AJBcoding Enterprise MLOps Platform - v4.0.0
Complete ML lifecycle orchestration with 2025's most stable stack
Primary Agent: backend-expert Stack: MLflow 3.6.0, DVC 3.x, Ray Serve 2.51.x, Kubeflow 1.10, Seldon Core 2.9.x, Feast 0.56.0, Optuna 4.6.0, Evidently AI, Prometheus 3.7.x, Grafana 11.3+
Level 1: Quick Reference
Core Technology Stack
Experiment Tracking & Model Registry:
- MLflow 3.6.0: Latest with AI Observability
- Model Registry for version management
- MLflow Gateway for model endpoints
Data Versioning & Pipelines:
- DVC 3.x: Cloud compute & versioning
- Pipeline stages (dvc.yaml)
- Remote storage integration (S3, GCS, Azure)
Model Serving:
- Ray Serve 2.51.1: Model multiplexing
- Seldon Core 2.9.1: KServe integration
- Triton Inference Server 2.45.x
ML Orchestration:
- Kubeflow Pipelines 1.10: Native Python SDK
- Argo Workflows 3.4.x: Backend orchestration
Feature Management:
- Feast 0.56.0: Online/offline stores
- Feature Registry & historical retrieval
Optimization & Monitoring:
- Optuna 4.6.0: Distributed hyperparameter tuning
- Evidently 0.2.2+: 100+ monitoring metrics
- Prometheus 3.7.3 + Grafana 11.3+: Observability stack
When to Use This Skill
- ✅ Setting up ML experiment tracking
- ✅ Implementing data versioning
- ✅ Deploying models at scale
- ✅ Building ML workflows
- ✅ Managing models on Kubernetes
- ✅ Creating feature stores
- ✅ Optimizing hyperparameters
- ✅ Monitoring model drift
- ✅ Automating ML CI/CD
- ✅ Building comprehensive observability
Level 2: Practical Implementation
MLflow 3.6.0 - Experiment Tracking
# Setup
pip install mlflow==3.6.0 python-dotenv
# Start server
mlflow server --backend-store-uri postgresql://user:pass@localhost/mlflow \
--default-artifact-root s3://bucket/mlflow-artifacts \
--host 0.0.0.0 --port 5000
Experiment Tracking
import mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
mlflow.set_experiment("breast-cancer-classification")
with mlflow.start_run(run_name="rf-baseline-v1"):
# Load and split data
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Log parameters
n_estimators = 100
mlflow.log_param("n_estimators", n_estimators)
mlflow.log_param("max_depth", 10)
# Train and evaluate
model = RandomForestClassifier(n_estimators=n_estimators, max_depth=10)
model.fit(X_train, y_train)
accuracy = model.score(X_test, y_test)
# Log metrics and model
mlflow.log_metric("accuracy", accuracy)
mlflow.sklearn.log_model(model, "model",
registered_model_name="breast-cancer-classifier")
DVC 3.x - Data Versioning
# Setup
pip install dvc==3.63.0 dvc-s3
dvc init
dvc remote add myremote s3://my-bucket/dvc-storage
dvc remote default myremote
Pipeline Configuration (dvc.yaml)
stages:
prepare:
cmd: python src/prepare.py --input data/raw --output data/prepared
deps: [data/raw, src/prepare.py]
outs: [data/prepared]
train:
cmd: python src/train.py --features data/features --model model.pkl
deps: [data/features, src/train.py]
outs: [model.pkl]
metrics: [metrics.json]
Execute Pipeline
dvc repro # Run pipeline
dvc dag # Show DAG
dvc metrics show # View results
Ray Serve 2.51.x - Model Serving
# Setup
pip install ray[serve]==2.51.1
from ray import serve
import torch
serve.start()
@serve.deployment(num_replicas=2, autoscaling_config=min_replicas=1)
class ModelDeployment:
def __init__(self, model_path: str):
self.model = torch.load(model_path)
self.model.eval()
async def __call__(self, request):
data = await request.json()
prediction = self.model.predict(data["features"])
return {"prediction": prediction.tolist()}
Kubeflow Pipelines 1.10 - ML Workflows
# Setup
pip install kfp==2.7.0 google-cloud-aiplatform
from kfp import dsl, compiler
from kfp.dsl import Dataset, Model, Artifact
@dsl.component(
base_image="python:3.11-slim",
packages_to_install=["pandas", "scikit-learn", "mlflow"]
)
def train_model(
input_data: Dataset,
model_output: Model,
metrics_output: Output[dict]
):
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import mlflow
# Load data
df = pd.read_csv(input_data.path)
X = df.drop('target', axis=1)
y = df['target']
# Train model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
# Save model and metrics
import joblib
joblib.dump(model, model_output.path)
metrics_output.update({
"accuracy": model.score(X_test, y_test),
"n_features": X.shape[1]
})
@dsl.pipeline(name="ml-training-pipeline")
def ml_pipeline():
data_task = load_data()
train_task = train_model(input_data=data_task.outputs["data"])
Feast 0.56.0 - Feature Store
# Setup
pip install feast==0.56.0
# feature_store.yaml
project: my_project
registry: data/registry.db
provider: local
offline_store:
type: file
online_store:
type: redis
connection_string: localhost:6379
from feast import FeatureStore, Entity, FeatureView
from feast.types import Float32, Int64
from feast.data_source import PushSource
# Define entities and features
customer = Entity(name="customer", join_keys=["customer_id"])
customer_features = FeatureView(
name="customer_features",
entities=["customer"],
schema=[
Field(name="customer_id", dtype=Int64),
Field(name="monthly_spend", dtype=Float32),
Field(name="transaction_count", dtype=Int64)
],
source=PushSource(name="customer_source")
)
# Initialize store
store = FeatureStore(repo_path=".")
