October 23rd (this past Sunday) was my 5th Googleversery and we just wrapped up an incredible Google Next 2022! It was great to see so many customers and my colleagues in person this year in New York City. This blog is an attempt to share progress we have made since last year (4th year anniversary …
Read more “An annual roundup of Google Data Analytics innovations”
AI is at a tipping point. We are seeing the impact of AI across more and more industries and use cases. Organizations with varying levels of ML expertise are solving business-critical problems with AI — from creating compelling customer experiences, to optimizing operations, to automating routine tasks, these organizations learn to innovate faster and ultimately, …
Read more “Accelerate your data to AI journey with new features in BigQuery ML”
If organizations can easily analyze unstructured data streams, like live video and images, they can more effectively leverage information from the physical world to create intelligent business applications. Retailers can improve shelf management by instantly spotting what products are out of stock, manufacturers can reduce product defects by detecting production errors in real time, and …
Read more “Vertex AI Vision: Easily build and deploy computer vision applications at scale”
Posted by Kedem Snir, Software Engineer, and Gal Elidan, Senior Staff Research Scientist, Google Research Whether it’s a professional honing their skills or a child learning to read, coaches and educators play a key role in assessing the learner’s answer to a question in a given context and guiding them towards a goal. These interactions …
Read more “Natural Language Assessment: A New Framework to Promote Education”
Today Amazon SageMaker announced the support of Grid search for automatic model tuning, providing users with an additional strategy to find the best hyperparameter configuration for your model. Amazon SageMaker automatic model tuning finds the best version of a model by running many training jobs on your dataset using a range of hyperparameters that you …
Read more “Amazon SageMaker Automatic Model Tuning now supports grid search”
Amazon SageMaker Serverless Inference is a purpose-built inference option that makes it easy for you to deploy and scale machine learning (ML) models. It provides a pay-per-use model, which is ideal for services where endpoint invocations are infrequent and unpredictable. Unlike a real-time hosting endpoint, which is backed by a long-running instance, compute resources for …
Read more “Introducing the Amazon SageMaker Serverless Inference Benchmarking Toolkit”
Monitoring machine learning (ML) predictions can help improve the quality of deployed models. Capturing the data from inferences made in production can enable you to monitor your deployed models and detect deviations in model quality. Early and proactive detection of these deviations enables you to take corrective actions, such as retraining models, auditing upstream systems, …
Read more “Deploy a machine learning inference data capture solution on AWS Lambda”
In just 5 years, tens of thousands of customers have tapped Amazon SageMaker to create millions of models, train models with billions of parameters, and generate hundreds of billions of monthly predictions. The seeds of a machine learning (ML) paradigm shift were there for decades, but with the ready availability of virtually infinite compute capacity, …
Read more “AWS Celebrates 5 Years of Innovation with Amazon SageMaker”
Data scientists choose models based on various tradeoffs when solving machine learning (ML) problems that involve tabular (i.e., structured) data, the most common data type within enterprises. Among such models, decision trees are popular because they are easy to interpret, fast to train, and can obtain high accuracy quickly from small-scale datasets. On the other …
Read more “Improved TabNet on Vertex AI: High-performance, scalable Tabular Deep Learning”