Predicting Medical Test Results using Driverless AI
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1. poder.IO uses AI to predict customer behavior and personalize experiences. It deploys over 100 models daily using techniques like regression, classification, text analysis and deep learning. 2. Driverless AI is currently used to benchmark models before production and for research cases. It may be used starting Q3 2018 for advertising optimization, content classification, profile matching and look-alike modeling. 3. A joint team from poder.IO and Bayer developed models to predict individual medical test results using healthcare data, without direct lab measures. This could help improve treatment strategies. They used techniques like GLM, GBM, random forest and Driverless AI to develop and compare models for a medical test, finding Driver
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Dr. Sergei Izrailev is Chief Data Scientist at BeeswaxIO, where he is responsible for data strategy and building AI applications powering the next generation of real-time bidding technology. Before Beeswax, Sergei led data science teams at Integral Ad Science and Collective, where he focused on architecture, development and scaling of data science based advertising technology products. Prior to advertising, Sergei was a quant/trader and developed trading strategies and portfolio optimization methodologies. Previously, he worked as a senior scientist at Johnson & Johnson, where he developed intelligent tools for structure-based drug discovery. Sergei holds a Ph.D. in Physics and Master of Computer Science degrees from the University of Illinois at Urbana-Champaign.
Real-Time AI: Designing for Low Latency and High Throughput - Dr. Sergei Izra...
This talk was recorded in London on October 30th, 2018 and can be viewed here: https://youtu.be/CeOJFynB6BE
Real-Time AI: Designing for Low Latency and High Throughput
Bio: Dr. Sergei Izrailev is Chief Data Scientist at Beeswax, where he is responsible for data strategy and building AI applications powering the next generation of real-time bidding technology. Before Beeswax, Sergei led data science teams at Integral Ad Science and Collective, where he focused on architecture, development, and scaling of data science-based advertising technology products. Prior to advertising, Sergei was a quant/trader and developed trading strategies and portfolio optimization methodologies. Previously, he worked as a senior scientist at Johnson & Johnson, where he developed intelligent tools for structure-based drug discovery.
Predicting medical tests results using Driverless AI
1. The document discusses using Driverless AI to predict medical test results based on patient data from a medical laboratory.
2. Over 11 million patient records spanning 10 years with 260 raw features were used to train and test models to predict a specific medical test result.
3. Driverless AI was able to predict the test results with less than 1 hour of training time and achieved similar accuracy as other models that took days to train, while only using 46% of the original features.
Bring Your Own Recipes Hands-On Session
1. Driverless AI can be used across many industries like banking, healthcare, telecom, and marketing to save time and money through tasks like fraud detection, customer churn prediction, and personalized recommendations.
2. The document highlights new features in Driverless AI 1.7.1 including improved time series recipes, natural language processing features, automatic visualization, and machine learning interpretability tools.
3. Driverless AI provides fully automated machine learning through techniques such as automatic feature engineering, model tuning, standalone scoring pipelines, and massively parallel processing to find optimal solutions.
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Using H2O for Mobile Transaction Forecasting & Anomaly Detection - Capital One
Presented at #H2OWorld 2017 in Mountain View, CA.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Effective volume anomaly detection presents unique challenges when monitoring customer transaction volumes across thousands of platforms and systems. We overcome this by using H2O, building on open source tools, and delivering machine learning anomaly detection for enterprise scale. Hear how we model, visualize then automatically alert on anomalous Mobile app volumes in real-time.
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- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
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Dive into H2O: NYC
This session took place at New York City on November 4th, 2019.
Speaker Bio:
Chemere is a Senior Data Science Training Specialist for H2O.ai. Chemere has a Master's in Business Administration with focus in Marketing Analytics from the University of North Carolina at Charlotte. She is an experienced data scientist with a diverse background in transformational decision-making in various industries including Banking, Manufacturing, Logistics, and Medical Devices. Chemere joins us from Venus Concept/2two5, where she was the Lead Data Scientist focused on building predictive models with Internet of Things (IoT) data and for a subscription-based marketing product for B2B customers. Prior to that, Chemere worked as a Senior Data Scientist at Wells Fargo Bank focused on various applied predictive analytic solutions.
More details about the event can be had here: https://www.eventbrite.com/e/dive-into-h2o-new-york-tickets-76351721053
Introducción al Machine Learning Automático
¿Cómo puede llevar el aprendizaje automático a las masas? Los proyectos de Machine Learning con la búsqueda de talento, el tiempo para construir e implementar modelos y confiar en los modelos que se construyen.
