From insights to robots, Speech AI use cases have exploded
It’s been five years since Google launched the Google Cloud Speech-to-Text (STT) API, and they’re awed by the things their customers have done. From powering voice-controlled apps to generating captions for videos, the API processes more than 1 billion minutes of spoken language each month—enough to transcribe the…
Meet AI’s multitool: Vector embeddings
Embeddings are one of the most versatile techniques in machine learning, and a critical tool every ML engineer should have in their toolbelt. It’s a shame, then, that so few of us understand what they are and what they’re good for! The problem, perhaps, is…
Google Cloud Text-to-Speech API now supports custom voices
With the rise of digital assistants and conversational interfaces, people have grown accustomed to hearing and speaking to synthetic voices. But what do those voices sound like? Often, pretty repetitive. Google are all familiar with the Google Assistant voice, for example. That’s why Google are…
USAA and Google Cloud: Modernizing Insurance Operations with Machine Learning
Delivering an automated solution capable of going from images of vehicle damage to actual car part repair/replace predictions has been an elusive dream for many auto insurance carriers. Such a solution could not only streamline the repair process, but also increase auditability of repairs and…
Developing High-quality ML Solutions
When a deployed ML model produces poor predictions, it can be due to a wide range of problems. It can be the result of bugs that are typical in any program—but it can also be the result of ML-specific problems. Perhaps data skews and anomalies…
Orchestrating PyTorch ML Workflows on Vertex AI Pipelines
Previously in the PyTorch on Google Cloud series, Google trained, tuned and deployed a PyTorch text classification model using Training and Prediction services on Vertex AI. In this post, we will show how to automate and monitor a PyTorch based ML workflow by orchestrating the pipeline in a serverless manner…
Add Intelligence to Your Document Processing with Google’s Enterprise Knowledge Graph
Google Cloud introduced Document AI to automate document processing and to streamline workflows with state-of-the-art machine learning models. With the deep neural networks, the models generalize the learning from seeing hundreds of thousands variations of the documents. But when information is missing or ambiguous on a document…
Unveiling a New Visual User Interface for Google Cloud’s Speech-to-Text API
Google Cloud were committed to making artificial intelligence (AI) accessible to everyone and easier to harness for new use cases. That’s why we’re excited to announce the general availability of our intuitive, new visual user interface for Google Cloud’s Speech-to-Text (STT) API, right in Google Cloud Console,…
Managing ML data sets with Vertex AI
Many enterprises want to use data to make meaningful predictions that can bolster their business or help them venture into new markets. This often requires using custom machine learning models—something not every business knows how to create or use. This is where Vertex AI can…
Use Deep Learning VM Images and Deep Learning Containers with Vertex AI
Deep Learning VM Overview Deep Learning VM Images is a set of virtual machine images optimized for data science and machine learning tasks. All images come with key ML frameworks and tools pre-installed. You can use them out of the box on instances with GPUs…