Building Large Scale Recommenders using Cloud TPUs
Introduction Personalized recommender systems are used widely for offering the right products or content to the right users. Some examples of such systems are video recommendations (“What to Watch Next”) on YouTube, Google Play Store app recommendations and similar services offered by other app stores…
Utilizing Cloud Support API to Programmatically Update Support Cases
Have you ever thought of leveraging Google Cloud’s Support API to programmatically manage your support cases? Or what if you wanted to create some scripts that can be plugged into your existing support case management system? In this blog post, we will walk you through…
Controlling your BigQuery costs
Are you worried about controlling your BigQuery costs across multiple projects? In this blog post, you will learn about the different guardrails BigQuery provides to limit costs, and monitor BigQuery consumption. Also, learn how to design the warehouse that scales seamlessly while keeping costs under…
Building security guardrails for developers with Google Cloud
Organizations across the globe are using the cloud to drive innovation. Developers are using cloud technology as the engine to test new ideas, fail fast, and automate scalability. Innovation on the cloud requires freedom and flexibility to run experiments and make mistakes. For many organizations…
Securing apps for Googlers using Anthos Service Mesh
Hi there! In this blog post, Google would like discuss on how Corp Eng is adopting Anthos Service Mesh internally at Google. Corp Eng is Google’s take on “Enterprise IT”. A big part of the Corp Eng mission is running the first and third party software that…
Building a scalable MLOps system with Vertex AI AutoML and Pipeline
When you build a Machine Learning (ML) product, consider at least two MLOps scenarios. First, the model is replaceable, as breakthrough algorithms are introduced in academia or industry. Second, the model itself has to evolve with the data in the changing world. Google can handle both scenarios…
Automate annotations for Vertex AI text datasets with Cloud Vision API and BigQuery
One of the main challenges machine learning practitioners face is the availability of annotated training datasets or a lack thereof. In many cases, practitioners may have access to existing datasets that have been manually extracted, which they can use to accelerate their model training. Through…
I/O Adventure Google Cloud architecture
Since 2020, many conferences have moved online – either fully or partially – as organizers, presenters, and attendees all reimagine how we approach our life and our work. Google I/O Adventure is a virtual conference experience that brings some of the best parts of real-world…
Mercari leverages Google’s vector search technology to create a new marketplace
Mercari is one of the most successful marketplace services in recent years, with 5.3 million active users in the US and 20 million active users in Japan. In Oct 2021, the company launched a new service Mercari Shops in Japan that allows small business owners and individuals to…
Infrastructure Security in Google Cloud
The security of the infrastructure that runs your applications is one of the most important considerations in choosing a cloud vendor. Google Cloud’s approach to infrastructure security is unique. Google doesn’t rely on any single technology to secure its infrastructure. Rather, it has built security…
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Recent Posts
- Vertex AI grounding: More reliable models, fewer hallucinations
- New ways to protect your sensitive data with Chrome Enterprise
- The modern marketer’s strategic advantage: AI-powered data clean rooms
- Now Available: Google Vids is bringing AI-powered video creation to work
- Generative AI use cases to inspire your Startup or Small Business
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