Introducción al Machine Learning Automático

This document discusses generative AI and its potential transformations and use cases. It outlines how generative AI could enable more low-cost experimentation, blur division boundaries, and allow "talking to data" for innovation and operational excellence. The document also references responsible AI frameworks and a pattern catalogue for developing foundation model-based systems. Potential use cases discussed include automated reporting, digital twins, data integration, operation planning, communication, and innovation applications like surrogate models and cross-discipline synthesis.

This document provides a technical introduction to large language models (LLMs). It explains that LLMs are based on simple probabilities derived from their massive training corpora, containing trillions of examples. The document then discusses several key aspects of how LLMs work, including that they function as a form of "lossy text compression" by encoding patterns and relationships in their training data. It also outlines some of the key elements in the architecture and training of the most advanced LLMs, such as GPT-4, focusing on their huge scale, transformer architecture, and use of reinforcement learning from human feedback.

Deck for session entitled "Unleashing the Power of Generative AI: Python API Integration with ChatGPT, DALL-E, and D-ID Studio" presented at PyCon Ireland Conference on November 11th 2023

Session delivered at Malaga, Spain in the Wey Wey Web conference about how to use and integrate IA, ChatGPT and other LLMs into your websites including: plugins, how ChatGPT browses the web, and how to use prompt engineering for formatted data generation. AI is everywhere nowadays, but if you are a web developer, you don't know where it fits in your work. In this session, you will quickly understand how to add AI models to your website. You will also see how ChatGPT plugins work, how to create one, and how to gain control of the content used by LLMs. In this session, you'll learn about API integration with OpenAI and Google LaMDA APIs, tokens, and how to keep things secure while scaling up. We'll walk you through real examples and hands-on demos, so you'll be ready to bring AI magic to your web projects quickly. But that's not all! We'll also discuss how to create your plugin for LLMs, how Bing Chat and ChatGPT browser plugin works when browsing your web content, and how to opt out or optimize the results for AI. We'll cover basic concepts of data preprocessing, structuring, and how to tweak the model for your needs. Let's have fun and unlock ChatGPT and AI's power together!

The document discusses how the Covid-19 pandemic has accelerated the shift to a digital economy. It discusses several impacts of the pandemic, including how it triggered the worst economic crisis since WWII and accelerated existing trends like digitalization. The pandemic has led to a permanent increase in digital transactions and payments. It also discusses how the pandemic showed that degrowth is possible through policies like limiting social practices and increasing community cooperation. The shift to remote work has also strengthened networks as the foundation of the new digital economy.

The document discusses how Azure helps build better business processes and customer experiences with AI. It provides an overview of Azure OpenAI and its capabilities for various industries like finance, marketing, and HR. The document also includes examples of how companies like CarMax and Strabag SE have used Azure OpenAI to improve efficiency, reduce costs, and provide better customer service.

Generative AI refers to algorithms that create new data similar to input data using techniques like GANs, VAEs, and RNNs to generate content like images, text, and music. It is used in applications such as art and design, gaming, and content generation. Some challenges of generative AI include data bias, ethical considerations, and ensuring advancements do not negatively impact privacy or authenticity. The RAG (Retrieval-Augmented Generation) model integrates text generation and retrieval to provide precise responses to queries and is used in conversational AI, information retrieval, and question answering systems.

The document discusses how artificial intelligence will impact the future of work. It notes that by 2030, Gartner predicts that 80% of today's project management tasks will be eliminated as AI takes over. It also lists the top 10 jobs that are likely to be adopted by companies using AI and other emerging technologies by 2022. The document emphasizes skills like analytical thinking, active learning, and emotional intelligence as important for the future of work as jobs change. It provides references to additional reports on AI, automation, and the future of jobs.

Artificial intelligence has made great strides in the area of content generation. From translating straightforward text instructions into images and videos to creating poetic illustrations and even 3D animation, there is no limit to AI’s capabilities, especially in terms of image synthesis.

