AI and the Future of Software Development: A Sneak Peek

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Sri Ambati

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

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AI and the Future of Software Development: A Sneak Peek

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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.

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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.

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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.

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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.

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