What a Lead } should know

Reviews algorithmic decisions in the team's ML pipelines, evaluates computational complexity of feature engineering and data preprocessing stages. Defines acceptable latency thresholds for inference services and optimizes batch training pipelines considering O-complexity of operations on large datasets.

Establishes async programming standards for the team's MLOps services: asyncio for inference API, concurrent data loading in training pipelines, non-blocking metric writes to Prometheus. Reviews correctness of concurrent request handling in model serving and parallel experiment execution in Kubeflow.

Implements code quality standards for the MLOps team: Python linting via ruff/flake8, typing via mypy, formatting via black. Defines code review rules for ML pipelines focusing on experiment reproducibility, data versioning via DVC, and correctness of Kubeflow/Airflow DAG configurations.

Defines data structure selection standards for the team's ML infrastructure: columnar formats for feature store, efficient sparse matrix representations, optimal indexes for embedding search. Reviews feature caching and batch buffering implementations in serving services.

Implements MLOps system design patterns in the team: Strategy for training algorithm selection, Pipeline for processing stage composition, Observer for model drift monitoring. Standardizes approaches to building reusable ML pipeline components via Kubeflow Components and Airflow Operators.

Establishes team standards for ML component design: abstractions for DataLoader, Transformer, Model through inheritance and interfaces. Reviews Python module architecture for SOLID compliance — single responsibility for pipeline stages, dependency inversion for pluggable storage backends and experiment trackers.

Defines the event-driven architecture for the MLOps team's infrastructure: topics for model events (deployed, retrained, degraded), feature update streams for online serving. Standardizes Avro/Protobuf schemas for ML events and configures Kafka Connect for integration with feature store and experiment tracker.

Defines the serving layer architecture for the team's ML models: choosing between FastAPI, Triton Inference Server, and BentoML. Standardizes inference API building patterns — model versioning via URLs, A/B testing via headers, shadow mode for canary deployment of new versions.

Defines the caching strategy for the team's MLOps services: Redis as online feature store for real-time serving, caching embeddings and intermediate pipeline results. Standardizes cache invalidation approaches during model retraining and feature pipeline updates.

Defines the ML artifact storage strategy for the team: bucket structure for model registry, feature store snapshots, and experiment artifacts. Standardizes naming conventions, configures cross-region replication for DR, and optimizes storage costs through intelligent tiering for rarely used models.

Defines the async processing architecture for the MLOps team: Celery/RQ for lightweight tasks, Kubeflow Pipelines for heavy training workflows. Standardizes retry and backoff patterns for GPU training jobs, configures queue monitoring and alerting for stuck ML tasks via Flower and Prometheus.

Defines indexing standards for all MLOps infrastructure databases: index creation policies for metadata store, feature catalog, and prediction logs. Conducts regular index audits, removes unused indexes, and creates covering indexes for critical model monitoring queries.

Defines PostgreSQL usage standards in the MLOps team's infrastructure: schemas for storing experiment results, model metrics, and lineage data. Designs database architecture for MLflow backend store, configures replication for metadata service fault tolerance, and optimizes performance under high-frequency metric writes.

Defines gRPC usage standards in the team's ML infrastructure: unified proto definitions for inference APIs, standardized error handling and metadata. Implements gRPC interceptors for prediction logging, latency metric collection, and request tracing via OpenTelemetry across the ML service chain.

Defines ML API design standards for the team: unified contracts for inference endpoints, standardized request/response formats for different model types. Implements API-first approach to MLOps service development, defines versioning and deprecation policies for model serving APIs.

Defines the AWS strategy for the MLOps team: choosing between SageMaker and self-managed Kubeflow on EKS, security standards for ML data. Manages GPU resource budgets, implements tags for cost allocation across ML projects, and configures AWS Organizations for separating training and production accounts.

Defines container security policies for the MLOps team's infrastructure: scanning standards, acceptable CVE levels for production inference. Implements regular ML image audits, configures automatic base image patching, and controls compliance for containers with access to training data and GPU resources.

