Terraform Production Readiness Cheatsheet
Terraform working isn’t enough. Learn what it takes to make it production-ready — from backend design to security and automated pipelines.
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Director - Cloud Engineering, osttra
DevSecOps: Rapid & Secure Delivery
If security is your last step, you’re already too late. This guide shows how to build a DevSecOps pipeline where security is continuous, automated, and invisible to delivery speed.
Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.
The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.
Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.