The presentation "ITDays_2023_GeorgeBara" discusses challenges in adopting AI large language models (LLMs) in enterprise settings. The presentation covers: 1. **Challenges in AI LLMs adoption**: It highlights the noise in the current AI landscape and questions the practical use of AI in real businesses. 2. **The DNA of an Enterprise**: Defines enterprise sizes and discusses the new solutions adoption process, emphasizing effective integration and minimizing disruption. 3. **Enterprise-Grade**: Lists qualities like robustness, reliability, scalability, performance, security, and support that are essential for enterprise-grade solutions. 4. **What are LLMs?**: Describes the pre-ChatGPT era with BERT, a model used for language understanding, and details its enterprise applications. 5. **LLM use-cases before ChatGPT**: Focuses on data triage, process automation, knowledge management, and the augmentation of business operations. 6. **EU Digital Decade Report**: Points out that AI adoption in Europe is slow and might not meet the 2030 targets. 7. **Adoption Challenges**: Addresses top challenges such as data security, predictability, performance, control, regulatory compliance, ethics, sustainability, and ROI. 8. **Conclusion**: Reflects on the slow adoption of AI in enterprises, suggesting that a surge might occur once the technology matures and is ready for enterprise use. The presenter concludes by stating that despite the hype around technologies like ChatGPT, enterprises are cautious and will adopt new technologies at their own pace. He anticipates a gradual then sudden adoption pattern once LLMs are proven to be enterprise-ready.

Korean strategy for 4th Industrial Revolution should be accompanied by the grand scale shifts of national paradigm. The proud miracle of Han-River was achieved by the swift pursuit of predictable objective through the quick follower strategy. However, the quick follower strategy which focuses on efficiency is not appropriate to adopt the 4th Industrial Revolution. The future vision is based on the history; therefore, the national consciousness on the shared national vision and values should be formed. The country that has lost its pride in history has never became the first-class nation. We need to perceive this 4th Industrial Revolution era as the opportunity from the sky for us to transform the paradigm of the past follower strategy.

This document summarizes a presentation given by Professor Pekka Abrahamsson on how ChatGPT and AI-assisted coding is profoundly changing software engineering. The presentation covers several key points: - ChatGPT and AI tools like Copilot are beginning to be adopted in software engineering to provide code snippets, answers to technical questions, and assist with debugging, but issues around code ownership, reliability, and security need to be addressed. - Early studies show potential benefits of ChatGPT for tasks like software testing education, code quality improvement, and requirements elicitation, but more research is still needed. - Prompt engineering techniques can help maximize the usefulness of ChatGPT for software engineering tasks. Overall, AI

I will talk about Generative AI and its applications to 2D art production in the gaming industry. We will explore the Stable Diffusion neural net and concepts such as Prompt Engineering, Image-to-Image, ControlNet, and Dreambooth and how they can enhance game development. Moreover, we will compare the pros and cons of Stable Diffusion with Midjourney. As a result, you will better understand the potential benefits of incorporating generative AI into your game development workflow.

Although many manufacturers want to get a Digital Transformation project going, they feel hesitant about investing major time and effort into a project that may not deliver the desired results. However, just imagine if you could achieve a quick win for Digital Transformation in only 90 minutes!

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.

This document discusses using AI chatbots for customer experience in retail businesses. It provides examples of chatbot uses in various industries like travel, food/beverage, banking, healthcare, and retail/fashion. It also discusses emotion AI and chatbots that can detect and respond to human emotions. Finally, it introduces AIELA, a blockchain-based emotion AI platform that uses chatbots and missions to better understand customer needs and provide rewards.

Give a background of Data Science and Artificial Intelligence, to better understand the current state of the art (SOTA) for Large Language Models (LLMs) and Generative AI. Then start a discussion on the direction things are going in the future.

This slide was presented by Dmitry Baev, Pratap Ramamurthy and Karthik Kannappan at our AWS DevDay in Toronto, Canada on July 17, 2019

Diego Oppenheimer discusses the rise of algorithm marketplaces and the new "algorithm economy". Key points include: - Advances in machine learning, computer vision, speech recognition and natural language processing are enabling algorithms to interpret unstructured data at scale. - Algorithm marketplaces allow algorithms to be hosted, discovered, monetized and composed modularly to address a wide range of use cases across many industries. - The algorithm economy will lower barriers to applying machine intelligence and foster innovation as algorithms become reusable assets that creators and users can both benefit from.

In this presentation we talked about how macnica.ai is preparing to provide AI as solutions to Japanese enterprises and business.