¿Cómo puede tener varios equipos en su organización para crear modelos de ML precisos sin ser expertos en ciencia de datos o aprendizaje automático?
¿Se pregunta sobre los diferentes sabores de AutoML?
H2O Driverless AI emplea las técnicas de científicos expertos en datos en una aplicación fácil de usar que ayuda a escalar sus esfuerzos de ciencia de datos. La inteligencia artificial Driverless permite a los científicos de datos trabajar en proyectos más rápido utilizando la automatización y la potencia de computación de vanguardia de las GPU para realizar tareas en minutos que solían tomar meses.
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H2O Driverless AI hace:
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* Ingeniería automática de funciones a nivel de Grandmaster
* Selección automática del modelo
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* Paralelización automática utilizando múltiples CPU o GPU
* Ensamblaje automático del modelo
*automática del Interpretaciónaprendizaje automático (MLI)
* Generación automática de código de puntuación
¿Quieres probarlo tú mismo? Puede obtener una prueba gratuita aquí: H2O Driverless AI trial.
Venga a esta sesión y descubra cómo comenzar con el Aprendizaje automático automático con AI sin conductor H2O, y cree modelos potentes con solo unos pocos clics.
¡Te veo pronto!
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H2O.ai es una empresa visionaria de software de código abierto de Silicon Valley que creó y reimaginó lo que es posible. Somos una empresa de fabricantes que trajeron al mercado nuevas plataformas y tecnologías para impulsar el movimiento de inteligencia artificial. Somos los creadores de, H2O, la principal plataforma de aprendizaje de ciencia de datos de fuente abierta y de aprendizaje automático utilizada por casi la mitad de Fortune 500 y en la que confían más de 14,000 organizaciones y cientos de miles de científicos de datos de todo el mundo.
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YouTube Video URL: https://youtu.be/gB0bTH-L6DE
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Enjoy the webinar recording here: https://youtu.be/Lll1qwQJKVw.
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Enjoy the video: https://youtu.be/-rGRHrED94Y.
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Follow @h2oai: https://twitter.com/h2oai.
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Abstract:
Most machine learning systems enable two essential processes: creating a model and applying the model in a repeatable and controlled fashion. These two processes are interrelated and pose technological and organizational challenges as they evolve from research to prototype to production. This presentation outlines common design patterns for tackling such challenges while implementing machine learning in a production environment.
Sergei's Bio:
Dr. Sergei Izrailev is Chief Data Scientist at BeeswaxIO, where he is responsible for data strategy and building AI applications powering the next generation of real-time bidding technology. Before Beeswax, Sergei led data science teams at Integral Ad Science and Collective, where he focused on architecture, development and scaling of data science based advertising technology products. Prior to advertising, Sergei was a quant/trader and developed trading strategies and portfolio optimization methodologies. Previously, he worked as a senior scientist at Johnson & Johnson, where he developed intelligent tools for structure-based drug discovery. Sergei holds a Ph.D. in Physics and Master of Computer Science degrees from the University of Illinois at Urbana-Champaign.
Real-Time AI: Designing for Low Latency and High Throughput - Dr. Sergei Izra...
This talk was recorded in London on October 30th, 2018 and can be viewed here: https://youtu.be/CeOJFynB6BE
Real-Time AI: Designing for Low Latency and High Throughput
Bio: Dr. Sergei Izrailev is Chief Data Scientist at Beeswax, where he is responsible for data strategy and building AI applications powering the next generation of real-time bidding technology. Before Beeswax, Sergei led data science teams at Integral Ad Science and Collective, where he focused on architecture, development, and scaling of data science-based advertising technology products. Prior to advertising, Sergei was a quant/trader and developed trading strategies and portfolio optimization methodologies. Previously, he worked as a senior scientist at Johnson & Johnson, where he developed intelligent tools for structure-based drug discovery.
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Using the power of OpenAI with your own data: what's possible and how to start?
Using the power of OpenAI with your own data: what's possible and how to start?
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...
Whither the Hadoop Developer Experience, June Hadoop Meetup, Nitin Motgi
Whither the Hadoop Developer Experience, June Hadoop Meetup, Nitin Motgi
More from Sri Ambati
GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...
“AGI should be open source and in the public domain at the service of humanity and the planet.”
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
This document provides an overview of H2O.ai, an AI company that offers products and services to democratize AI. It mentions that H2O products are backed by 10% of the world's top data scientists from Kaggle and that H2O has customers in 7 of the top 10 banks, 4 of the top 10 insurance companies, and top manufacturing companies. It also provides details on H2O's founders, funding, customers, products, and vision to make AI accessible to more organizations.