# Get features for training
feature_service = store.get_feature_service("customer_features_v1")
training_data = store.get_historical_features(
entity_df=customer_df,
features=feature_service
).to_df()
Level 3: Advanced Integration
Evidently AI - Model Monitoring
# Setup
pip install evidently==0.2.2
from evidently.report import Report
from evidently.metrics import DatasetDriftMetric, ClassificationPerformanceMetric
# Data drift detection
drift_report = Report(metrics=[
DatasetDriftMetric(),
ClassificationPerformanceMetric()
])
drift_report.run(
reference_data=reference_df,
current_data=current_data,
column_mapping=column_mapping
)
# Save report
drift_report.save_html("drift_report.html")
Optuna 4.6.0 - Hyperparameter Optimization
# Setup
pip install optuna==4.6.0
import optuna
def objective(trial):
# Suggest hyperparameters
n_estimators = trial.suggest_int('n_estimators', 50, 300)
max_depth = trial.suggest_int('max_depth', 3, 10)
learning_rate = trial.suggest_float('learning_rate', 0.01, 0.3)
# Train model
model = XGBClassifier(
n_estimators=n_estimators,
max_depth=max_depth,
learning_rate=learning_rate
)
# Cross-validation
score = cross_val_score(model, X, y, cv=5).mean()
return score
# Optimize
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=100)
Prometheus + Grafana - Observability
# Model metrics export
from prometheus_client import Counter, Histogram, Gauge, start_http_server
# Define metrics
model_predictions = Counter('model_predictions_total', 'Total model predictions')
model_latency = Histogram('model_inference_seconds', 'Model inference latency')
model_accuracy = Gauge('model_accuracy', 'Current model accuracy')
# Use in model serving
@model_latency.time()
def predict(features):
model_predictions.inc()
return model.predict(features)
Installation Commands
# Core MLOps stack
pip install mlflow==3.6.0
pip install dvc==3.63.0 dvc-s3
pip install ray[serve]==2.51.1
pip install kfp==2.7.0
pip install feast==0.56.0
pip install optuna==4.6.0
pip install evidently==0.2.2
# Monitoring
pip install prometheus-client
pip install grafana-api
# Additional tools
pip install wandb # Weights & Biases
pip install tensorboard
pip install mlflow-skinet # MLflow UI enhancements
Quick Architecture Patterns
1. Experiment → Registry → Production
MLflow Tracking → MLflow Registry → Ray Serve → Monitoring
↓ ↓ ↓ ↓
Experiments Model Versions Deployments Metrics
2. Data → Features → Model → Serving
DVC Versioning → Feast Features → Training → Ray Serve
↓ ↓ ↓ ↓
Data Pipeline Feature Store Model API Endpoint
3. CI/CD Pipeline
GitHub Actions → DVC Pipeline → MLflow → Registry → Production
↓ ↓ ↓ ↓ ↓
Test & Build Data Version Experiments Models Deployment
Best Practices
- Version Everything: Data, models, code, experiments
- Automate Deployments: Use CI/CD for model promotion
- Monitor Continuously: Track drift, performance, latency
- Use Feature Stores: Prevent training/serving skew
- Document Experiments: MLflow tracking with proper tags
- Test Thoroughly: Model validation before production
- Scale Gracefully: Autoscaling and load balancing
Version: 4.0.0 Enterprise Last Updated: 2025-11-13 Status: Production Ready Enterprise Grade: ✅ Full Enterprise Support