In a similar article on this blog, I talked about new data analytics releases that were announced at the Google Cloud Next 2019 conference in San Francisco. In this post, I will describe a few of the new AI & ML beasts that have been announced there. The focus this year has been on ML democratisation with the enhancements of BigQuery ML and AutoML, plus the arrival of a full end-to-end AI platform.
It’s definitely a very exciting period for AI and ML. Needless to say that Google makes it now accessible to everyone. Far away is the time when you needed advanced statistical and mathematical background to approach Artificial Intelligence or Machine Learning. Let’s take a closer look at the new AI releases.
AI is a tool. The choice about how it gets deployed is ours.” – Oren Etzioni
- AI platform: Streamline and simplify ML projects from ideation to production and deployment. End goal is to have one platform to make it all. AI platform is backed by Kubeflow, which uses Kubernetes in the backend. This enables each part of your ML pipeline to be portable and sit on a microservice of its own. The platform also leverages the best of Google technology with TensorFlow, TPUs and TFX tools as you deploy your AI applications to production.
- BigQuery ML: It was introduced last year at Google Next 2018 and since then it keeps on evolving at a very fast pace. BigQuery ML is now generally available and includes all the following new models: K-means clustering ML, Tensorflow DNN classifier and Tensorflow DNN regressor. This positions BigQuery ML as a serious player to develop ML models straight from your data warehouse. This definitely increases the power of BigQuery as the backbone of the analytical infrastructure. We can only be happy for this evolution, due to the ever-evolving efficiency of BigQuery.
- AutoML Tables: The AutoML suite also gets bigger with the arrival of AutoML Tables, that will enable you to build and deploy state-of-the-art ML models on structured data with only a few clicks. Same concept as for the other products of the AutoML suite: you just need to feed AutoML with the data and it will then do the rest. The technology will build you an optimal model by carrying out automatic feature engineering, grid-searching on various different models and a combination of hyper-parameters fine-tuning.
- Recommendations AI: Data scientists know all too well that building recommendation systems can be a cumbersome and time-consuming task, with results not always guaranteed. GCP is now tackling this with a new product that is based on years of experience at Google on recommending content across flagship properties such as Google Ads, Google Search and YouTube.
Recommendations AI uses Google’s latest Machine Learning architectures, which dynamically adapt to real-time customer behaviour and changes in variables like assortment, pricing, and special offers.” – Google Cloud Recommendations AI documentation
- Document understanding AI: Uses ML to understand and analyse any kind of documents. A lot of companies loose time in non-added value and annoying tasks that consist of taking data from a document and putting it in their IT system. Document Understanding AI comes at the rescue by capturing, classifying, enriching and visualising documents in both physical and digital formats. It will save your organisation time and money, but more importantly will make your process less error-prone. Use cases go from “document and content management“ to “robotic Process Automation” and “procure-to-pay automation”.
It has never been a more exciting time to work in the AI field, as all the technology around it gets easier to handle day by day. Thanks to the enhancements of its core AI building blocks and some few brand-new products, GCP is keeping its position as a leader in doing AI in the Cloud.
If you think of any AI applications, rest assured that Google Cloud Platform has the solution for you and that you can get something working within a few days. This is somehow the magic of the Cloud: you get things done quickly and in case you fail, you fail quickly and at low cost as you leverage the “pay as you go” pricing policy of every GCP product.
While the entrance barrier for ArtificiaI Intelligence & Machine Learning is getting lower and lower, we acknowledge that this barrier still exists. For this reason, it’s good to know that our GCP experts are always there for you to help in your different AI journeys on GCP.
For a complete list of new releases, I invite you to visit the full list of new announcements at Next ’19 on our blog. For data analytics announcements, see my previous blog post.
As a Google Cloud premier partner, we are here to help you get the most out of Google Cloud’s AI & ML technologies. Any questions? Just drop us a line, we’re happy to help!
