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Spotlight on AI at Google Cloud Next '18 – SyncedReview – Medium

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Artificial intelligence has become a sort of secret weapon in the battle to build the best cloud service platform. Google Cloud Platform is currently the underdog, trailing both Amazon Web Services and Microsoft Azure. But Google is betting robust AI will give it the edge it needs to catch up. At the annual Google Cloud Next conference which kicked off July 24 in San Francisco the company unveiled a series of AI-based product releases and enhancements for its analytics and machine learning tools, additional applications on G Suite, and new IoT products. Earlier this week, Google parent company Alphabet reported its Q2 earnings, which were ahead of Wall Street's expectations.


Review: Google Cloud AutoML is truly automated machine learning

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When you're trying to train the best machine learning model for your data automatically, there's AutoML, or automated machine learning, and then there's Google Cloud AutoML. Google Cloud AutoML is a cut above. In the past I've reviewed H2O Driverless AI, Amazon SageMaker, and Azure Machine Learning AutoML. Driverless AI automatically performs feature engineering and hyperparameter tuning, and claims to perform as well as Kaggle masters. Azure Machine Learning AutoML automatically sweeps through features, algorithms, and hyperparameters for basic machine learning algorithms; a separate Azure Machine Learning hyperparameter tuning facility allows you to sweep specific hyperparameters for an existing experiment.


Beginners guide to machine learning

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Building AI-powered apps can be painful. I've endured a lot of that pain because the payout of using this technology is often worth the suffering. The juice is worth the squeeze, as they say. Happily, over the past five years, developing with machine learning has gotten much easier thanks to user-friendly tooling. Nowadays I find myself spending very little time building and tuning machine learning models and much more time on traditional app development. In this post, I'll walk you through some of my favorite, painless Google Cloud AI tools and share my tips for building AI-powered apps fast.


Beginners Guide To Painless Machine Learning - Liwaiwai

#artificialintelligence

Building AI-powered apps can be painful. I've endured a lot of that pain because the payout of using this technology is often worth the suffering. The juice is worth the squeeze, as they say. Happily, over the past five years, developing with machine learning has gotten much easier thanks to user-friendly tooling. Nowadays I find myself spending very little time building and tuning machine learning models and much more time on traditional app development. In this post, I'll walk you through some of my favorite, painless Google Cloud AI tools and share my tips for building AI-powered apps fast.


Automated machine learning or AutoML explained

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The two biggest barriers to the use of machine learning (both classical machine learning and deep learning) are skills and computing resources. You can solve the second problem by throwing money at it, either for the purchase of accelerated hardware (such as computers with high-end GPUs) or for the rental of compute resources in the cloud (such as instances with attached GPUs, TPUs, and FPGAs). On the other hand, solving the skills problem is harder. Data scientists often command hefty salaries and may still be hard to recruit. Google was able to train many of its employees on its own TensorFlow framework, but most companies barely have people skilled enough to build machine learning and deep learning models themselves, much less teach others how.