Google and Kaggle join hands to boost machine learning and AI
Founded in 2012, Kaggle is an online platform which hosts machine learning and data science competitions. It has half a million data scientists on its platform, which helps various organizations to solve real-world problems. Data scientists at Kaggle analyze data provided by organizations and try to make sense out of it as required. According to Crunchbase, Kaggle raised $12.5 million since its launch in 2010.
Google joining Kaggle:
Kaggle has a bit of a history with Google. Earlier in March 2017, Google and Kaggle teamed up to host a $100,000 machine learning competition around classifying YouTube videos. On 8th March 2017, Google made the announcement of acquiring Kaggle at its Google Cloud Next conference held in San Francisco. However, Anthony Goldbloom, CEO of Kaggle announced that “The Kaggle team will remain together and will continue Kaggle as a distinct brand within Google Cloud and Kaggle Kernels will continue to support a diverse ecosystem of machine learning libraries and packages supported by Google as well as those outside of Google’s toolkit”
Implications for Google and Kaggle after the acquisition:
- Google now has an access to a huge community of talented Data Scientists, which they can use to recruit talent.
- Google recently released Tensorflow 1.0. Acquiring Kaggle is the best way to promote this library and making sure that people work with it.
- Google can now compete with Microsoft Azure ML and Amazon Web services, which are bigger than Google Cloud.
- More data scientists and enthusiasts to test and help improve Google’s Cloud Platform.
- For Kaggle, integrating Google Cloud is a good way to improve their performances, have an easy use of larger datasets, and host more ambitious competitions.
For Google, acquiring Kaggle is part of a necessary progression for AI technologies, including natural language processing, predictive analytics and machine learning tools intended to achieve breakthroughs. Additionally, with Kaggle, Google bought one of the largest and most active communities for data scientists!
According to a report from CBS News, almost half of United States citizens are represented in a facial recognition database...
Asset Optimization is a structured approach to improving the effectiveness of overall asset management by classifying, defining, scheduling and implementing initiatives for better performance and decision making...
Google is also developing another deep learning algorithm that surveys biopsy images to locate metastatic breast cancer that has spread to the lymph nodes. The algorithm was tested...