Welcome to the October 1–15, 2025 edition of Google Cloud Platform Technology Nuggets. The nuggets are also available on YouTube.
AI and Machine Learning
The big announcement in this period was Gemini Enterprise, which aims to bring the power of AI to every employee through an intuitive chat interface that acts as a single front door for AI in the workplace. What is behind that chat interface? 6 key components: Gemini models, no-code workbench, taskforce of pre-built Google agents, connectivity to company data, central governance and solutions from the partner ecosystem. Check out the blog post to understand more about Gemini Enterprise, how customers are planning to use it and more.
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There has been several updates to the generative media models on the Vertex AI platform. These include:
- General Availability of Gemini 2.5 Flash Image for advanced image creation and editing
- Imagen 4, which is engineered for fast, high-resolution text-to-image generation
- New features for the video generation model Veo 3, such as vertical format output for social media
- General Availability of Gemini 2.5 Text-to-Speech (TTS), which supports multi-speaker dialogue and granular style control
Check out the blog post for more details.
LLM-Evalkit is a centralized hub for all prompt-related activities, from creation and testing to versioning and benchmarking. This unification simplifies the workflow, ensuring that all team members are working from the same playbook. With a shared interface, you can easily track the history and performance of different prompts over time, creating a reliable system of record. Check out the blog post for more details.
There is a clear demand from users to deploy leading AI models directly into their own Virtual Private Cloud (VPC). This is now possible in Google Cloud, which lets you securely deploy a growing selection of leading proprietary models from industry partners, including AI21 Labs, CAMB.AI, Contextual AI, CSM, Mistral AI, Qodo, Virtue AI, and WRITER. You can deploy these models (available in Vertex AI Model Garden) including closed-source models and those with restricted commercial licenses directly into your own Virtual Private Cloud (VPC). Check out the blog post for more details.
API Management
Google has been recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for API Management, positioned highest for its ability to Execute. Check out the blog post and download the full report.
Identity and Security
The first Cloud CISO Perspectives for October 2025 is out. This edition introduces a new AI-powered defence layer integrated into Google Drive for desktop, specifically designed to protect Google Workspace customers from ransomware attacks by identifying and stopping file corruption before it spreads. The bulletin also covers topics like guidance on AI supply chain security, economic threat modelling, and new security training courses.
Starting in November 2025, Google will move from the traditional “brute force disk erase” method to a modern, more efficient cryptographic erasure strategy for media sanitisation. This new approach leverages the secure deletion of cryptographic keys, making the underlying data unreadable and unrecoverable, which is faster and aligns with NIST industry standards. Check out the blog post for more details.
If you are into malware analysis, you should check out the new Mandiant Academy course titled, “Basic Static and Dynamic Analysis.” This course is designed to equip the learners with core skills for malware analysis, digital forensics, and threat hunting. The curriculum covers Basic Static Analysis (exploring the Portable Executable file format and extracting metadata) and Basic Dynamic Analysis (monitoring malware execution, network traffic, and system events). Check out the post.
Data Analytics
Google Cloud has released a new open-source Python library called Dataproc ML, designed to simplify AI/ML inference for data science teams using Apache Spark on Dataproc managed clusters. the library helps to connect Spark data pipelines directly to machine learning models, powered by Vertex AI. It helps developers apply Gemini generative AI models for tasks like classification or summarisation at scale, and also allows running batch inference with model files from Google Cloud Storage, supporting frameworks like PyTorch and TensorFlow. Check out the blog post for more details.
What are BigQuery data clean room query templates? They permit clean room owners to establish fixed, reusable queries that run against specific tables, allowing users to obtain insights without directly accessing the raw data. Check out the blog post that dives into explaining these templates and how they strengthen data leakage prevention, simplify the onboarding process for users who may lack technical expertise, and ensure analytical consistency across collaborations.
Databases
The release of Gemini CLI extensions for Google Data Cloud has made it easy to work with Google Databases inside of the Gemini CLI interactive terminal. The true power of these extensions can only be understood when you compare it to a real world task and how you would have done it without AI Assistant. The blog post highlights how a developer can implement a “fuzzy search” feature in PostgreSQL in minutes, as the Gemini CLI automatically identifies the necessary pg_trgm extension, checks for its installation, recommends performance optimisations like GIST or GIN indexes, and provides the required SQL code.
Compute
Who does not want to save costs around Google Cloud compute? Not one, but the article provides 11 methods to optimize infrastructure spending and reduce Google Cloud compute costs, ranging from basic adjustments to strategic decisions. A few of the methods focus on right-sizing and advanced configuration, such as choosing the right VM instances by adopting latest-generation VMs, leveraging Custom Machine Types (CMT), available on N4 VMs to precisely configure the CPU-to-memory ratio, making the most of Committed Use Discounts (CUDs) and more. Check out the blog post.
Startups
While there is a good amount of debate out there on the return on investment of AI in organizations, there are startups that are pushing the limits when it comes to its usage. Check out 150 use cases and get inspired.
And if you are a startup looking to implement production-ready AI agents, check out the Startup Technical Guide for AI Agents, which provides information on not just Agentc AI, but outlines three paths for adoption: building custom agents using tools like the open-source Agent Development Kit (ADK), using managed Google Cloud agents such as Gemini Code Assist, or integrating partner agents from the Cloud Marketplace. Check out the guide for more details.
DevOps and SRE
Chaos engineering is a crucial practice for building resilient cloud-based systems by intentionally introducing failures in a controlled environment. If you are looking to get started with Chaos Engineering on Google Cloud, this blog post is essential. It outlines the five core principles of chaos engineering, such as formulating a steady-state hypothesis and replicating real-world conditions, alongside the six practical steps for conducting an experiment. Finally, the post recommends using the open-source Chaos Toolkit to get started along with Google Cloud-specific recipes for implementing these techniques.
Google Skills
A new platform, Google Skills, that will bring together nearly 3,000 courses and labs in one place, including content from across Google Cloud, Google DeepMind, Grow with Google and Google for Education, is scheduled to launch early next year.
In addition to that, a new skilling intiative named GEAR (Gemini Enterprise Agent Ready) is coming too. Its goal is to empower developers and pros to build and deploy enterprise-grade agents using Google tech. Interested? While we are not sure what the details are, you can join the list here → https://goo.gle/4mTwlWz to be notified when it goes live.
Check out the blog post for more details.
Network Security Learning Path
If you are into cloud security professionals, Google Cloud is launching a new Network Security Learning Path that equips you with the validated skills needed to protect sensitive data and applications, ensure business continuity, and drive growth. Some of the key areas include: designing secure VPC and GKE topologies, controlling traffic with Google Cloud NGFW, establishing connectivity using Cloud VPN and Interconnect, defending with Cloud Armor, and applying granular IAM permissions across hybrid and multicloud architectures.
Enroll in the Google Cloud Network Security Learning Path today and check out the detailed blog post.
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Source Credit: https://medium.com/google-cloud/google-cloud-platform-technology-nuggets-october-1-15-2025-ad0d0da0ad68?source=rss—-e52cf94d98af—4
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