Valliappa Lakshmanan
- Auteur
Valliappa (Lak) Lakshmanan is currently a Technical Lead for Data and Machine Learning Professional Services for Google Cloud. His mission is to democratize machine learning so that it can be done by anyone anywhere using Google's amazing infrastructure, without deep knowledge of statistics or programming or ownership of a lot of hardware.
Before Google, he led a team of data scientists at the Climate Corporation and was a Research Scientist at NOAA National Severe Storms Laboratory, working on machine learning applications for severe weather diagnosis and prediction.
Before Google, he led a team of data scientists at the Climate Corporation and was a Research Scientist at NOAA National Severe Storms Laboratory, working on machine learning applications for severe weather diagnosis and prediction.
Valliappa (Lak) Lakshmanan is currently a Technical Lead for Data and Machine Learning Professional Services for Google Cloud. His mission is to democratize machine learning so that it can be done by anyone anywhere using Google's amazing infrastructure, without deep knowledge of statistics or programming or ownership of a lot of hardware.
Before Google, he led a team of data scientists at the Climate Corporation and was a Research Scientist at NOAA National Severe Storms Laboratory, working on machine learning applications for severe weather diagnosis and prediction. http://aisoftwarellc.weebly.com/
Before Google, he led a team of data scientists at the Climate Corporation and was a Research Scientist at NOAA National Severe Storms Laboratory, working on machine learning applications for severe weather diagnosis and prediction. http://aisoftwarellc.weebly.com/
Boeken van Valliappa Lakshmanan
Marco Tranquillin
Valliappa Lakshmanan
Firat Tekiner
Architecting Data and Machine Learning Platforms
All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way.
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Valliappa Lakshmanan
Martin Görner
Ryan Gillard
Practical Machine Learning for Computer Vision
By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries.
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Evren Eryurek
Uri Gilad
Valliappa Lakshmanan
Anita Kibunguchy
Jessi Ashdown
Data Governance: The Definitive Guide
Valliappa Lakshmanan
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Google BigQuery: The Definitive Guide
Valliappa Lakshmanan
Automating the Analysis of Spatial Grids
Our ability to create algorithms for automatically processing gridded spatial data is increasingly important as the volume of remotely sensed imagery expands.
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