A New Way to Explore What’s Possible with Google Maps Platform
For more than 15 years, developers have used Google Maps Platform to deliver location-based experiences to their end users and used location intelligence to optimize their businesses. Along this journey, Google Maps made a variety of changes to better support our community as needs have…
Meet the Next Generation of Mobile-Optimized Maps
A customized map can be key to delivering a frictionless experience that engages users and sets you apart in users’ minds–whether you’re a real estate company fine-tuning points of interest (POIs) on a map to help buyers decide where to live, or a regional pharmacy…
Best Practices for Mitigating Ransomware Attacks using Google Cloud
Code that was created by a third party to infiltrate your systems to hijack, encrypt, and steal data is referred to as ransomware. To protect your enterprise resources and data from ransomware attacks, you must put multi-layered controls in place across your on-premises and cloud…
Virtual Desktop untuk Windows Machine di Compute Engine
Sedikit mengenai Virtualisasi Jika kita tarik ke 25 tahun kebelakang, untuk mendeploy aplikasi sebuah perusahaan harus memiliki physical server di data center milik mereka.Di situasi ini, perusahaan harus menyiapkan tempat yang dialokasikan sebagai data center, menyewa jasa keamanan untuk memastikan tidak terjadi pencurian hardware server…
Service Meshes in a Microservices Architecture
What are the new problems in microservices architecture? Companies are increasingly adopting microservices, containers, and Kubernetes. The need to modernize, and the need to increase developer productivity, application agility, and scalability drives this increase. Many organizations are also venturing into cloud computing and adopting a…
What is Microservices Architecture?
Microservices architecture (often shortened to microservices) refers to an architectural style for developing applications. Microservices allow a large application to be separated into smaller independent parts, with each part having its own realm of responsibility. To serve a single user request, a microservices-based application can…
Architecture: Scalable commerce workloads using microservices
Retail commerce requirements and microservices Retail commerce workloads require a number of cloud-native features in order to meet demand from an ever-growing number of consumer devices and platforms: Typically, these deployments must be multi-region to serve a global customer base. They must support some degree…
Using GPUs for Training Models in the Cloud
Graphics Processing Units (GPUs) can significantly accelerate the training process for many deep learning models. Training models for tasks like image classification, video analysis, and natural language processing involves compute-intensive matrix multiplication and other operations that can take advantage of a GPU’s massively parallel architecture….
Tokopedia’s journey to creating a Customer Data Platform (CDP) on Google Cloud Platform
Founded in 2009, Tokopedia is an ecommerce platform that enables millions of Indonesian to transact online. As the company grows, there is an urgent need to better understand customer’s behavior in order to improve the customer’s experience across the platform. Now, Tokopedia has more than…
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…
Introduction to Vertex AI
Vertex AI is Google Cloud’s end-to-end ML platform for data scientists and ML engineers to accelerate ML experimentation and deployment. The platform unifies Google Cloud’s existing ML offerings into a single environment for efficiently building and managing the lifecycle of ML projects. Vertex AI brings…