Generative AI Masterclass - Model Risk Management.pptx

This document discusses AI and ChatGPT. It begins with an introduction to David Cieslak and his company RKL eSolutions, which provides ERP sales and consulting. It then provides definitions for key AI concepts like artificial intelligence, generative AI, large language models, and ChatGPT. The document discusses OpenAI's ChatGPT tool and how it works. It covers prompts, commands, and potential uses and impacts of generative AI technologies. Finally, it discusses concerns regarding generative AI and the future of life institute's call for more oversight of advanced AI.

Retrieval Augmented Generation (RAG) combines the concepts of semantic search and LLM-based text generation. When a person makes a query in natural language, the query is compared to the entries in the knowledge base and most relevant results are returned to the LLM, which uses this extra information to generate more accurate and reliable response. RAG can therefore limit hallucination and provide accurate responses from reliable source. In this talk, we will present the concept of RAG and underlying concept of semantic search, and present available libraries and vector databases.

This is a gentle introduction to Natural language Generation (NLG) using deep learning. If you are a computer science practitioner with basic knowledge about Machine learning. This is a gentle intuitive introduction to Language Generation using Neural Networks. It takes you in a journey from the basic intuitions behind modeling language and how to model probabilities of sequences to recurrent neural networks to large Transformers models that you have seen in the news like GPT2/GPT3. The tutorial wraps up with a summary on the ethical implications of training such large language models on uncurated text from the internet.

第74回 Machine Learning 15minutes! Broadcast https://machine-learning15minutes.connpass.com/event/272281/

Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities? This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.

The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.

What are the "use case patterns" for deploying LLMs into production? Understanding these will allow you to spot "LLM-shaped" problems in your own industry.

LangChain is an advanced framework that allows developers to create language model-powered applications. It provides a set of tools, components, and interfaces that make building LLM-based applications easier. With LangChain, managing interactions with language models, chaining together various components, and integrating resources like APIs and databases is a breeze. The platform includes a set of APIs that can be integrated into applications, allowing developers to add language processing capabilities without having to start from scratch.

The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.

Mihai is the Principal Architect for Platform Engineering and Technology Solutions at IBM, responsible for Cloud Native and AI Solutions. He is a Red Hat Certified Architect, CKA/CKS, a leader in the IBM Open Innovation community, and advocate for open source development. Mihai is driving the development of Retrieval Augmentation Generation platforms, and solutions for Generative AI at IBM that leverage WatsonX, Vector databases, LangChain, HuggingFace and open source AI models. Mihai will share lessons learned building Retrieval Augmented Generation, or “Chat with Documents” platforms and APIs that scale, and deploy on Kubernetes. His talk will cover use cases for Generative AI, limitations of Large Language Models, use of RAG, Vector Databases and Fine Tuning to overcome model limitations and build solutions that connect to your data and provide content grounding, limit hallucinations and form the basis of explainable AI. In terms of technology, he will cover LLAMA2, HuggingFace TGIS, SentenceTransformers embedding models using Python, LangChain, and Weaviate and ChromaDB vector databases. He’ll also share tips on writing code using LLM, including building an agent for Ansible and containers. Scaling factors for Large Language Model Architectures: • Vector Database: consider sharding and High Availability • Fine Tuning: collecting data to be used for fine tuning • Governance and Model Benchmarking: how are you testing your model performance over time, with different prompts, one-shot, and various parameters • Chain of Reasoning and Agents • Caching embeddings and responses • Personalization and Conversational Memory Database • Streaming Responses and optimizing performance. A fine tuned 13B model may perform better than a poor 70B one! • Calling 3rd party functions or APIs for reasoning or other type of data (ex: LLMs are terrible at reasoning and prediction, consider calling other models) • Fallback techniques: fallback to a different model, or default answers • API scaling techniques, rate limiting, etc. • Async, streaming and parallelization, multiprocessing, GPU acceleration (including embeddings), generating your API using OpenAPI, etc.

In this event we will cover: - What is Generative AI and how it is being for future of work. - Best practices for developing and deploying generative AI based models in productions. - Future of Generative AI, how generative AI is expected to evolve in the coming years.

The document discusses advances in large language models from GPT-1 to the potential capabilities of GPT-4, including its ability to simulate human behavior, demonstrate sparks of artificial general intelligence, and generate virtual identities. It also provides tips on how to effectively prompt ChatGPT through techniques like prompt engineering, giving context and examples, and different response formats.

