Common MLOps Mistakes and How to Avoid Them
After helping dozens of teams build production ML systems, we've seen the same mistakes repeated across organizations of every size. This guide covers the 15 most common MLOps failures and how to avoid each one.
Mistake 1: Training-Serving Skew
The problem: Features are computed differently during training (batch, in notebooks) and serving (real-time, in production). The model gets different inputs than what it learned from.
How it happens: A data scientist writes feature engineering in pandas, then an engineer reimplements it in SQL for the serving pipeline. Subtle differences (null handling, aggregation windows, timezone handling) cause silent accuracy drops.
The fix: Use a feature store that computes features once and serves them consistently for both training and inference.
Mistake 2: No Experiment Tracking
The problem: Training runs are tracked in spreadsheets, Slack messages, or not at all. No one knows which model version used which hyperparameters on which data.
How it happens: "We'll add tracking later." Later never comes, and reproducing past results becomes impossible.
The fix: Start with MLflow from day one. It's free, takes 10 minutes to set up, and every training run gets logged automatically with mlflow.autolog().
import mlflow
mlflow.autolog() # That's it. Every sklearn/pytorch/tensorflow run is now tracked.Mistake 3: Deploying Without Monitoring
The problem: A model is deployed to production and no one checks if it's still performing well. By the time someone notices the accuracy dropped, it's been serving bad predictions for weeks.
The fix: Implement production monitoring before deployment, not after. At minimum: track prediction distribution, data drift, and serving latency.
Mistake 4: Manual Model Deployment
The problem: Deploying a model involves SSH-ing into a server, copying files, and restarting services. This is error-prone, unreproducible, and doesn't scale.
The fix: Build ML CI/CD pipelines. Every model deployment should be triggered by a pipeline, tested automatically, and deployed progressively.
Mistake 5: Not Versioning Data
The problem: The training data "just lives somewhere" — an S3 bucket, a shared drive, a database table that gets updated in-place. You can't reproduce last month's model because the training data has been overwritten.
The fix: Version your data alongside code. DVC, lakeFS, or Delta Lake give you Git-like versioning for datasets.
Mistake 6: Over-Engineering Day One
The problem: The team spends 6 months building a "platform" with Kubeflow, Feast, KServe, custom monitoring, and a governance framework — before deploying a single model to production.
How it happens: Engineers read blog posts about Netflix's ML platform and try to build the same thing for a 3-person team.
The fix: Start minimal and add complexity only when needed:
Week 1: MLflow + simple training script + FastAPI serving
Month 1: Add DVC for data versioning + basic CI/CD
Month 3: Add monitoring (Evidently) + model registry
Month 6: Consider feature store / Kubeflow if complexity warrants it
Mistake 7: Ignoring Data Quality
The problem: Models are trained on dirty data — nulls, duplicates, schema changes, stale records. The model "works" in testing because test data has the same issues.
The fix: Validate data at every pipeline boundary:
# Simple but effective: validate before training
def validate_training_data(df):
assert df.isnull().mean().mean() < 0.05, "Too many nulls"
assert len(df) >= 1000, "Insufficient training samples"
assert df["target"].nunique() > 1, "Only one class in target"
assert df.duplicated().mean() < 0.01, "Too many duplicates"Mistake 8: No Quality Gate Before Deployment
The problem: A retrained model is automatically deployed without checking if it's actually better than the current production model. The new model might perform worse.
The fix: Always compare against the production model before promoting:
def quality_gate(new_model, production_model, test_data):
new_score = evaluate(new_model, test_data)
prod_score = evaluate(production_model, test_data)
if new_score < prod_score - 0.02: # Allow 2% tolerance
raise ValueError(f"New model ({new_score:.3f}) worse than production ({prod_score:.3f})")
return TrueMistake 9: One Giant Notebook
The problem: The entire ML pipeline lives in a single Jupyter notebook — data loading, cleaning, feature engineering, training, evaluation, and deployment logic all in one file.
The fix: Refactor into modular pipeline steps that can be run independently, cached, and tested. See our MLOps best practices guide for pipeline architecture patterns.
Mistake 10: Testing Only Accuracy
The problem: The model passes accuracy tests, gets deployed, and then fails in production because of latency, memory usage, edge cases, or bias.
The fix: Test at multiple levels:
- Data tests: Schema, distributions, quality
- Model tests: Accuracy, F1, bias, robustness
- Infrastructure tests: Latency, throughput, memory
- Integration tests: End-to-end prediction pipeline
Mistake 11: No Rollback Plan
The problem: A new model is deployed, starts misbehaving, and there's no fast way to revert to the previous version.
The fix: Always keep the previous production model warm and ready:
# KServe: canary with automatic rollback
spec:
predictor:
canaryTrafficPercent: 10 # Route 10% to new, 90% to current
# If metrics degrade, traffic automatically routes back to currentMistake 12: Treating ML Like Traditional Software
The problem: Applying software engineering practices directly to ML without adaptation. Code reviews catch code bugs but miss data bugs, model degradation, or pipeline issues.
Key differences:
| Software | ML | |----------|-----| | Bug = wrong code | Bug = wrong code OR wrong data OR wrong model | | Test = deterministic | Test = statistical (thresholds, not exact) | | Deploy = new code | Deploy = new code + new model + maybe new data | | Monitor = uptime | Monitor = uptime + accuracy + drift | | Rollback = previous code | Rollback = previous model + compatible features |
Mistake 13: GPU Waste
The problem: GPU instances run 24/7 for training jobs that only execute a few hours per day. Or A100s are used for inference that could run on T4s.
The fix: Right-size GPUs and schedule training during off-peak hours. See our GPU infrastructure optimization guide.
Mistake 14: No Documentation
The problem: The only person who understands the model leaves the company. No one knows what the model does, what data it needs, or how to retrain it.
The fix: Model cards for every production model. They don't need to be verbose — just answer: What does this model do? What data does it use? How do you retrain it? What are its limitations? See our model governance guide.
Mistake 15: Ignoring Regulatory Requirements
The problem: An ML model is deployed for high-risk decisions (credit, hiring, healthcare) without considering EU AI Act, GDPR, or industry-specific regulations.
The fix: Assess regulatory requirements during model design, not after deployment. MLOps practices (experiment tracking, audit trails, monitoring) directly support EU AI Act compliance.
The MLOps Maturity Checklist
Use this to assess where you stand:
| Level | Practice | Status | |-------|----------|--------| | 0 | Experiment tracking (any tool) | | | 0 | Code in version control (not just notebooks) | | | 1 | Data versioning | | | 1 | Automated training pipeline | | | 1 | Model registry with versions | | | 2 | Data validation in pipeline | | | 2 | CI/CD for model deployment | | | 2 | Production monitoring | | | 3 | Feature store | | | 3 | Automated retraining with quality gates | | | 3 | Model governance and documentation | |
Related Resources
- What is MLOps? — Foundational concepts
- MLOps best practices — The complete playbook
- MLOps tools comparison — Choose the right tools
- MLOps for small teams — Lean MLOps without complexity
Avoiding MLOps mistakes is easier with experienced guidance. DeviDevs helps teams build production ML platforms the right way. Get a free assessment →