This document introduces H2O.ai and their product H2O Driverless AI, an automated machine learning platform. It provides an overview of the company and product, including use cases across different industries where customers saw improvements in key metrics like accuracy, time savings, and performance. It also shares resources for learning more about Driverless AI through tutorials, documentation, and community forums.

This session was recorded in San Francisco on February 4th, 2019 and can be viewed here: https://youtu.be/oQfFPPUg5t8 Bio: Arno Candel is the Chief Technology Officer at H2O.ai. He is the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine-learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He was named “2014 Big Data All-Star” by Fortune Magazine and featured by ETH GLOBE in 2015. Follow him on Twitter: @ArnoCandel.

IBM Cloud Private for Data, an ultimate platform for all AI, ML and Data Science workloads. Integrated analytics platform based on Containers and micro services. Works with Kubernetes and dockers, even with Redhat openshift. Delivers the variety of business use cases in all industries- FS, Telco, Retail, Manufacturing etc

At H2O.ai we see a world where all software will incorporate AI, and we’re focused on bringing AI to business through software. H2O.ai is the maker behind H2O, the leading open source machine and deep learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark. In this webinar, you will learn about the scalable H2O core platform and the distributed algorithms it supports. H2O integrates seamlessly with the R and the Python environments. We will show you how to leverage the power of H2O algorithms in R, Python and H2O Flow interface. Come with an open mind and some high level knowledge of machine learning, and you will take away a stream of knowledge for your next ML/DL project. Amy Wang is a math hacker at H2O, as well as the Sales Engineering Lead. She graduated from Hunter College in NYC with a Masters in Applied Mathematics and Statistics with a heavy concentration on numerical analysis and financial mathematics. Her interest in applicable math eventually lead her to big data and finding the appropriate mediums for data analysis. Desmond is a Senior Director of Marketing at H2O.ai. In his 15+ years of career in Enterprise Software, Desmond worked in Distributed Systems, Storage, Virtualization, MPP databases, Streaming Analytics Platform, and most recently Machine Learning. He obtained his Master’s degree in Computer Science from Stanford University and MBA degree from UC Berkeley, Haas School of Business.

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/07/uncode-ai-code-free-development-delivery-and-maintenance-of-edge-ai-applications-a-presentation-from-blaize/ Dmitry Zakharchenko, Vice President of Research and Product Development at Blaize, presents the “Uncode AI: Code-free Development, Delivery and Maintenance of Edge AI Applications” tutorial at the May 2021 Embedded Vision Summit. Software is becoming the critical differentiator in AI edge app development and adoption. This presentation examines a new approach for enterprises challenged to take AI edge products from idea to production fast, but lacking the right talent, tools and capacity to deliver. Zakharchenko provides a detailed look at the new Blaize AI Studio Platform, the industry’s first end-to-end open software platform for the complete AI edge application lifecycle. Zakharchenko examines how a code-free visual interface, contextually intelligent assistive agent, and knowledge-driven recommendation systems open doors for AI developers, systems builders and subject matter experts to easily develop, deploy and manage AI edge applications. He also touches on the implications of the emergence of easy-to-use AI systems, and a vision for the potential of these systems to be deployment vehicles for widespread AI use case innovation and affordable AI applications.

This document provides an overview of artificial intelligence trends and applications in development and operations. It discusses how AI is being used for rapid prototyping, intelligent programming assistants, automatic error handling and code refactoring, and strategic decision making. Examples are given of AI tools from Microsoft, Facebook, and Codota. The document also discusses challenges like interpretability of neural networks and outlines a vision of "Software 2.0" where programs are generated automatically to satisfy goals. It emphasizes that AI will transform software development over the next 10 years.

From Amazon to Google, top technology firms have embraced data science and machine learning to improve business outcomes. Yet AI adoption beyond these firms has been slow due to obstacles such as hiring talent, heterogeneous data, and compute infrastructure. Larger firms have built teams to tackle these issues with some success, but small- and mid-tier firms are at a distinct disadvantage. AI as a Service is a paradigm that levels the playing field and empowers businesses across the spectrum.