Generative AI Masterclass - Model Risk Management.pptx
Here are some key points about benchmarking and evaluating generative AI models like large language models:
- Foundation models require large, diverse datasets to be trained on in order to learn broad language skills and knowledge. Fine-tuning can then improve performance on specific tasks.
- Popular benchmarks evaluate models on tasks involving things like commonsense reasoning, mathematics, science questions, generating truthful vs false responses, and more. This helps identify model capabilities and limitations.
- Custom benchmarks can also be designed using tools like Eval Studio to systematically test models on specific applications or scenarios. Both automated and human evaluations are important.
- Leaderboards like HELM aggregate benchmark results to compare how different models perform across a wide range of tests and metrics.
AI and the Future of Software Development: A Sneak Peek
The document discusses developers including both in-house and outsourced developers. It also mentions dev shops and low/no code platforms. The main topics covered are developers, dev shops, and low-code platforms.
LLMOps: Match report from the top of the 5th
The document discusses LLMOps (Large Language Model Operations) compared to traditional MLOps. Some key points:
- LLMOps and MLOps face similar challenges across the development lifecycle, but LLMOps requires more GPU resources and integration is faster due to more models in each application. Evaluation is also less clear.
- The LLMOps field is around the 5th generation of models, with debates around proprietary vs open source models, and balancing privacy, cost and control.
- LLMOps platforms are emerging to provide solutions for tasks like prompting, embedding databases, evaluation, and governance, similar to how MLOps platforms have evolved.
Building, Evaluating, and Optimizing your RAG App for Production
The document discusses optimizing question answering systems called RAG (Retrieve-and-Generate) stacks. It outlines challenges with naive RAG approaches and proposes solutions like improved data representations, advanced retrieval techniques, and fine-tuning large language models. Table stakes optimizations include tuning chunk sizes, prompt engineering, and customizing LLMs. More advanced techniques involve small-to-big retrieval, multi-document agents, embedding fine-tuning, and LLM fine-tuning.
Building LLM Solutions using Open Source and Closed Source Solutions in Coher...
Sandeep Singh, Head of Applied AI Computer Vision, Beans.ai
H2O Open Source GenAI World SF 2023
In the modern era of machine learning, leveraging both open-source and closed-source solutions has become paramount for achieving cutting-edge results. This talk delves into the intricacies of seamlessly integrating open-source Large Language Model (LLM) solutions like Vicuna, Falcon, and Llama with industry giants such as ChatGPT and Google's Palm. As the demand for fine-tuned and specialized datasets grows, it is imperative to understand the synergy between these tools. Attendees will gain insights into best practices for building and enriching datasets tailored for fine-tuning tasks, ensuring that their LLM projects are both robust and efficient. Through real-world examples and hands-on demonstrations, this talk will equip attendees with the knowledge to harness the power of both open and closed-source tools in a coherent and effective manner.
Risk Management for LLMs
Patrick Hall, Professor, AI Risk Management, The George Washington University
H2O Open Source GenAI World SF 2023
Language models are incredible engineering breakthroughs but require auditing and risk management before productization. These systems raise concerns about toxicity, transparency and reproducibility, intellectual property licensing and ownership, disinformation and misinformation, supply chains, and more. How can your organization leverage these new tools without taking on undue or unknown risks? While language models and associated risk management are in their infancy, a small number of best practices in governance and risk are starting to emerge. If you have a language model use case in mind, want to understand your risks, and do something about them, this presentation is for you!
Open-Source AI: Community is the Way
Dr. Alexy Khrabrov, Open Source Science Community Director, IBM
H2O Open Source GenAI World SF 2023
In this talk, Dr. Alexy Khrabrov, recently elected Chair of the new Generative AI Commons at Linux Foundation for AI & Data, outlines the OSS AI landscape, challenges, and opportunities. With new models and frameworks being unveiled weekly, one thing remains constant: community building and validation of all aspects of AI is key to reliable and responsible AI we can use for business and society needs. Industrial AI is one key area where such community validation can prove invaluable.
Building Custom GenAI Apps at H2O
The document announces the launch of the H2O GenAI App Store, which provides a collection of applications that make it easier for average users to leverage large language models through custom interfaces for specific tasks like getting gardening advice or feedback on code. The app store is designed to accelerate the development of these GenAI apps using the H2O Wave platform and provides access to H2OGPTE for retrieval augmented generation and language model calls. Developers can also contribute their own apps through the GitHub repository listed.
