MLOps

MLOps Services

Machine learning in production requires more than just model training. MLOps brings DevOps practices to ML systems, ensuring reliable, scalable, and maintainable AI deployments.

ML Systems Are Different

Unlike traditional software, ML systems can fail silently—degrading performance without throwing errors. MLOps provides the practices and tooling to detect, prevent, and recover from these failures.

MLOps Lifecycle


flowchart LR
    subgraph Dev["Development"]
        A[Data Prep] --> B[Training]
        B --> C[Evaluation]
        C --> D[Registry]
    end

    subgraph Deploy["Deployment"]
        D --> E[Staging]
        E --> F[A/B Test]
        F --> G[Production]
    end

    subgraph Ops["Operations"]
        G --> H[Monitor]
        H --> I{Drift?}
        I -->|Yes| A
        I -->|No| H
    end

    style Dev fill:#e0f2fe,stroke:#0284c7
    style Deploy fill:#fef3c7,stroke:#d97706
    style Ops fill:#dcfce7,stroke:#16a34a

Development Phase

Stage	Activities	Tooling
Data Preparation	Collection, cleaning, validation	DVC, Great Expectations
Feature Engineering	Transformation, storage, serving	Feast, Tecton
Model Training	Experimentation, optimization	MLflow, W&B
Evaluation	Metrics, validation, comparison	Custom dashboards

Experiment Tracking

Version control for data, code, and models
Hyperparameter logging and optimization
Metric comparison across runs
Reproducible training pipelines

Feature Engineering

Feature store implementation
Online and offline feature serving
Feature versioning and lineage
Real-time feature computation

Deployment Phase

Deployment Strategies

Strategy	Risk Level	Rollback Time	Best For
Blue-Green	Low	Instant	Critical services
Canary	Low	Fast	Gradual rollout
A/B Testing	Medium	Moderate	Performance comparison
Shadow Mode	Very Low	N/A	Pre-production validation

Model Packaging

Containerization with Docker
ONNX for framework-agnostic deployment
Model optimization and quantization
API standardization (REST/gRPC)

Infrastructure

Training Infrastructure

Cost Optimization

Our Kubernetes-based training clusters with spot instance support typically reduce training costs by 60-80% compared to on-demand pricing.

Component	Purpose	Options
Compute	Model training	Kubernetes, Ray, Spark
Storage	Data and artifacts	S3, GCS, HDFS
GPUs	Accelerated training	NVIDIA A100, H100
Scheduling	Resource management	K8s, Slurm

Serving Infrastructure

Server	Best For	Features
TensorFlow Serving	TF models	Batching, versioning
TorchServe	PyTorch models	Multi-model, metrics
Triton	Mixed frameworks	GPU optimization
BentoML	Rapid deployment	Easy packaging
KServe	Kubernetes native	Serverless, autoscaling

Monitoring & Observability

Key Metrics

Category	Metrics	Alert Triggers
Model Performance	Accuracy, F1, AUC	Drop below threshold
Latency	p50, p95, p99	Exceeds SLA
Throughput	Requests/second	Capacity limits
Data Quality	Schema drift, nulls	Validation failures
Model Drift	Distribution shift	Statistical tests

Drift Detection

Data Drift: Input distribution changes
Concept Drift: Target relationship changes
Prediction Drift: Output distribution changes
Feature Drift: Individual feature shifts

Automation & CI/CD

Pipeline Stages

Code Commit: Trigger automated tests
Data Validation: Schema and quality checks
Model Training: Automated with tracked experiments
Model Validation: Performance gate checks
Staging Deployment: Integration testing
Production Deployment: Gradual rollout
Monitoring: Continuous observability

Orchestration Tools

Tool	Best For	Key Features
Airflow	Complex DAGs	Battle-tested, extensible
Prefect	Modern workflows	Python-native, dynamic
Dagster	Data-aware	Asset-based, testing
Kubeflow	Kubernetes ML	End-to-end platform

Tools & Platforms

Category	Tools
Experiment Tracking	MLflow, Weights & Biases, Neptune
Feature Stores	Feast, Tecton, Hopsworks
Data Versioning	DVC, LakeFS, Delta Lake
Model Serving	Seldon, KServe, BentoML
Monitoring	Prometheus, Grafana, Evidently
Orchestration	Airflow, Prefect, Dagster

Implementation Approach

Assessment: Evaluate current ML maturity and pain points
Architecture: Design MLOps platform for your scale
Infrastructure: Set up training and serving environments
Pipelines: Build automated training and deployment workflows
Monitoring: Implement observability and alerting
Documentation: Create runbooks and best practices
Training: Enable your team with MLOps practices

Need reliable ML in production? Talk to us about MLOps implementation.