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Machine Learning


If you’ve ever used only your voice to authenticate a payment, place an order, or check an account over the phone, there is a good chance that Pindrop’s technology made it possible. Founded in Atlanta in 2011, Pindrop provides software and technology that uses machine learning, voice recognition, and behavioral analytics to help detect and …

It’s been five years since we launched the Google Cloud Speech-to-Text (STT) API, and we’re awed by the things our 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 entirety of the Oxford English Dictionary more …

Editor’s note: Prevision.io has built the first ever pay-as-you-go AI management platform that simplifies the machine learning project lifecycle while offering powerful analytics capabilities. Now available exclusively on Google Cloud Marketplace, users can experiment, build, deploy, and manage AI projects in the cloud in weeks—without having extensive data science knowledge. Similar to the momentum that …

Without a central place to manage models, those responsible for operationalizing ML models have no way of knowing the overall status of trained models and data. This lack of manageability can impact the review and release process of models into production, which often requires offline reviews with many stakeholders. Earlier this week we announced Vertex …

Developers (especially ML engineers) looking to orchestrate BigQuery and BigQuery ML (BQML) operations as part of a Vertex AI Pipeline have previously needed to write their own custom components. Today we are excited to announce the release of new BigQuery and BQML components for Vertex AI Pipelines, that help make it easier to operationalize BigQuery …

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One does not simply productionalize machine learning models. If you’ve developed models before, you know that most of the time input features need to be preprocessed before they are ready to be consumed by a model. Often this preprocessing step is done by another application before sending the processed output to the prediction engine. This …

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 that embeddings sound slightly abstract and esoteric: In machine learning, …

Financial markets were among the first to adopt new technologies, and that has certainly been true of the derivatives markets, which were early adopters of electronic trading. Going forward, new capabilities will transform the way industry participants communicate, analyze, and trade. I sat down with Google Cloud’s Phil Moyer and former SEC Commissioner, Troy Paredes, …

Whether they meet customers online, offline, or in some combination, retailers share a big problem: How can they offer the right choices, when and how the customer wants, without overwhelming (and often losing) the buyer? More than anything, this is an information problem. As such, it’s a good candidate for using artificial intelligence (AI) for …

Bringing AI models to a production environment is one of the biggest challenges of AI practitioners. Much of the discussions in the AI/ML space revolve around model development. As shown in this diagram from the canonical Google paper “Hidden Technical Debt in Machine Learning Systems”, the bulk of activities, time and expense in building and …