Capstone Project40-45 hrs

PredictFlow Real-Time Feature Store

Bridge the gap between data engineering and ML. Deploy a real-time feature store serving predictions at sub-10ms latency.

4 Parts/12 Tools/25+ Interview Q's

predictflow / mlops-pipeline

EXPERIMENT

MLflow

DVC

Notebooks

Baselines

→FEATURE

Feast

Redis

Parquet

PIT Joins

→DEPLOY

BentoML

K8s

CI/CD

Canary

→MONITOR

Evidently

Prometheus

Grafana

Retrain

fig 1 — end-to-end mlops pipeline

THROUGHPUT

1K+

Predictions / sec

LATENCY

<50ms

P99 Serving

FEATURES

10+

Real-time Store

DRIFT

PSI

Auto-Detection

What You'll Build

A production-ready ML platform for PredictFlow, a SaaS company predicting customer churn.

Experiment Tracking

MLflow server with 10+ experiments, model registry, artifact storage, and reproducible pipelines

Feature Store

Feast with offline (Parquet) and online (Redis) stores, point-in-time correctness for 10+ features

K8s Model Serving

BentoML API on Kubernetes with HPA, canary rollouts, and CI/CD via GitHub Actions

ML Observability

Evidently AI drift detection, Prometheus metrics, Grafana dashboards, and auto-retraining triggers

Curriculum

Each part builds on the previous. Complete end-to-end in 4-6 weeks part-time.

Foundation — ML Experimentation & Tracking

10-12 hours

Set up MLflow tracking server, DVC data versioning, and build baseline churn prediction model with reproducible pipelines.

Trained model with experiment tracking

Feature Engineering — Feature Store Implementation

10-12 hours

Build Feast feature store with offline/online stores and implement point-in-time correctness for training-serving parity.

Feature store serving real-time features

Deployment — CI/CD & Model Serving

10-12 hours

Deploy models to Kubernetes with BentoML, implement CI/CD pipeline with GitHub Actions, and configure canary rollouts.

Model deployed to K8s with CI/CD

Monitoring — Production Observability & Retraining

10-12 hours

Implement Evidently AI drift detection, build Grafana dashboards, and create automated retraining pipelines.

Complete monitoring & auto-retraining

Technical Standards

Production patterns you'll implement across all four parts.

Performance

1K+req/sec

BentoML serving with horizontal pod autoscaling and <50ms P99 latency

Observability

3+dashboards

Prometheus metrics, Grafana dashboards, and Evidently drift reports

Automation

CI/CDpipeline

GitHub Actions with automated testing, model training, and canary deployment

Environment Setup

Spin up the full MLOps stack locally with Docker Compose.

predictflow-mlops

# Clone the project & launch MLOps stack
$ git clone https://github.com/aide-hub/predictflow-mlops.git
$ cd predictflow-mlops

# Start MLflow + Feast + Prometheus + Grafana
$ docker-compose -f docker-compose.mlops.yml up -d

# Initialize experiment tracking
$ mlflow server --host 0.0.0.0 --port 5000

Tech Stack

MLflow 2.15+DVC 3.51+Feast 0.38+BentoML 1.3+Kubernetes 1.28+Evidently 0.4+PrometheusGrafanaDockerGitHub ActionsPython 3.11+scikit-learn

Prerequisites

Python proficiency (pandas, scikit-learn basics)
Machine learning fundamentals (supervised learning, evaluation)
Docker basics (build and run containers)
Git & GitHub (branching, pull requests)

Kubernetes & cloud experience helpful but not required

Related Learning Paths

Master feature stores — Feast, offline/online stores, point-in-time correctness, and real-time serving.

Feature Stores Learning Path

Level up your ML operations skills covering experiment tracking, deployment, and monitoring.

MLOps Learning Path

New to feature stores? Read the complete guide covering offline/online stores, point-in-time correctness, and Feast.

What is a Feature Store? — Full Guide

Learn to build high-quality training datasets — deduplication, quality filtering, versioning, and data flywheel pipelines.

Dataset Engineering Learning Path

The complete guide to dataset engineering: MinHash dedup, quality filtering, data cards, and the data flywheel architecture.

What is Dataset Engineering? — Full Guide

Ready to build production ML systems?

Start with Part 1: Foundation — ML Experimentation & Tracking