Google Cloud unveils Vertex AI, a machine learning platform
Google Cloud announced yesterday the general availability of Vertex AI, a managed machine learning (ML) platform that allows companies to accelerate the deployment and maintenance of artificial intelligence (AI) models.
The platform requires nearly 80% fewer lines of code to train a model versus competitive platforms, according to Google. This allows data scientists and ML engineers the ability to implement Machine Learning Operations (MLOps) to efficiently build and manage ML projects throughout the entire development.
Google explained that Vertex AI can be used to tackle challenges that data scientists are facing as the platform brings together the Google Cloud services for building ML under one unified UI and API, to simplify the process of building, training, and deploying machine learning models at scale.
“Enterprise data science practitioners hoping to put AI to work across the enterprise aren’t looking to wrangle tooling. Rather, they want tooling that can tame the ML lifecycle. Unfortunately, that is no small order,” said Bradley Shimmin, chief analyst for AI Platforms, Analytics and Data Management at Omdia. “It takes a supportive infrastructure capable of unifying the user experience, plying AI itself as a supportive guide, and putting data at the very heart of the process -- all while encouraging the flexible adoption of diverse technologies.”
With Vertex AI, data science and ML engineering teams can:
- Access the AI toolkit used internally to power Google that includes computer vision, language, conversation, and structured data, continuously enhanced by Google Research.
- Deploy more, useful AI applications, faster with new MLOps features like Vertex Vizier, which increases the rate of experimentation, the fully managed Vertex Feature Store to help practitioners serve, share, and reuse ML features, and Vertex Experiments to accelerate the deployment of models into production with faster model selection. If your data needs to stay on device or on-site, Vertex ML Edge Manager can deploy and monitor models on the edge with automated processes and flexible APIs.
- Manage models with confidence by removing the complexity of self-service model maintenance and repeatability with MLOps tools like Vertex Model Monitoring, Vertex ML Metadata and Vertex Pipelines to streamline the end-to-end ML workflow.
ModiFace using Vertex AI
ModiFace, a part of L’Oréal, is a leader in augmented reality and AI for the beauty industry. They create new services for consumers to try beauty products virtually and in real-time, such as different make-up and hair colours. ModiFace is using Vertex AI platform to train its AI models for all of its new services.
“With more and more of our users looking for information at home, on their phone, or at any other touchpoint, Vertex AI allowed us to create technology that is incredibly close to actually trying the product in real life,” said Jeff Houghton, chief operating officer at ModiFace.
Does the workplace have the right skills?
As the use of technology has developed fast, especially over the past year, there have often been problems trying to find the right skilled people to work in the correct job. Nearly two in five companies cite a lack of technical expertise as a major roadblock to using AI technologies.
Vertex AI is a single platform and according to Google it has every tool you need, allowing you to manage your data, prototype, experiment, deploy models, interpret models, and monitor them in production without requiring formal ML training. Therefore your data scientists don’t need to be ML engineers. The platform assists with responsible deployment and ensures you move faster from testing and model management to production and ultimately to driving business results.
Google is using AI to design faster and improved processors
Engineers at Google are now using artificial intelligence (AI) to design faster and more efficient processors, and then using its chip designs to develop the next generation of specialised computers that run the same type of AI algorithms.
Google designs its own computer chips rather than buying commercial products, this allows the company to optimise the chips to run its own software, but the process is time-consuming and expensive, usually taking two to three years to develop.
Floorplanning, a stage of chip design, involves taking the finalised circuit diagram of a new chip and arranging the components into an efficient layout for manufacturing. Although the functional design of the chip is complete at this point, the layout can have a huge impact on speed and power consumption.
Previously floorplanning has been a highly manual and time-consuming task, says Anna Goldie at Google. Teams would split larger chips into blocks and work on parts in parallel, fiddling around to find small refinements, she says.
Fast chip design
They have created a convolutional neural network system that performs the macro block placement by itself within hours to achieve an optimal layout; the standard cells are automatically placed in the gaps by other software. This ML system should be able to produce an ideal floorplan far faster than humans at the controls. The neural network gradually improves its placement skills as it gains experience, according to the AI scientists.
In their paper, the Googlers said their neural network is "capable of generalising across chips — meaning that it can learn from experience to become both better and faster at placing new chips — allowing chip designers to be assisted by artificial agents with more experience than any human could ever gain."
Generating a floorplan can take less than a second using a pre-trained neural net, and with up to a few hours of fine-tuning the network, the software can match or beat a human at floorplan design, according to the paper, depending on which metric you use.
"Our method was used to design the next generation of Google’s artificial-intelligence accelerators, and has the potential to save thousands of hours of human effort for each new generation," the Googlers wrote. "Finally, we believe that more powerful AI-designed hardware will fuel advances in AI, creating a symbiotic relationship between the two fields.