We are entering a new era of software development. Companies are realizing that AI and machine learning are critical to success in business, both to save cost on repetitive tasks, and to enable to new features and products that would be impossible without machine intelligence. Algorithmia makes these tools available through web APIs that makes tools like computer vision and natural language processing available to companies everywhere. Kenny will talk about how sharing of intelligent APIs can improve your applications. https://rakutentechnologyconference2016.sched.org/event/8aS5/using-algorithmia-to-leverage-ai-and-machine-learning-apis Rakuten Technology Conference 2016 http://tech.rakuten.co.jp/

This document discusses Microsoft's Azure OpenAI Service and how it can be used to build applications using large language models. Some key points: - Azure OpenAI Service provides access to models from OpenAI like GPT-3 through Microsoft's Azure cloud platform while ensuring security, privacy and responsible AI. - It allows generating complex documents, steering models with nuanced instructions, and customizing models for any language or dialect. - Example capabilities include content generation, summarization, code generation, and semantic search. These can be applied to use cases like call center analytics, software documentation, and marketing content creation. - Tools are discussed for developing applications using prompt engineering, grounding models with domain-specific

The document summarizes the agenda and key topics from a UiPath Community Geneva chapter reboot session held on November 2nd, 2023. The agenda included an introduction, announcements about new UiPath products like Forward VI and Autopilot, highlights of the 23.10 product release including new features for process mining, communications mining, and SAP testing, and a discussion around themes or use cases members would like to see covered in future sessions. Members provided suggested future session topics such as document understanding, test automation, citizen development strategies, process mining strategies for small/medium organizations, automation governance best practices, and AI-enabled automation in finance.

This is my Deep Water talk for the TensorFlow Paris meetup. Deep Water is H2O's integration with multiple open source deep learning libraries such as TensorFlow, MXNet and Caffe. On top of the performance gains from GPU backends, Deep Water naturally inherits all H2O properties in scalability. ease of use and deployment.

This document provides an agenda and overview for a tutorial on building shared AI services. The tutorial consists of two modules: the first module discusses a case study of AI as a service and challenges of traditional machine learning, and how deep learning can help address these challenges. The second module introduces Keras and options for running Keras on Spark, including a use case, code lab, and prerequisites for running the code lab in Docker containers.

Helixa uses serverless machine learning architectures to power an audience intelligence platform. It ingests large datasets and uses machine learning models to provide insights. Helixa's machine learning system is built on AWS serverless services like Lambda, Glue, Athena and S3. It features a data lake for storage, a feature store for preprocessed data, and uses techniques like map-reduce to parallelize tasks. Helixa aims to build scalable and cost-effective machine learning pipelines without having to manage servers.

The document discusses artificial intelligence (AI) and Capgemini's approach to AI. It provides examples of how AI can be applied in different industries and business functions. It also outlines Capgemini's AI platform, principles, and offerings. Capgemini aims to help clients implement impactful and scalable AI solutions through a combination of technology, services, and ecosystem partnerships.

Every business is looking for a game-changer in data science, machine learning, and AI. Most organizations are also looking for ways to tap into open-source and commercial data science tools such as Python, RStudio, Apache Spark, Jupyter, and Zeppelin notebooks, to accelerate predictive and machine learning model building and deployment while leveraging the scale, security and governance of the Hortonworks Data Platform and other commercial platforms. Ana Maria Echeverri will demonstrate how to accelerate data science, machine learning, and deep learning workflows by using IBM Watson Studio, an integrated environment for data scientists, application developers, and subject matter experts. This suite of tools allows to collaboratively connect to data, wrangle that data and use it to build, train and deploy models at scale while using Open Source skills (i.e.: Python) and expanding into cognitive capabilities through access to Watson APIs to build AI-powered applications. If you love Python and want to tap into the power of IBM Watson, this is the session for you.

Memoori's 10th Webinar in the 2019 Smart Buildings Series. We spoke with Chris Irwin, VP Sales EMEA & Asia at J2 Innovations about the FIN 5 software framework and “Simplifying Building Automation by Leveraging Semantic Tagging with a New Breed of Software”.

Building a Real-Time Security Application Using Log Data and Machine Learning- Karthik Aaravabhoomi - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

“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.

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.

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.

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.

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.

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.

- Jon McKinney, Director of Research, H2O.ai - Arno Candel, Chief Technology Officer, H2O.ai H2O Open Source GenAI World SF 2023

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.

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.

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)