MLOps Development Roadmap

Build expertise in managing and deploying machine learning workflows

Model Development

Foundation

Data Preparation

Data cleaning, transformation, feature engineering

PandasScikit-learnDask

Model Training

Training pipelines, hyperparameter tuning

TensorFlowPyTorchOptuna

Model Evaluation

Metrics, validation techniques, performance comparison

Scikit-learnSHAPLIME

Infrastructure Management

Essential

Containerization

Docker, orchestration, containerized workflows

DockerKubernetesPodman

Cloud Platforms

Deploying and managing ML workflows on cloud

AWS SageMakerGoogle Vertex AIAzure ML

Resource Scaling

Autoscaling, distributed training, infrastructure monitoring

RayHorovodCloud Monitoring

Continuous Integration & Deployment

Advanced

CI/CD Pipelines

Automating testing, deployment, and monitoring

GitHub ActionsJenkinsCircleCI

Model Versioning

Tracking model changes and reproducibility

MLflowDVCWeights & Biases

Deployment Strategies

Batch, online, and edge deployments

FastAPITensorFlow ServingTorchServe

Monitoring & Optimization

Advanced

Model Drift Detection

Monitoring changes in data and model performance

EvidentlyAlibi DetectWhyLogs

System Monitoring

Tracking infrastructure and application health

PrometheusGrafanaNew Relic

Cost Optimization

Efficient resource usage, cost analysis

KubecostAWS Cost ExplorerGCP Billing

Track your progress in MLOps skills and best practices

Master the art of deploying and maintaining ML systems