Building your AI and ML stack

With the cloud fuelling the next round of digital transformation, leveraging Artificial Intelligence (AI) and Machine Learning (ML) should have become easier.

However, for most companies, machine learning and artificial intelligence are still rare areas of expertise, with a high level of talent, and therefore investment, required to build this capability. Currently, there are around one million data scientists worldwide, and at best, AI services can be used by these people.

Google’s AutoML-based stack on the cloud overcomes many constraints and improves access to AI and ML capabilities.

Google’s Cloud AutoML Suite

AutoML stands for automated machine learning, where a whole model can be created automatically, and a business can utilize its own data-set for their own custom ML deployment. Cloud AutoML, provides machine learning-based solutions for all organizations, regardless of whether they have mastery in the field or not.

Google’s Cloud AutoML suite aims to make the advantages of artificial intelligence widely accessible to all developers, even if they don’t have a machine learning background.

What’s more, Cloud AutoML released for Google’s Cloud Vision API will let businesses create models which rely on Vision-based machine learning.

Simply put, Google is moving towards automated models of machine learning, where a business can simply convey its whole data-set to the Cloud AutoML Vision API and create an AI framework for their necessities.

AI building blocks for text, speech, video, vision can be provided by Google’s machine learning APIs. However, these building blocks only offer generic solutions. Google is focusing on the center ground of developers, taking note of the fact that, “Many have needs beyond what’s accessible with pre-trained models, however, don’t have the right stuff or assets to build their own custom solutions.”

  • AutoML Natural Language enables you to foresee custom content classifications of particular domains
  • AutoML Translation supports the transfer of translated dialect sets to prepare your own custom translation model

Alongside AutoML Vision, the video,  text and images API for AI are currently accessible in open beta.

In the interim, after announcing the third-age of Cloud TPUs at I/O 2018, developers would be able to get the new liquid-cooled processors in alpha. These Tensor Processing Unit cases have eight times execution of the earlier age, with speeds achieving 100 petaflops.

Different updates to Google’s core machine learning APIs include:

  • Cloud Vision API now recognizes handwriting, supports additional file types (PDF and TIFF) and product search, and can identify where an object is located inside an image.
  • Cloud Text-to-Speech enhancements include incorporation of multilingual access to voices created by DeepMind WaveNet technology and the ability to optimize for the sort of speaker from which your speech is proposed to play.
  • Cloud Speech-to-Text can recognize what dialect is being used, unique speakers in a discussion, word-level certainty scores, and multi-channel recognition. So you can record every participant independently in multi-participant recordings.

With enterprise AI on the rise, speed and agility are crucial to staying competitive. But developing custom solutions can be time-consuming, complex, and costly.

CloudNow is a Premier Google Partner and can help build your custom AI and ML stack, delivered through our DevOps practice.

With CloudNow deploying Google Cloud AI solutions for your enterprise, you can quickly and easily apply these technologies across your work streams.

Whether you’re looking to classify images and videos automatically or deliver recommendations based on user data, CloudNow can help to deploy Google Cloud AI solutions to drive insights and improve customer experiences. CloudNow can also help discover relevant use cases to apply AI and ML and run workloads to quickly benefit from the application of AI and ML to your data.