MLOps Engineer

Feature Stores

Pourquoi : You can run a shared feature store across teams and keep features consistent between training and serving, so reuse does not introduce hidden skew.

Pipeline Orchestration

Pourquoi : You can orchestrate multi-step ML workflows as DAGs with retries and schedules, so a failed step at 3 a.m. recovers or alerts instead of leaving a half-trained model.

Inference Servers

Pourquoi : You can design the inference tier for several models, including autoscaling and batch versus real-time paths, and diagnose latency spikes under load.

ML Deployment Strategies

Pourquoi : You can release a new model through canary or shadow traffic and roll it back on a metric, so a regression reaches a small slice of users, not all of them.

Model Monitoring

Pourquoi : You can define the monitoring standard each team's models must meet and decide which degradation justifies paging someone, so alerts stay actionable.

Data & Concept Drift

Pourquoi : You can choose drift tests and thresholds that separate real distribution shifts from noise, and link a detected shift to a retraining decision.

GPU & Compute Management

Pourquoi : You can plan GPU capacity and scheduling across teams and justify instance choices on cost, so shared accelerators are used well and budget overruns are caught early.

Containerized ML Workflows

Pourquoi : You can run training and serving on Kubernetes with reproducible images, so an environment difference never explains a result that cannot be reproduced.

Model Governance

Pourquoi : You can put approval gates and lifecycle rules around model releases, so no model reaches production without the sign-off the organization requires.

Compliance & Audit

Pourquoi : You can produce lineage and audit trails showing which data and code trained a deployed model, when an auditor asks you to explain a past prediction.

ML Observability

Pourquoi : You can build alerting pipelines on drift and decay metrics, so an on-call engineer sees which feature or segment moved instead of a generic accuracy drop.

Feature Store Architecture

Pourquoi : You can design the online and offline split, backfills and freshness of a feature store, so serving latency stays low when a new feature is added.