Machine Learning at Progressive with H2O

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Machine Learning at Progressive with H2O by Edward Agarwala and Scott Marsh

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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.

Open-Source AI: Community is the Way

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Building Custom GenAI Apps at H2O

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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.

Applied Gen AI for the Finance Vertical

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Cutting Edge Tricks from LLM Papers

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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.

Practitioner's Guide to LLMs: Exploring Use Cases and a Glimpse Beyond Curren...

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Introducción al Aprendizaje Automatico con H2O-3 (1)

Sri Ambati

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.

From Rapid Prototypes to an end-to-end Model Deployment: an AI Hedge Fund Use...

Sri Ambati

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 - Shifting to the Next Step in Your AI Transfo...

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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)

AI Foundations Course Module 1 - An AI Transformation Journey

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Machine Learning Model Deployment and Scoring on the Edge with Automatic Machine Learning and Data Flow YouTube Video URL: https://youtu.be/gB0bTH-L6DE Deploying Machine Learning models to the edge can present significant ML/IoT challenges centered around the need for low latency and accurate scoring on minimal resource environments. H2O.ai's Driverless AI AutoML and Cloudera Data Flow work nicely together to solve this challenge. Driverless AI automates the building of accurate Machine Learning models, which are deployed as light footprint and low latency Java or C++ artifacts, also known as a MOJO (Model Optimized). And Cloudera Data Flow leverage Apache NiFi that offers an innovative data flow framework to host MOJOs to make predictions on data moving on the edge.

ML Model Deployment and Scoring on the Edge with Automatic ML & DF

Sri Ambati

This presentation was made on June 30th, 2020. Recording of the presentation is available here: https://youtu.be/9LajqAL_CU8 As enterprises “make their own AI”, a new set of challenges emerge. Maintaining reproducibility, traceability, and verifiability of machine learning models, as well as recording experiments, tracking insights, and reproducing results, are key. Collaboration between teams is also necessary as “model factories” are created for enterprise-wide model data science efforts. Additionally, monitoring of models ensures that drift or performance degradation is addressed with either retraining or model updates. Finally, data and model lineage in case of rollbacks or addressing regulatory compliance is necessary. H2O ModelOps delivers centralized catalog and management, deployment, monitoring, collaboration, and administration of machine learning models. In this webinar, we learn how H2O can assist with operationalizing, scaling and managing production deployments. Speaker's Bio: Felix is a part of the Customer Success team in Asia Pacific at H2O.ai. An engineer and an IIM alumni, Felix has held prominent positions in the data science industry.

Scaling & Managing Production Deployments with H2O ModelOps

Sri Ambati

This presentation was made on June 18, 2020. Video recording of the session can be viewed here: https://youtu.be/YEtDwYSXXJo For many companies, model documentation is a requirement for any model to be used in the business. For other companies, model documentation is part of a data science team’s best practices. Model documentation includes how a model was created, training and test data characteristics, what alternatives were considered, how the model was evaluated, and information on model performance. Collecting and documenting this information can take a data scientist days to complete for each model. The model document needs to be comprehensive and consistent across various projects. The process of creating this documentation is tedious for the data scientist and wasteful for the business because the data scientist could be using that time to build additional models and create more value. Inconsistent or inaccurate model documentation can be an issue for model validation, governance, and regulatory compliance. In this virtual meetup, we will learn how to create comprehensive, high-quality model documentation in minutes that saves time, increases productivity, and improves model governance. Speaker's Bio: Nikhil Shekhar: Nikhil is a Machine Learning Engineer at H2O.ai. He is currently working on our automatic machine learning platform, Driverless AI. He graduated from the University of Buffalo majoring in Artificial Intelligence and is interested in developing scalable machine learning algorithms.

