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.