We’re excited to bring you the latest happenings in AI, Machine Learning, Deep Learning, Data Science and Big Data.
Who are we? We’re H2O.ai ( https://www.h2o.ai/ ), creators of the world’s leading open-source machine learning platform, used by hundreds of thousands of data scientists and 18,000 organizations around the world.
Our goal is to congregate with data enthusiasts and discuss trending topics in the world of AI. We also regularly invite esteemed industry influencers and thought leaders who talk shop on all things data science.
In this virtual meetup, we will start off with an introduction to the basics of Machine Learning and how it is being applied to a variety of industry use cases. We will then provide overviews of H2O-3, the #1 open-source machine learning platform. We'll walk you through demos of H2O-3 and highlight its features and capabilities.
What you will learn:
- Introduction to machine learning
- Overviews of the features and capabilities of H2O-3
- Live demo H2O-3 applied to real-world datasets
Parul Pandey, Kaggle Grandmaster and Data Scientist at H2O.ai
Parul is a Kaggle Grandmaster and Data Scientist 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.
According to the International Society of Automation, a typical factory loses between 5% and 20% of its manufacturing capacity due to downtime. Traditional preventive maintenance processes require machines to be repaired at intervals based on time or usage. These methods, however, still result in significant instances of equipment failure resulting in idle workers, increased scrap rates, lost revenues, and angry customers.
AI-based predictive maintenance uses a variety of data from IoT sensors embedded in equipment, data from manufacturing operations, environmental data, and more to determine which components should be replaced before they break down. AI models can look for patterns in data that indicate failure modes for specific components or generate more accurate predictions of the lifespan for a component given environmental conditions. When specific failure signals are observed, or component aging criteria are met, the components can then be replaced during scheduled maintenance windows.
In this virtual meetup, we will go over AI as a solution for predictive maintenance and look at a live demonstration of tools using real-world datasets.
Sairaam Varadarajan, H2O.ai
Sai is a Customer Data Scientist at H2o.ai. Prior to H2O, Sai was working as a Senior Data Scientist at Dexcom, a diabetes management system company based in San Diego. He has also worked for Medtronic, healthcare technology company based in Minneapolis, and Larsen and Toubro, India.
He has more than 10 years of experience in the DS / ML field in developing and deploying machine learning models in healthcare and the manufacturing domain. Sai holds a degree in Mechanical Engineering and Statistics and pursued some interests in computer science academia. His passion lies in solving real-life problems through Machine Learning and data sciences.