System Architecture Services

From .ipynbto Production

Stop letting models die in notebooks. I engineer resilient machine learning systems that scale, monitor themselves, and deliver value from day one.

View Strategy
description
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notebook_v3_FINAL.ipynb
# TODO: fix this later
df = pd.read_csv("data.csv")
model.fit(X, y) # magic numbers
print(accuracy) # 0.89??
warning4 code smells detected
Analyzing notebook...
Notebook
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The Engineering Pipeline

A structured approach to transforming research into reliable software.

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Framing & Strategy

We start by defining API contracts and inference strategies. Is it batch or real-time? What are the SLA requirements? We map the data flow before writing a single line of production code.

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Refactoring & Packaging

Modularizing spaghetti notebook logic into clean, tested Python packages. We implement dependency management (uv), unit tests (Pytest), and containerize the environment (Docker) to eliminate "it works on my machine".

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CI/CD & Deployment

Automated pipelines for training and deployment. We integrate with a model registry to ensure only validated models hit production, utilizing strategies like Canary or Blue/Green deployments for safety.

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Observability & Monitoring

Deployment isn't the end. We set up comprehensive monitoring for data drift, concept drift, and system latency. Alerts trigger automatically when model performance degrades.

Code Evolution

Transforming imperative, fragile scripts into declarative, robust systems.

BEFORE
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AFTER
notebook_v3_final_FINAL.ipynb
import pandas as pd
from sklearn.ensemble import RandomForestClassifier

# TODO: clean this data properly later
df = pd.read_csv("data_dump_2023.csv")
df = df.dropna() # dropping valuable data?

X = df.drop('target', axis=1)
y = df['target']

# Magic numbers everywhere
clf = RandomForestClassifier(n_estimators=100)

clf.fit(X, y)

print(clf.score(X, y)) # Evaluate on training data?!
src/pipelines/training.py
from src.config import ModelConfig
from src.models import ModelTrainer
import mlflow

def run_training_pipeline(cfg: ModelConfig) -> None:
    """Executes robust training flow with tracking."""
    with mlflow.start_run():
        loader = DataLoader(cfg.data_source)
        trainer = ModelTrainer(cfg.hyperparams)

        # Train & Validate
        metrics = trainer.train(loader.get_splits())

        # Log artifacts & registry
        mlflow.log_metrics(metrics)
        trainer.register_model(alias="prod-candidate")

The Tooling

Best-in-class technologies for robust MLOps.

codePython
deployed_codeDocker
anchorKubernetes
scienceMLflow
cloudAWS / GCP
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Ready to productionize?

Let's move your architecture from "research" to "revenue". Book a discovery call to discuss your specific infrastructure needs.

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