Applied Gen AI for the Finance Vertical
Megan Kurka, Vice President, Customer Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
Discover the transformative power of Applied Gen AI. Learn how the H2O team builds customized applications and workflows that integrate capabilities of Gen AI and AutoML specifically designed to address and enhance financial use cases. Explore real world examples, learn best practices, and witness firsthand how our innovative solutions are reshaping the landscape of finance technology.
Cutting Edge Tricks from LLM Papers
This document discusses techniques for improving language models (LLMs) discussed in recent papers. It describes building blocks of LLMs like fine-tuning, foundation training, memory, and databases. Specific techniques covered include LIMA which uses 1,000 carefully curated examples, instruction backtranslation to generate question-answer pairs, fine-tuning models on API examples like Gorilla, and reducing false answers through techniques like not agreeing with incorrect user opinions. The goal is to discuss cutting edge tricks to build better LLMs.
Practitioner's Guide to LLMs: Exploring Use Cases and a Glimpse Beyond Curren...
Pascal Pfeiffer, Principal Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
This talk dives into the expansive ecosystem of Large Language Models (LLMs), offering practitioners an insightful guide to various relevant applications, from natural language understanding to creative content generation. While exploring use cases across different industries, it also honestly addresses the current limitations of LLMs and anticipates future advancements.
Open Source h2oGPT with Retrieval Augmented Generation (RAG), Web Search, and...
- Jon McKinney, Director of Research, H2O.ai
- Arno Candel, Chief Technology Officer, H2O.ai
H2O Open Source GenAI World SF 2023
KGM Mastering Classification and Regression with LLMs: Insights from Kaggle C...
This document discusses using large language models (LLMs) for text classification tasks. It begins by describing how LLMs are commonly used for text generation and question answering. For classification, models are usually trained supervised on labeled data. The document then explores using LLMs for zero-shot classification without training, and techniques like fine-tuning LLMs on tasks to improve performance. It provides an example of fine-tuning an LLM on a financial sentiment dataset. The document concludes by describing H2O.ai's LLM Studio tool for fine-tuning and a few Kaggle competitions where LLMs achieved success in text classification.
LLM Interpretability
1) Generative AI (GenAI) enables the creation of novel content by learning patterns in unstructured data rather than labeling outputs like traditional AI.
2) Both traditional and generative AI models lack transparency and may contain biases, but generative models can additionally hallucinate or leak private information.
3) To interpret generative models, researchers evaluate accuracy globally by checking for hallucinations or undesirable content, and locally by confirming the quality of individual responses.
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Luiz Pizzato, Executive Manager Artificial Intelligence, Commonwealth Bank
H2O Open Source GenAI World SF 2023
From Rapid Prototypes to an end-to-end Model Deployment: an AI Hedge Fund Use...
Numerai is an open, crowd-sourced hedge fund powered by predictions from data scientists around the world. In return, participants are rewarded with weekly payouts in crypto.
In this talk, Joe will give an overview of the Numerai tournament based on his own experience. He will then explain how he automates the time-consuming tasks such as testing different modelling strategies, scoring new datasets, submitting predictions to Numerai as well as monitoring model performance with H2O Driverless AI and R.
AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transfo...
In this session, you will learn about what you should do after you’ve taken an AI transformation baseline. Over the span of this session, we will discuss the next steps in moving toward AI readiness through alignment of talent and tools to drive successful adoption and continuous use within an organization.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/K1Cl3x3rd8g
Speaker:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
AI Foundations Course Module 1 - An AI Transformation Journey
The chances of successfully implementing AI strategies within an organization significantly improve when you can recognize where your organization is on the maturity scale. Over this course, you will learn the keys to unlocking value with AI which include asking the right questions about the problems you are solving and ensuring you have the right cross-section of talent, tools, and resources. By the end of this module, you should be able to recognize where your organization is on the AI transformation spectrum and identify some strategies that can get you to the next stage in your journey.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/PJgr2epM6qs
Speakers:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
Ingrid Burton (H2O.ai - CMO)
More from Sri Ambati (20)
GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...
GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
Generative AI Masterclass - Model Risk Management.pptx
Generative AI Masterclass - Model Risk Management.pptx
AI and the Future of Software Development: A Sneak Peek
AI and the Future of Software Development: A Sneak Peek
Building, Evaluating, and Optimizing your RAG App for Production
Building, Evaluating, and Optimizing your RAG App for Production
Building LLM Solutions using Open Source and Closed Source Solutions in Coher...
