Google Cloud Machine Learning: now open to all with new professional services and education programs
Posted by Riku Inoue, Product Manager, Google Cloud Platform
Earlier this year at GCP NEXT, we introduced new Cloud Machine Learning products with the intention to change the way businesses operate and create new customer experiences, while deepening the insights derived from data.Today, we want to share how Google aims to help more businesses benefit from the advancements in machine learning, while making it easier for them to use it.
Google Cloud Machine Learning is now publicly available in beta and can empower all businesses to easily train quality machine learning models at a faster rate. With its powerful distributed training capability, you can train models on terabytes of data within hours, instead of waiting for days. Integrated with Google Cloud Platform (GCP), Cloud Machine Learning is a fully-managed service that can scale and creates a rich environment across TensorFlow and cloud computing tools such as Google Cloud Dataflow, BigQuery, Cloud Storage and Cloud Datalab.
We're also introducing a new feature, HyperTune, that automatically improves predictive accuracy. It allows data scientists to build better performing models faster by automatically tuning hyperparameters, instead of manually discovering values that work for their model.
Machine learning can unlock new possibilities for businesses — from improving customer service to more accurately streamlining operations to creating new applications and experiences.
One of our customers, Airbus Defense and Space, tested the use of Google Cloud Machine Learning to automate the process of detecting and correcting satellite images that contain imperfections such as the presence of cloud formations. Historically, this process was time consuming, prone-to-error and was unable to scale as satellite technology improved the quality and amount of images available.Satellite images are often used by industries such as agriculture and civil engineering. Common use-cases are precision farming for predicting crop yields and identifying the crops’ health, environmental groups for monitoring forestry, and city governments for land management. The ability to detect patterns in satellite images — such as the difference between snow and clouds —is critical to Airbus Defense and Space’s users who depend on highly precise, up-to-date and reliable information.
“In our tests, Google Cloud Machine Learning enabled us to improve the accuracy and speed at which we analyze the images captured from our satellites. It solved a problem that has existed for decades.” - Mathias Ortner, Data Analysis and Image Processing Lead
We're also excited to announce a dedicated machine learning practice within our Professional Services team. Our practice helps immerse businesses into the full capabilities of machine learning to determine how machine learning can help to solve individual needs.
Today, we're introducing:
- Machine Learning Advanced Solutions Lab that allows businesses to engage with Google Machine Learning engineers to help them solve their complex problems.
- The Cloud Start program that offers educational workshops for businesses to learn about the fundamentals of the public cloud and how to identify opportunities with advancements such as machine learning.
For more information on these services and the rest of the Professional Services offered, please contact your Google Cloud Sales Representative or visit our website. And last, but certainly not the least, we’re rolling out a certification program to help bring machine learning to more businesses than ever. Although real-world, hands-on experience is the best preparation for the certification, we recommend our data engineer training to help you strengthen your skill set.
The Google Data Engineer Certification and training are aimed at businesses, partners, and data scientists who want to design, train, and deploy accurate machine learning models to gain insights previously out of reach.
We recognize that machine learning has traditionally required specialized training and expertise. By opening these programs, more users can learn to create new machine learning applications.