No MLOps. Manual model deployments breaking.Automated MLOps pipeline today.

Yes. Automated model retraining is a Full Day to Sprint Pack engagement depending on your infrastructure complexity. The engineer builds: a data pipeline that collects, cleans, and versions your training data automatically when new labelled examples arrive, a training job that triggers automatically on a schedule or when a data threshold is reached (new data triggers retraining without manual intervention), a model evaluation step that runs your validation dataset against both the new model and the current production model and only promotes the new model if it beats the current one on your key metrics, a deployment step that updates your serving infrastructure with the new model weights using a blue-green or canary strategy, and alerting that notifies your team when retraining runs, its results, and whether the new model was promoted or rejected.

Yes. A full MLOps pipeline is a Sprint Pack engagement (2 weeks). The engineer delivers: data versioning using DVC or Weights and Biases Artifacts so every training dataset version is tracked and reproducible, experiment tracking using MLflow or Weights and Biases logging hyperparameters, metrics, and model artifacts for every training run, automated training jobs on your cloud infrastructure using AWS SageMaker, Google Vertex AI, or a self-managed Kubernetes job with GPU support, a model registry for staging and production model versions with rollback capability, a deployment pipeline pushing the approved model to your inference infrastructure (vLLM, TGI, or a custom FastAPI serving layer), and monitoring for production model drift and data drift alerting when model performance degrades.

Yes, significantly different in both capability and complexity. AWS SageMaker is a managed end-to-end ML platform that handles the infrastructure provisioning for training jobs (GPU instance allocation, job scheduling, distributed training coordination) and provides built-in experiment tracking, model registry, feature store, and one-click deployment via SageMaker endpoints. The trade-off is vendor lock-in and higher cost at scale. Self-managed MLflow running on your own infrastructure gives you more control over the tracking server, integrates with any cloud provider or on-premise GPU cluster, and is less expensive at volume but requires you to manage the MLflow server, its database backend, and artifact storage separately. The engineer implements whichever you use and can migrate from one to the other if your infrastructure requirements have changed since you originally set it up.

A production-ready MLOps pipeline has six layers: first, data versioning (every training dataset version is tracked, reproducible, and linked to the model it produced you can answer which data trained the model now in production); second, experiment tracking (every training run logs hyperparameters, metrics, and model artifacts so you can compare runs and identify what configuration produces the best results); third, automated evaluation (every new model is automatically tested against a held-out validation set and compared to the current production model before being eligible for promotion); fourth, model registry (staging and production model versions are explicitly tracked with promotion workflow and rollback capability); fifth, deployment automation (approved models are deployed to serving infrastructure without manual steps); sixth, production monitoring (model performance, data drift, and prediction distribution are monitored with alerts when degradation exceeds thresholds).

For a startup ML team with limited infrastructure budget and bandwidth, the minimum viable MLOps setup is three components. First, experiment tracking with MLflow or Weights and Biases every training run logs metrics so you can compare experiments and reproduce results; without this, you are flying blind on which model configurations work. Second, a model registry even a simple one using DVC or a shared S3 bucket with version naming conventions so you always know which model version is in production and can roll back if a new deployment degrades. Third, a deployment script that is reproducible and documented not a manual process that exists only in one engineer memory. This three-component foundation can be set up in a Full Day session and is the foundation everything else is built on as the team scales.

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