Apr 24
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In this video, we walk through deploying a CPU-based serverless inference endpoint on RunPod using a custom Docker image. For previous videos, check this playlist - https://www.youtube.com/playlist?list=PLDk-bh96Xz-i93E42wi_V3XaF0Z-3PU6N Building on the previous parts of the series, we take our reusable text-to-speech (TTS) Docker setup and deploy it seamlessly on RunPod Serverless. You’ll learn how to use the Custom Deployment option, configure the endpoint, and get your model up and running in seconds. We also briefly cover different deployment options in RunPod, including Docker Registry and GitHub-based workflows, and when to use each. 🔧 What you’ll learn: How to deploy a custom Docker image on RunPod Serverless CPU-based inference setup for lightweight workloads Key configuration options (endpoint type, worker type, compute) When to use Docker Registry vs GitHub deployment By the end of this video, you’ll have a working serverless inference endpoint ready for integration. Chapters: 00:00 – Introduction & Recap 00:22 – RunPod Serverless Setup 00:43 – Deployment Options (Docker vs GitHub) 01:18 – Using Docker Registry Deployment 02:03 – Endpoint Configuration (CPU, Load Balancer, Compute) 04:12 – Deploying the Endpoint 04:27 – Monitoring Workers & Logs 👍 Like, Share & Subscribe for more AI tutorials. Join Whatsapp Community for more AI updates: https://chat.whatsapp.com/ES8FAmLiRhn7sn75mrCr83 Subscribe: https://www.youtube.com/@UCtqZnR47Bc9lwVpkvBKWmxg Follow: https://www.instagram.com/aipaatshal/ Medium: https://medium.com/@shrinath.suresh #AI #MachineLearning #TextToSpeech #TTS #FastAPI #Python #Serverless #RunPod #Docker #MLOps #AIEngineering #DeepLearning #APIDevelopment #BackendDevelopment #TechTutorial #LearnAI #GenerativeAI #AIProjects #PythonProjects