Building and deploying ML models isn't hard anymore. What’s hard? Making them work reliably in production. This post dives deep into the most overlooked yet painful bottlenecks in modern MLOps:

1. Manual Feature Engineering and Dataset Versioning

You can automate model training, but if your data inputs keep changing without version control, you’re flying blind.

Fix: Use tools like DVC or Feast for data & feature versioning.

2. Inconsistent Retraining Pipelines

Models get stale. If retraining isn’t automated, it won’t happen.

Fix: CI/CD pipelines + retraining triggers = scalable ML.

3. Lack of Observability and Alerting

“The model isn’t working” – but why? Was it data drift? A failed ingestion job?

Fix: Layer in tools like Evidently, Arize, or Grafana with Prometheus.

4. Dev <> Ops Misalignment

Data scientists optimize for performance; ops teams optimize for uptime. Bridging the gap is key.

Fix: MLOps isn’t just tools—invest in cross-functional collaboration and shared metrics.

Abracadata 2025 will go deep on how to solve these—from Beam pipelines to Airflow 3, and everything in between.