Session 1 👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed: Introduction to Generative AI & harnessing the power of large language models. What’s generative AI & what’s LLM. How are we using it in our document understanding & communication mining models? How to develop a trustworthy and unbiased AI model using LLM & GenAI. Personal Intelligent Assistant Speakers: 📌George Roth - AI Evangelist at UiPath 📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP 📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP

The document discusses key players in generative AI and their progress. It provides an overview of generative AI including its evolution since 1950, where the spending is focused, how the technology works, and deployment models. It then profiles several major companies leading advancements in generative AI, including their strategies, growth areas, and risks. These companies are TSMC, Nvidia, Microsoft, Google, Amazon, Tesla, Oracle, Salesforce, SAP, and Palo Alto Networks.

How to hack a LLM-integrated system using prompt injection. This is the slide-deck that I used for AVTOKYO 2023.

The document discusses different methods for customizing large language models (LLMs) with proprietary or private data, including training a custom model, fine-tuning a general model, and prompting with expanded inputs. Fine-tuning techniques like low-rank adaptation and supervised fine-tuning allow emphasizing custom knowledge without full retraining. Prompt expansion using techniques like retrieval augmented generation can provide additional context beyond the character limit.

In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.

This document provides an overview of a talk by Maxim Salnikov and Jon Jahren at Oslo Spektrum from November 7-9. It discusses using OpenAI with your own data and how to get started. Examples of enterprise use cases for generative AI are presented, such as chatbots, document indexing, and financial analysis. Tools for prompt engineering like LangChain and Semantic Kernel are introduced. Best practices for fine-tuning models on proprietary data are covered, including data formatting, training data size, and an iterative tuning process. Responsible AI techniques like grounding responses and maintaining a positive tone are also discussed.

Automated generation of high quality dataset based on your documents (pdf, csv, json, text files etc) to evaluate any LLM app endpoints

Reviewing progress in the machine learning certification journey 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid 𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran. A discussion on sample questions to aid certification exam preparation. An interactive Q&A session to clarify doubts and questions. Previewing next steps and topics, including course completions and material reviews.

In this session, we'll explore different scenarios where the features of Generative AI can provide added value to an IT solution. We'll also learn how to begin developing your own application powered by AI. Using Azure OpenAI service as an illustration, we'll examine the various APIs it offers, review the best practices of Prompt Engineering, explore different ways to incorporate your own data into the process, and take a glance at several tools and resources that make the developer experience more seamless.

The potential for combining agile and formal methods holds promise. Although it might not always be an easy partnership, it will succeed if it can foster a fruitful interchange of expertise between the two communities. In this talk I explain how formal methods can complement agile practices and vice versa. There are no pre-requisites for this talk, except an open mind and a desire to make software development more reliable. Leave any pre-conceptions at home, and be prepared for myths to be dispelled.

Agile Network India - Chennai Title: Conversational AI for Agility in Healthcare by Shiney Jeyaraj Date: March 2024 Hosted by: CGI

1) Learning user and item representations is challenging due to sparse data and shifting preferences in recommender systems. 2) The presentation outlines research at Google to address sparsity through two approaches: focused learning, which develops specialized models for subsets of data like genres or cold-start items, and factorized deep retrieval, which jointly embeds items and their features to predict preferences for fresh items. 3) The techniques have improved overall viewership and nomination of candidates, demonstrating their effectiveness in production recommender systems.

The document describes a problem prediction model that uses artificial intelligence algorithms to evaluate changes made by an IT company and anticipate potential problems. The model analyzed 194 known problems, 2,400 past changes, and 201 predicted future changes. As a result, the model identified one change from October 29, 2019 that was likely to cause a problem. A team is investigating this potential issue. The document concludes that the naive Bayes classifier model is an important tool for change analysis and problem prediction.

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.