Automatic Model Documentation with H2O

Sri Ambati

This presentation was made on June 16, 2020. A recording of the presentation can be viewed here: https://youtu.be/khjW1t0gtSA AI is unlocking new potential for every enterprise. Organizations are using AI and machine learning technology to inform business decisions, predict potential issues, and provide more efficient, customized customer experiences. The results can enable a competitive edge for the business. H2O.ai is a visionary leader in AI and machine learning and is on a mission to democratize AI for everyone. We believe that every company can become an AI company, not just the AI Superpowers. We are empowering companies with our leading AI and Machine Learning platforms, our expertise, experience and training to embark on their own AI journey to become AI companies themselves. All companies in all industries can participate in this AI Transformation. Tune into this virtual meetup to learn how companies are transforming their business with the power of AI and where to start. About Parul Pandey: Parul is a Data Science Evangelist here at H2O.ai. She combines Data Science , evangelism and community in her work. Her emphasis is to spread the information about H2O and Driverless AI to as many people as possible, She is also an active writer and has contributed towards various national and international publications.

Your AI Transformation

Sri Ambati

This presentation was made on June 11, 2020. Recording from the presentation can be viewed here: https://youtu.be/02Gb062U_M4 The manufacturing industry is adopting artificial intelligence (AI) at a fast rate. This century-old industry is complex but has seen constant transformation across all of its facets. Led by big data analytics, miniaturization of sensors enabling the Internet of Things (IoT), and, now, AI machine learning (ML), manufacturers everywhere have embarked on an AI transformation that is opening up potential new revenue streams as well taking costs and time out of existing processes. This talk will walk through a use case for enterprise AI solutions within the manufacturing sector. We will discuss the challenges, motivation, and tool selection process, then cover the solution development in detail. Speaker Bio: eRic is armed with the technical know-how of Data Science, Machines Learning, and Big Data Analytics. He. is equipped with skill-sets to value-add businesses exploring into areas of Artificial Intelligence (AI) with an AI consultation approach. Translating BDA, Machine Learning, and AI into Business Values. eRic CHOO had spent the last 8 years in the IT industry from integration of Infrastructure (Storage and Back-up) solutions to Advance Analytics Software specializing in BDA, Machines Learning, and AI. Before joining the IT industry, he had vast experience in the Semiconductor industry, thus a deep understanding in advance manufacturing processes. SIONG Jong Hang works as a Solutions Engineer/Data Scientist at H2O.ai based in Singapore where he helps business, government, academia, and non-profit organizations in their transformation into AI. Prior to H2O.ai, he has worked at the Quant Group at Bank of America Merrill Lynch in Hong Kong and Teradata in Singapore as a data scientist. He has completed data science projects for various verticals in Europe and Asia. After hours, he’s an avid learner and has attained 100 MOOC certificates in various fields such as AI, science, engineering, and maths. He has also authored articles to instill interest in science, technology as well as AI.

AI Solutions in Manufacturing

Sri Ambati

This presentation was made on June 9th, 2020. Video recording of the session can be viewed here: https://youtu.be/OCB9sTUnUug In this meetup with Sanyam Bhutani, Machine Learning Engineer at H2O.ai, he gives a recap of the eight annual ICLR (International Conference on Learning Representations) 2020 - a niche deep learning conference whose focus is to study how to learn representations of data, which is basically what deep learning does. Sanyam goes through a few of his favorite selected papers from this year’s ICLR, note this session may not be able to capture the richness of all papers or allow a detailed discussion. You will be able to find Sanyam in our community slack (https://www.h2o.ai/slack-community/), please feel free to start a discussion with him, if you send a  emoji greeting, you’ll find the answers. Following are the papers we will look into: U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty Your classifier is secretly an energy based model and you should treat it like one ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators ALBERT: A Lite BERT for Self-supervised Learning of Language Representations Reformer: The Efficient Transformer Generative Models for Effective ML on Private, Decentralized Datasets Once for All: Train One Network and Specialize it for Efficient Deployment Thieves on Sesame Street! Model Extraction of BERT-based APIs Plug and Play Language Models: A Simple Approach to Controlled Text Generation BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning Real or Not Real, that is the Question

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