Building LLM Solutions using Open Source and Closed Source Solutions in Coher...
Practitioner's Guide to LLMs: Exploring Use Cases and a Glimpse Beyond Curren...
Practitioner's Guide to LLMs: Exploring Use Cases and a Glimpse Beyond Curren...
Open Source h2oGPT with Retrieval Augmented Generation (RAG), Web Search, and...
Open Source h2oGPT with Retrieval Augmented Generation (RAG), Web Search, and...
KGM Mastering Classification and Regression with LLMs: Insights from Kaggle C...
KGM Mastering Classification and Regression with LLMs: Insights from Kaggle C...
From Rapid Prototypes to an end-to-end Model Deployment: an AI Hedge Fund Use...
From Rapid Prototypes to an end-to-end Model Deployment: an AI Hedge Fund Use...
AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transfo...
AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transfo...
AI Foundations Course Module 1 - An AI Transformation Journey
AI Foundations Course Module 1 - An AI Transformation Journey
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A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
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- How to streamline operations with automated policy checks on container images
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications.
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
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Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Building RAG with self-deployed Milvus vector database and Snowpark Container...
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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A tale of scale & speed: How the US Navy is enabling software delivery from l...
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Large Language Model (LLM) and it’s Geospatial Applications
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
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Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
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Predicting Medical Test Results using Driverless AI
- 1. Alexander Gedranovich Chief Technology Officer poder.IO linkedin.com/in/alexander-gedranovich-73847435 Predicting medical tests results using Driverless AI
- 2. Outline 1. poder.IO Introduction 2. H2O at poder.IO 3. Cases for Driverless AI 4. Predicting medical tests results
- 3. poder.IO Introduction Our main product is cloud platform EPICA. EPICA uses AI to predict what your audience is going to do and when with a high degree of accuracy. You can use these predictions to have granular understanding of customer journeys and personalize user’s experience at individual level across web, email, social and digital advertising.
- 4. H2O at poder.IO We update and deploy as API 100+ models daily (POJO / MOJO) • Regression / Classification (GBM, GLM, RandomForest) • Text Classification (Word2Vec +) • Time Series Patterns (iSAX) • Deep Networks (DeepWater + Tensorflow) • Etc.
- 5. Cases for Driverless AI At the moment: 1. Driverless AI as a benchmark for all models before production 2. Research Department for handle clients’ cases Planning to use in production Q3 2018: 1. Advertising Campaigns Optimization 2. Content Classification 3. Profiles Matching 4. Look-a-like models
- 6. Predicting medical tests results Disclaimer The research was supported by Bayer AG. The project was completed by the joint team of Data Scientists from RocketScience.ai and Analytics from Bayer. Currently RocketScience.ai team is a part of poder.IO.
- 7. Predicting medical tests results: Problem The research goal is to develop an approach to predict individual medical test results based on longitudinal medical and pharma claims data without direct lab measures using data-driven analytic techniques. Such discoveries may result in improved treatment strategies. TODO: // Substitute to graphics
- 8. Predicting medical tests results: Problem • Medical laboratory test, which is required for making a decision on a patient’s treatment strategy • The test results are not available in most healthcare databases • There is a need to predict the results of the test for any patient at any point of time
- 9. Predicting medical tests results: Design
- 10. Predicting medical tests results: Design
- 11. Predicting medical tests results: Data • 10 years time interval • 11 M records • 4 M unique patients • Training data: 80% • Test data: 20% • Number of raw features: ~260
- 12. Predicting medical tests results: Prerequisites Models / methods: • ETL (C++, R, ggplot2) • H2O.ai based GLM, GBM, Random Forest • H2O.ai Driverless AI Hardware: • ETL, H2O models: 128Gb / 1Tb / 32 cores • Driverless AI: AWS g3.8xlarge
- 13. Predicting medical tests results: Outcome Model Training time RMSE R2 MAE Top features GLM (ElasticNet) 00:13:20 16.477 0.5540 13.3785 100% original GBM 100% original Random Forest 100% original Ensemble (3 models) - Ensemble (9 models) - Driverless AI 00:55:15 15.913 0.5857 12.8999 46% original TODO: // Fill details
- 14. Predicting medical tests results: top 10 features TODO: // Insert table with top 10 feature intersection from different models
- 15. Predicting medical tests results: Surrogate model
- 16. Predicting medical tests results: Partial dependence
- 17. Thank you!