This talk was recorded in London on Oct 30, 2018 and can be viewed here: https://youtu.be/p4iAnxwC_Eg The good news is building fair, accountable, and transparent machine learning systems is possible. The bad news is it’s harder than many blogs and software package docs would have you believe. The truth is nearly all interpretable machine learning techniques generate approximate explanations, that the fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) are very new, and that few best practices have been widely agreed upon. This combination can lead to some ugly outcomes! This talk aims to make your interpretable machine learning project a success by describing fundamental technical challenges you will face in building an interpretable machine learning system, defining the real-world value proposition of approximate explanations for exact models, and then outlining the following viable techniques for debugging, explaining, and testing machine learning models Mateusz is a software developer who loves all things distributed, machine learning and hates buzzwords. His favourite hobby data juggling. He obtained his M.Sc. in Computer Science from AGH UST in Krakow, Poland, during which he did an exchange at L’ECE Paris in France and worked on distributed flight booking systems. After graduation he move to Tokyo to work as a researcher at Fujitsu Laboratories on machine learning and NLP projects, where he is still currently based.

With organizations under intense pressure to get products out to market quickly, they can’t afford to operate within operational silos. Yet communicating and collaborating across the organizational boundaries of QA and development can be difficult. Development is typically a black box to QA teams. QA has no visibility into the quality and security of the code until late in the lifecycle. Watch this recorded webcast to learn how to break down the barriers and improve visibility and transparency by integrating development testing results into the IBM Rational Team Concert and providing QA and development with a unified workflow for ensuring code quality. Explore different development testing techniques and the types of defects and security vulnerabilities they can find. About the Presenter: James Croall, Director of Product Management, Coverity Over the last 8 years, James Croall has helped a wide range of customers incorporate static analysis into their software development lifecycle. Prior to Coverity, Mr. Croall spent 10 years in the computer and network security industry as a C/C++ and Java software engineer.

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.

The document discusses Agile software development methods and provides evidence that Agile approaches are effective. It defines Agile development as iterative and incremental with close collaboration. Case studies show organizations achieving better results with Agile, including increased productivity, quality, and customer satisfaction. Adopting Agile practices like Scrum and test-driven development enables organizations to adapt to changing priorities and deliver working software more frequently.

Serverless Toronto's 6th-anniversary event helps IT pros understand and prepare for the #GenAI tsunami ahead. You'll gain situational awareness of the LLM Landscape, receive condensed insights, and actionable advice about RAG in 2024 from Google AI Lead Mark Ryan and LlamaIndex creator Jerry Liu. We chose #RAG (Retrieval-Augmented Generation) because it is the predominant paradigm for building #LLM (Large Language Model) applications in enterprises today - and that's where the jobs will be shifting. Here is the recording: https://youtu.be/P5xd1ZjD-Os?si=iq8xibj5pJsJ62oW

Reliable, Safe & Trustworthy are some key factors to be considered for Human-Centered AI. There are certain Guidelines for Human-AI Interaction to be taken into evaluation to ensure RST systems overcome autonomy problems.

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.

A whirlwind tour of Glasswall Solution’s use of Wardley Maps and experiments with a Service-based operating model. Delivered at Open Security Summit Dec 7th, 2020 as context for a panel discussion, which you can watch here: https://www.youtube.com/watch?v=GS8Vndr-B4A The original 100-slide deck is available here: https://open-security-summit.org/tracks/2020/mini-summits/dec/wardley-maps/wardley-maps-and-services-model-at-glasswall/

This presentation dives into the practical applications of machine learning within Google's operations, providing a comprehensive overview of how to leverage AI technologies to solve real-world business challenges. Key Points Covered: - Introduction to Machine Learning at Google: Discussion on the role of ML and its evolution in enhancing Google's operational efficiency. - Experience Sharing: Insights into the team's long-term engagement with machine learning projects and the impacts on Google’s operational strategies. - Practical Applications: Real-world examples of ML applications within Google’s daily operations, providing a blueprint to adapt similar strategies. - Challenges and Solutions: Discussion on the challenges faced during the implementation of ML projects and the strategic solutions employed to overcome them. - Future of ML at Google: Insights into future trends in machine learning at Google and how they plan to continue integrating AI into their ecosystem.

The document discusses various methods for automated testing of DITA content and output, including using Schematron for validating content structure, the QA plugin for identifying tagging errors, XMLUnit for comparing XML, and the DITA OT regression test for validating the output of the open-source DITA Open Toolkit. It also covers automating browser tests using Selenium and comparing HTML output using Needle and Nose. Demo examples are provided for several of these automated testing tools and techniques.

What is Generative AI?

“AGI should be open source and in the public domain at the service of humanity and the planet.”

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.

Pritika Mehta, Co-Founder, Butternut.ai H2O Open Source GenAI World SF 2023

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.

