--- title: A guide to putting together a continuous ML stack description: Let's take a dive into MLOps CI/CD/CT. How to put together a continuous ML pipeline to train, deploy, monitor, and retrain your models + automations and production-first insights to detect and resolve issues faster. image: https://go.superwise.ai/hubfs/A-guide-to-putting-together-a-continuous-ML-stack_webinar_19.04.2022.png --- [![Colors - Logo_Superwise - Standard](https://go.superwise.ai/hubfs/Colors%20-%20Logo_Superwise%20-%20Standard.svg "Colors - Logo_Superwise - Standard")](http://superwise.ai) [![White - Logo_Superwise - Standard](https://go.superwise.ai/hs-fs/hubfs/White%20-%20Logo_Superwise%20-%20Standard.png?width=809&height=174&name=White%20-%20Logo_Superwise%20-%20Standard.png "White - Logo_Superwise - Standard")](http://superwise.ai) ![menu-1](https://f.hubspotusercontent20.net/hubfs/6667626/menu-1.svg) ## A guide to putting together a continuous ML stack ![Itay Ben Haim](https://go.superwise.ai/hs-fs/hubfs/Itay%20Ben%20Haim.png?width=217&height=264&name=Itay%20Ben%20Haim.png) ### Itay Ben Haim ML engineer @Superwise April 19th 1:00 PM ET ## Workshop In this workshop, we'll take a dive into MLOps CI/CD + CT pipeline automation. In part 1, we’ll focus on how to put together a continuous ML pipeline to train, deploy, monitor, and retrain your models. Part 2 will focus on automations and production-first insights to detect and resolve issues faster. Repos will be provided after the event. What will you learn: - How to build a continuous MLOps stack - Platform and tool alternatives for each step - Considerations for scaling up - Production-first insights and automations [![White - Logo_Superwise - Standard](https://go.superwise.ai/hs-fs/hubfs/White%20-%20Logo_Superwise%20-%20Standard.png?width=809&height=174&name=White%20-%20Logo_Superwise%20-%20Standard.png "White - Logo_Superwise - Standard")](http://superwise.ai) All rights reserved to Superwise 2026 - - -