“Lak was the head of solutions for data analytics when I was Director of Product at Google. He ran a key area of the business, and there couldn't have been a better person for the job. His ability to come up with interesting ways to tie together technologies to solve problems is a very strong trait, and helped numerous customers in his role at Professional Services at Google and then in Solutions Engineering. This trait also make his books and blog posts informative and interesting to read, even if you already are familiar with the subject matter. The folks on his team partnered with Product Managers to take data analytics technologies to market, and they collaborated very well with my team. Lak was my co-author for "BigQuery, the definitive guide". He was astonishingly productive, knocking out chapters of really great content in almost no time. At the same time, he was building a solutions team, writing blog posts, and helping give feedback on a half dozen products at Google. And next time I turned around, he had written another book and a major update to our book. As one of the founding engineers of Google BigQuery, and someone who worked on it for 10 years, there are very few people who know the product better than I do, and Lak is one of them.”
Valliappa Lakshmanan
Bellevue, Washington, United States
12K followers
500+ connections
About
Lak is an operating executive at Silver Lake, an investment firm. He helps management…
Experience
Education
Volunteer Experience
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Member of the Board of Advisors
University of Oklahoma – Gallogly College of Engineering
- Present 1 year
One of 30 distinguished alumni who provide counsel, connections, and funding to promote the vision, goals, and objectives of the college, notably the vision of producing engineering graduates sought among the first by industry and investors.
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Industry Advisory Board (Data Analytics Program)
Washington State University
- Present 7 years
One of 10 business leaders across the Pacific Northwest who plays a vital role in developing the WSU data analytics curriculum so that graduates are ready to meet industry needs and expectations.
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Science Advisory Board
Allen Institute for Immunology
- 2 years
The institute is focused on accelerating foundational research, developing standards and models, and cultivating new ideas to make a broad, transformational impact on science
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Member of the Board of Advisors
Conrad Blucher Institute for Surveying & Science
- 5 years
The institute conducts innovative geospatial science research and serves as a focused resource area for geospatial datasets relevant to the coastal environment.
Publications
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Architecting Data and Machine Learning Platforms
O'Reilly Media, Inc
All cloud architects need to know how to build data platforms—the key to enabling businesses with data and delivering enterprise-wide intelligence in a fast and efficient way. This handbook is ideal for learning how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, or multicloud tools like Fivetran, dbt, Snowflake, and Databricks.
Other authorsSee publication -
Data Science on the Google Cloud Platform
O'Reilly
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP.
Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own…Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP.
Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. -
Practical machine learning for computer vision
O'Reilly Media, Inc
Machine learning on images is revolutionizing healthcare, manufacturing, retail, and many other sectors. Many previously difficult problems can now be solved by training machine learning (ML) models to identify objects in images. Our aim in this book is to provide intuitive explanations of the ML architectures that underpin this fast-advancing field, and to provide practical code to employ these ML models to solve problems involving classification, measurement, detection, segmentation…
Machine learning on images is revolutionizing healthcare, manufacturing, retail, and many other sectors. Many previously difficult problems can now be solved by training machine learning (ML) models to identify objects in images. Our aim in this book is to provide intuitive explanations of the ML architectures that underpin this fast-advancing field, and to provide practical code to employ these ML models to solve problems involving classification, measurement, detection, segmentation, representation, generation, counting, and more.
Other authorsSee publication -
Machine Learning Design Patterns
O'Reilly Media
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow.
The authors, three Google Cloud engineers, describe 30 patterns for data and problem…The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow.
The authors, three Google Cloud engineers, describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the most appropriate remedy for your situation.
Other authorsSee publication -
BigQuery the definitive guide
O'Reilly Media
Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you’ll examine how to analyze data at scale to derive insights from large datasets efficiently.
Other authorsSee publication -
Automating the analysis of spatial grids
Springer
The ability to create automated algorithms to process gridded spatial data is increasingly important as remotely sensed datasets increase in volume and frequency. Whether in business, social science, ecology, meteorology or urban planning, the ability to create automated applications to analyze and detect patterns in geospatial data is increasingly important. This book provides students with a foundation in topics of digital image processing and data mining as applied to geospatial datasets…
The ability to create automated algorithms to process gridded spatial data is increasingly important as remotely sensed datasets increase in volume and frequency. Whether in business, social science, ecology, meteorology or urban planning, the ability to create automated applications to analyze and detect patterns in geospatial data is increasingly important. This book provides students with a foundation in topics of digital image processing and data mining as applied to geospatial datasets. The aim is for readers to be able to devise and implement automated techniques to extract information from spatial grids such as radar, satellite or high-resolution survey imagery.
Organizations
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American Meteorological Society
Fellow
- PresentElected for my work on pioneering the use of AI in severe weather forecasting, the AMS Fellow is an honor given to only 0.2% of meteorology professionals
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