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!

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.

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.

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.

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.

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.

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.

Luiz Pizzato, Executive Manager Artificial Intelligence, Commonwealth Bank H2O Open Source GenAI World SF 2023

En esta reunión virtual, damos una introducción a la plataforma de aprendizaje automático de código abierto número 1, H2O-3 y te mostramos cómo puedes usarla para desarrollar modelos para resolver diferentes casos de uso.

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.

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)

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)

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.

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.

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.

H2O.ai provides open source machine learning platforms and enterprise AI solutions that help companies implement artificial intelligence. It offers tools for data scientists to build models using Python and R and also provides support services to help customers successfully deploy models in production. H2O.ai aims to democratize AI and help companies become AI-driven by leveraging its experts, community knowledge, and world-class technology.

The integration of programming into civil engineering is transforming the industry. We can design complex infrastructure projects and analyse large datasets. Imagine revolutionizing the way we build our cities and infrastructure, all by the power of coding. Programming skills are no longer just a bonus—they’re a game changer in this era. Technology is revolutionizing civil engineering by integrating advanced tools and techniques. Programming allows for the automation of repetitive tasks, enhancing the accuracy of designs, simulations, and analyses. With the advent of artificial intelligence and machine learning, engineers can now predict structural behaviors under various conditions, optimize material usage, and improve project planning.

Today’s digitally connected world presents a wide range of security challenges for enterprises. Insider security threats are particularly noteworthy because they have the potential to cause significant harm. Unlike external threats, insider risks originate from within the company, making them more subtle and challenging to identify. This blog aims to provide a comprehensive understanding of insider security threats, including their types, examples, effects, and mitigation techniques.

Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)

MuleSoft Meetup on APM and IDP

CIO Council Cal Poly Humboldt September 22, 2023

The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.

Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation

Sustainability requires ingenuity and stewardship. Did you know Pigging Solutions pigging systems help you achieve your sustainable manufacturing goals AND provide rapid return on investment. How? Our systems recover over 99% of product in transfer piping. Recovering trapped product from transfer lines that would otherwise become flush-waste, means you can increase batch yields and eliminate flush waste. From raw materials to finished product, if you can pump it, we can pig it.

Support en anglais diffusé lors de l'événement 100% IA organisé dans les locaux parisiens d'Iguane Solutions, le mardi 2 juillet 2024 : - Présentation de notre plateforme IA plug and play : ses fonctionnalités avancées, telles que son interface utilisateur intuitive, son copilot puissant et des outils de monitoring performants. - REX client : Cyril Janssens, CTO d’ easybourse, partage son expérience d’utilisation de notre plateforme IA plug & play.

An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)

Cybersecurity is a major concern in today's connected digital world. Threats to organizations are constantly evolving and have the potential to compromise sensitive information, disrupt operations, and lead to significant financial losses. Traditional cybersecurity techniques often fall short against modern attackers. Therefore, advanced techniques for cyber security analysis and anomaly detection are essential for protecting digital assets. This blog explores these cutting-edge methods, providing a comprehensive overview of their application and importance.

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Six months into 2024, and it is clear the privacy ecosystem takes no days off!! Regulators continue to implement and enforce new regulations, businesses strive to meet requirements, and technology advances like AI have privacy professionals scratching their heads about managing risk. What can we learn about the first six months of data privacy trends and events in 2024? How should this inform your privacy program management for the rest of the year? Join TrustArc, Goodwin, and Snyk privacy experts as they discuss the changes we’ve seen in the first half of 2024 and gain insight into the concrete, actionable steps you can take to up-level your privacy program in the second half of the year. This webinar will review: - Key changes to privacy regulations in 2024 - Key themes in privacy and data governance in 2024 - How to maximize your privacy program in the second half of 2024

As a popular open-source library for analytics engineering, dbt is often used in combination with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models. This webinar will cover a step-by-step guide to Cosmos, an open source package from Astronomer that helps you easily run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through: - Standard ways of running dbt (and when to utilize other methods) - How Cosmos can be used to run and visualize your dbt projects in Airflow - Common challenges and how to address them, including performance, dependency conflicts, and more - How running dbt projects in Airflow helps with cost optimization Webinar given on 9 July 2024

How do we build an IoT product, and make it profitable? Talk from the IoT meetup in March 2024. https://www.meetup.com/iot-sweden/events/299487375/