The question: Spending Time Rolling Your Own or Using Google Tools in Anger? is one faced by many people who have watched computer technology evolve.
You could write your own blogging software or you can use one of the standard distributions.
You could write your own compiler or you can use one of the standard distributions.
You can install and maintain your own machine learning, big data apps, or you can use the tools offered by Google Machine Learning.
Tinkering with your local system until it is “just so” is fun, but it eats into billable time and honestly is a distraction.
Not promising I immersing in the Google-verse but an honest assessment of where to spend my time is in order.
Google takes Cloud Machine Learning service mainstream by Fausto Ibarra, Director, Product Management.
From the post:
Hundreds of different big data and analytics products and services fight for your attention as it’s one of the most fertile areas of innovation in our industry. And it’s no wonder; the most amazing consumer experiences are driven by insights derived from information. This is an area where Google Cloud Platform has invested almost two decades of engineering, and today at GCP NEXT we’re announcing some of the latest results of that work. This next round of innovation builds on our portfolio of data management and analytics capabilities by adding new products and services in multiples key areas:
Machine Learning:
We’re on a journey to create applications that can see, hear and understand the world around them. Today we’ve taken a major stride forward with the announcement of a new product family: Cloud Machine Learning. Cloud Machine Learning will take machine learning mainstream, giving data scientists and developers a way to build a new class of intelligent applications. It provides access to the same technologies that power Google Now, Google Photos and voice recognition in Google Search as easy to use REST APIs. It enables you to build powerful Machine Learning models on your data using the open-source TensorFlow machine learning library:
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Big Data and Analytics:
Doing big data the cloud way means being more productive when building applications, with faster and better insights, without having to worry about the underlying infrastructure. To further this mission, we recently announced the general availability of Cloud Dataproc, our managed Apache Hadoop and Apache Spark service, and we’re adding new services and capabilities today:
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Open Source:
Our Cloud Machine Learning offering leverages Google’s cutting edge machine learning and data processing technologies, some of which we’ve recently open sourced:
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What, if anything, do you see as a serious omission in this version of the Google-verse?
Suggestions?