Vertex AI Archives - aster.cloud

8 min

Computing

How To Use Advance Feature Engineering To Preprocess Data In BigQuery ML

Preprocessing and transforming raw data into features is a critical but time consuming step in the ML process. This is especially true when a data scientist or data engineer has to move data across different platforms to do MLOps. In this blogpost, we describe how we streamline this process by adding two feature engineering capabilities in BigQuery ML. Our previous blog outlines the data to AI journey with BigQuery ML, highlighting two powerful features that simplify MLOps – data preprocessing functions for feature engineering and the ability to export BigQuery ML TRANSFORM statement as part of the model artifact. In…

10 min

Vertex AI Foundations For Secure And Compliant ML/AI Deployment

An increasing number of Enterprise customers are adopting ML/AI as their core transformational pillars, in order to differentiate, increase revenue, reduce costs and maximize efficiency. For many customers ML/AI adoption can be a challenging endeavor not only because of the broad spectrum of applications ML/AI can support, deciding on which one to prioritize can be a challenge, but because moving these solutions into production require a series of security, access and data assessments and features that some ML/AI platforms might not have. This blog post focuses on how to set up your Cloud foundations to cater specifically to the Vertex…

5 min

SAVI Transforms Global Surgical Instrument Tracking With Google Cloud

Powered by Vertex AI (Google Cloud’s platform for accelerating development and deployment of machine learning models into production), SAVI (Semi Automated Vision Inspection)1 is transforming surgical instrument identification and cataloging, leading to fewer canceled surgeries and easing pressure on surgery waitlists. Max Kelsen, an analytics and software agency that specializes in machine learning, has worked closely with Google Cloud and Johnson & Johnson MedTech to create a system that can manage tens of thousands of individual devices, their characteristics, and how they apply to each set or tray used by a surgeon. SAVI does this while delivering a one in…

8 min

How To Optimize Training Performance With The TensorFlow Profiler On Vertex AI

Training ML models can be computationally expensive. If you’re training models on large datasets, you might be used to model training taking hours, or days, or even weeks. But it’s not just a large volume of data that can increase training time. Nonoptimal implementations such as an inefficient input pipeline or low GPU usage can dramatically increase your training time. Making sure your programs are running efficiently and without bottlenecks is key to faster training. And faster training makes for faster iteration to reach your modeling goals. That’s why we’re excited to introduce the TensorFlow Profiler on Vertex AI, and…

6 min

Improving Model Quality At Scale With Vertex AI Model Evaluation

Typically, data scientists retrain models at regular intervals to keep them fresh and relevant. This practice may turn out to be costly if the model is trained too often or inefficient if the model training isn’t frequent enough to serve the business. Ideally, data scientists prefer to continuously evaluate the models and intentionally retrain models when the model performance starts to degrade. At scale, continuous model evaluation would require a standard and efficient evaluation process and system.In fact, after training a model, data scientists and ML engineers use an offline dataset of historical examples from the production environment to evaluate…

10 min

Engineering

Movie Score Prediction With BigQuery, Vertex AI And MongoDB Atlas

Hey there! It’s been a minute since we last wrote about Google Cloud and MongoDB Atlas together. We had an idea for this new genre of experiment that involves BigQuery, BQML, Vertex AI, Cloud Functions, MongoDB Atlas, and Cloud Run and we thought of putting it together in this blog. You will get to learn how we brought these services together in delivering a full stack application and other independent functions and services the application uses. Have you read our last blog about Serverless MEAN stack applications with Cloud Run and MongoDB Atlas? If not, this would be a good…

5 min

Using AI To Increase Asset Utilization And Production Uptime For Manufacturers

Today, manufacturers are advancing on their factory digitalization journey, betting on innovative technologies to strengthen competitiveness, deliver sustainable growth, and offer new services. Macroeconomic factors – such as high energy costs, increasing labor, and raw material shortages – drive the need for urgent operational optimizations and automation.Cloud capabilities have matured at an accelerated pace, giving manufacturers practical avenues to achieve these goals. Manufacturers are finding new ways to bring AI and machine learning (ML) to practical use cases, like predictive maintenance, anomaly detection, and asset utilization management. However, manufacturers struggle to adopt AI at scale due to challenges around data…

5 min

Tech

Best Practices For Managing Vertex Pipelines Code

Organizations are increasingly using machine learning pipelines to streamline and scale their ML workflows. However, managing these pipelines can be challenging when an organization has multiple ML projects and pipelines at different stages of development. To solve this, we need a way to build upon DevOps concepts and apply them to this ML-specific problem. In this post, we’ll share some best practices on how to manage the codebase for your ML pipelines. The guidance we’re sharing is based on our work with top Google Cloud customers and partners. We’ll provide a few best practices based on pipeline implementation patterns we’ve…

7 min

An Annual Roundup Of Google Data Analytics Innovations

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 blog post 2021 Next). Bringing BigQuery to the heart of your Data Cloud Since last year we have made significant progress across the whole portfolio. I want to start with BigQuery, which is at the heart of our customers’ Data Cloud. We have enhanced BigQuery…

3 min

Vertex AI Vision: Easily Build And Deploy Computer Vision Applications At Scale

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 in our communities, administrators could improve traffic management by analyzing vehicle patterns. The possibilities to create new experiences, efficiencies, and insights are endless.However, enterprises struggle to ingest, process, and analyze real-time video feeds at scale due to high infrastructure costs, development effort, longer lead times,…

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