Horoskop forum

Hey everyone, I wanted to ask how you’ve handled building end-to-end MLOps pipelines in real-world production setups. I’ve been helping a small data science team move from manual model training to something more automated, but we keep hitting walls when it comes to integrating monitoring and CI/CD. I’ve read a bit about model drift detection and automated retraining, but it feels like overkill for now. Curious if anyone has practical tips or lessons learned, especially from working with consulting teams or specific tools that made a difference.

I’ve actually gone through this last year when we were struggling with similar issues — manual processes were breaking every time we retrained or updated data pipelines. We ended up bringing in mlops consulting services to help us set things up properly. They didn’t just throw in a bunch of tools; they helped us define what was actually worth automating. For example, instead of setting up full-scale model retraining, we started with lightweight data validation triggers and version control for model artifacts. Once that stabilized, we added CI/CD pipelines using Jenkins and MLflow for experiment tracking. What helped most was how they encouraged documentation and knowledge transfer so the system didn’t become a black box. If you’re just starting, focus on visibility and reproducibility first — automation can come later.

That’s a really good point about visibility. I’ve seen teams jump straight into automating everything, and then nobody knows what’s actually running. Taking it step by step like you described sounds more sustainable. We did something similar by setting clear versioning and rollback policies before adding retraining loops — made troubleshooting a lot easier later on.