We love the tech that comes out of Google’s annual I/O conference. This year Google Assistant got an upgrade with Lens and came to iPhone, Google Home got phone capabilities and visual responses, and doubled down on machine learning with expanded features and new Tensor Processing Unit chips.
But the technology isn’t what is truly important to small businesses like us. What we care most deeply about is how that tech might help us grow our businesses, improve our margins, and deliver more and greater experiences to our clients. Below are three business boons for your organization from Google I/O 2017.
Google’s infrastructure as a service GCP product continues to grow. This year GCP is rolling out instances that will run Google’s new Tensor Processing Units, opening up efficient machine learning experimentation to a new bundle of users. Even those on a shoestring budget have the possibility of running their machine learning research on Google Cloud thanks to the new TensorFlow Research Cloud, a bundle of 1000 Cloud TPUs being made available to a broad range of research projects.
Google Cloud Platform also has continued to support machine learning options for non-experts. Google’s Machine Learning Engine, Translation API, Natural Language API, Cloud Vision API, and more grant businesses the opportunity to leverage machine learning capabilities without needing a score of data scientists on staff.
One of the most exciting things that continues to come out of Google is their continued improvement and support for Progressive Web Apps. In an age of walled gardens, open standards driven development means businesses like us can serve the majority of users without having to maintain multiple versions of the same codebase. Google’s release of Polymer 2.0 build on web standards to make progressive enhancement easier, while the PWA-evaluating Lighthouse offers a simple set of metrics to determine how services stack up against progressive enhancement best practices.
AI and machine learning continue to eat software: Google’s machine learning team has now developed neural networks that build neural networks better than the team. Tensorflow continues to grow and remains the go-to library for exploring machine learning, especially with the continued addition of high-level libraries such as Keras. Improvements to Google Assistant have opened verbal interface development to developers, significantly expanding the range of experiences we can provide users.
Google I/O 2017 presented a plethora of opportunities for businesses to improve products, expand services, and reduce overhead. Whether you’re exploring integrating machine learning to your business, investing in web technologies, or looking to improve your BLANK
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