs at probabl.ai and has a PhD in computational biology. He is also a maintainer of open source projects such as scikit-learn and fairlearn. He focuses on developer tools in the statistical machine learning and responsible ML space.
+ +Alina Dallmann is a computer scientist currently working as a Data Scientist at scieneers GmbH. Her enthusiasm for classical software development and data-driven projects has recently come together in various projects focused on building retrieval-augmented generation (RAG) systems.
+ + diff --git a/conferences/2025/PPAYDV.html b/conferences/2025/PPAYDV.html index 55fc097..d643049 100644 --- a/conferences/2025/PPAYDV.html +++ b/conferences/2025/PPAYDV.html @@ -4,22 +4,22 @@Software Engineer
@@ -193,6 +203,10 @@I am a data analyst with experience in various sectors including tech and supply chain. I am also a hobby programmer and like to spend my spare time working on cool personal projects.
+ +Tobias is the CEO of MOSTLY AI, the leader in privacy-preserving synthetic data. Originally from Vienna, Austria, he is currently based in Munich, Germany. Before joining MOSTLY AI, Tobias worked as a management consultant with the Boston Consulting Group and in tech start-ups in different leadership roles. He earned a PhD from the Vienna University of Business and Economics and an MBA from the Haas School of Business at UC Berkeley. With his extensive background in strategy and technology, Tobias drives MOSTLY AI’s mission to revolutionize data access and data insights across industries.
+ +Lead Conference Resurrection Engineer
@@ -222,8 +222,14 @@Liza (Elizaveta) Zinovyeva is an Applied Scientist at AWS Generative AI Innovation Center and is based in Berlin. She helps customers across different industries to integrate Generative AI into their existing applications and workflows. She is passionate about AI/ML, finance and software security topics. In her spare time, she enjoys spending time with her family, sports, learning new technologies, and table quizzes.
+ +Tutorial
+This hands-on workshop focuses on what AI engineers do most often: making data AI-ready and turning it into production-useful applications. Together with dltHub and LanceDB, you’ll walk through an end-to-end workflow: collecting and preparing real-world data with best practices, managing it in LanceDB, and powering AI applications with search, filters, hybrid retrieval, and lightweight agents. By the end, you’ll know how to move from raw data to functional, production-ready AI setups without the usual friction. We will touch upon multi-modal data and going to production with this end-to-end use case.
+ + +TBA
+ + +Modern AI applications are only as powerful as the data that fuels them. Yet, much of the real-world data AI engineers encounter is messy, incomplete, or unoptimized data. In this hands-on tutorial, AI-Ready Data in Action: Powering Smarter Agents, participants will walk through the full lifecycle of preparing unstructured data, embedding it into LanceDB, and leveraging it for search and agentic applications. Using a real-world dataset, attendees will incrementally ingest, clean, and vectorize text data, tune hybrid search strategies, and build a lightweight chat agent to surface relevant results. The tutorial concludes by showing how to take a working demo into production. By the end, participants will gain practical experience in bridging the gap between messy raw data and production-ready pipelines for AI applications.
+Prior knowledge
+The tutorial is designed to be accessible: engineers familiar with Python should be able to follow along step by step.
+Key Takeaways
+By the end of the tutorial, participants will:
+Outline
+Chang is the CEO/Co-founder of LanceDB and has been making data tooling for ML/AI for almost two decades. +One of the original co-authors of the pandas project, Chang started LanceDB to make it easy for AI teams to work with all of the data that doesn't fit neatly into all of those dataframes - from embeddings to images, from audio to video, at petabyte scale.
+ + +As large language models become integral to real-world applications, evaluating and improving their performance is a growing challenge. Generic benchmarks and simple metrics fail to adequately assess domain-specific, multi-step reasoning required by compound AI pipelines like retrieval-augmented generation (RAG), multi-tool agents, or knowledge assistants. Moreover, manual evaluation of every step is infeasible at scale, while fully automated LLM-as-a-judge approaches lack critical domain insights.
In this talk, we will present a practical evaluation approach to enable continuous improvement of LLM-powered systems. It incorporates the following stages:
-- Automatic tracing (e.g. using MLFlow
, Langfuse
, etc.): capturing input/output pairs across the pipeline to build an evaluation dataset.
+- Automatic tracing: capturing input/output pairs across the pipeline to build an evaluation dataset.
- Expert feedback collection: working with subject matter experts and user interactions to assess correctness, and identify failure points.
-- Iterative improvement cycle: tuning the components and/or optimizing prompts (using frameworks like DSPy
, TextGrad
, etc.).
-- Degradation tests: turning feedback into automated evaluation tests - ranging from exact match checks to LLM-as-a-judge assertions (using approaches like DeepEval
) - to guard against regressions.
+- Iterative improvement cycle: tuning the components and/or optimizing prompts.
+- Degradation tests: turning feedback into automated evaluation tests - ranging from exact match checks to LLM-as-a-judge assertions - to guard against regressions.
- Continuous monitoring: using the growing evaluation dataset to validate the system as models, tools, or data sources evolve.
This framework ensures that LLM applications remain reliable, and aligned with specific business needs over time.
Target audience: AI practitioners developing and maintaining LLM-based applications.
@@ -194,7 +204,7 @@Iryna is a data scientist and co-founder of DataForce Solutions GmbH, a company specialized in delivering end-to-end data science and AI services. She contributes to several open-source libraries, and strongly believes that open-source products foster a more inclusive tech industry, equipping individuals and organizations with the necessary tools to innovate and compete.
+ +Talk
+Spare Cores is a Python-based, open-source, and vendor-independent ecosystem collecting, generating, and standardizing comprehensive data on cloud server pricing and performance. In our latest project, we started 2000+ server types across five cloud vendors to evaluate their suitability for serving Large Language Models from 135M to 70B parameters. We tested how efficiently models can be loaded into memory of VRAM, and measured inference speed across varying token lengths for prompt processing and text generation. The published data can help you find the optimal instance type for your LLM serving needs, and we will also share our experiences and challenges with the data collection and insights into general patterns.
+ + +-
+ + +Spare Cores is a vendor-independent, open-source, Python-based ecosystem offering a comprehensive inventory and performance evaluation of servers across cloud providers. We automate the discovery and provisioning of thousands of server types in public using GitHub Actions to run hardware inspection tools and benchmarks for different workloads, including:
+- General performance (GeekBench, PassMark)
+- Memory bandwidth and compressions algorightms
+- OpenSSL, Redis, and web serving speed
+- DS/ML-specific benchmarks like GBM training and LLM inference on CPUs and GPUs
All results and open-source tools (such as database dumps, APIs, and SDKs) are openly published to help users identify and launch the most cost-efficient instance type for their specific use case in their own cloud environment.
+This talk introduces the open-source ecosystem, then highlights our latest benchmarking efforts, including the performance evaluation of ~2,000 server types to determine the largest LLM model (from 135M to 70B parameters) that can be loaded on the machines and the inference speeds achievable with various token length for prompt processing and text generation.
+Hack of all trades, master of NaN
+ + +Gergely Daroczi, PhD, is a passionate R/Python user and package developer for two decades. With over 15 years in the industry, he has expertise in data science, engineering, cloud infrastructure, and data operations across SaaS, fintech, adtech, and healthtech startups in California and Hungary, focusing on building scalable data platforms. Gergely maintains a dozen open-source R and Python projects and organizes a tech meetup with 1,800 members in Hungary – along with other open-source and data conferences.
+ + + + +Software and data engineering consultant. I build data systems that help businesses to answer questions about their business. I like solving problems in a pragmatic way.
+ +