Defines containerization standards for the MLOps team: unified base images with pre-installed ML frameworks, CUDA driver and Python version update policies. Implements automated ML image building and testing in CI/CD, standardizes Docker best practices for reproducible training environments.

Defines infrastructure strategy with Helm. Establishes IaC standards. Conducts architecture reviews. Optimizes FinOps.

Defines infrastructure strategy with Kubernetes Advanced. Establishes IaC standards. Conducts architecture reviews. Optimizes FinOps.

Defines Kubernetes usage standards for the MLOps team: namespace strategy for separating training/staging/production, resource quotas for GPU workloads. Implements GitOps for ML infrastructure management, standardizes model deployment via Seldon Core or KServe, and configures K8s cluster monitoring with focus on ML metrics.

Defines Infrastructure as Code standards for the MLOps team: Terraform module library for typical ML components, review policies for infrastructure changes. Implements Terraform Cloud/Atlantis for collaborative IaC, configures Sentinel policies for GPU instance cost control and ML infrastructure compliance.

Defines ArgoCD-based GitOps strategy for the organization's ML platform and model delivery infrastructure. Establishes standards for model deployment pipelines, governance of ML resource provisioning, and integration with ML experiment tracking systems. Conducts architecture reviews of ML deployment patterns and drives adoption of reproducible model delivery practices.

Defines CI/CD standards for the MLOps team in GitHub Actions: mandatory pipeline stages (lint, test, model validation, security scan, deploy), templates for typical ML projects. Implements CI/CD metrics — ML image build time, model deployment frequency, rollback rate — and optimizes GPU runner costs.

Defines the DevOps strategy with GitLab CI/CD Advanced. Establishes CI/CD standards. Implements platform engineering approaches.

Defines integration testing standards for the MLOps team: mandatory smoke tests before model deployment, test environments with realistic data. Implements staging environment for ML pipelines with full service sets, configures automated integration test runs on every model or infrastructure update.

Defines load testing standards for the MLOps team: inference latency SLAs by model type, mandatory tests before production rollout. Implements regular ML service performance testing in CI/CD, configures automated alerts on degradation, and defines benchmarks for comparing model versions by performance.

Defines testing standards for the MLOps team: minimum coverage for feature engineering code, mandatory tests for model serving endpoints, test templates for Kubeflow components. Builds a testing culture for ML code, configures coverage gates in CI/CD, and defines approaches for testing non-deterministic ML components.

Defines experiment tracking standards for the MLOps team: mandatory metadata for each experiment, tag taxonomy and naming conventions. Implements experiment review processes before production deployment, configures automated result reports, and ensures linkage between experiments, Git PRs, and model deployments.

Defines the Feature Store strategy at team/product level. Establishes standards and best practices. Conducts reviews.

Defines the ML Pipelines strategy at team/product level. Establishes standards and best practices. Conducts reviews.

Defines MLflow usage standards for the MLOps team: mandatory metrics and tags for each experiment, naming conventions, project structure. Implements best practices for experiment organization — parent/child runs for hyperparameter tuning, nested runs for ensemble models, standardizes MLflow Projects for training reproducibility.

Defines model monitoring standards for the MLOps team: mandatory metrics for each model type, prediction quality SLAs, drift response procedures. Builds ML observability culture — stakeholder dashboards, automated model health reports, and runbooks for quality degradation incidents.

Defines the model serving strategy for the MLOps team: standard stack (KServe/Seldon Core on Kubernetes), deployment patterns (canary, shadow, blue-green). Implements unified model rollout process with mandatory quality checks, configures SLA monitoring for latency, and defines runbooks for inference service incidents.

Defines standards for AI assistant usage in the MLOps team: recommendations for effective ML code prompts, policies for reviewing generated code. Implements best practices — mandatory review of AI-generated infrastructure configurations, no direct commits without review, trains the team on effective Copilot usage for MLOps.

Defines custom metrics standards for the MLOps team: ML-specific metrics (prediction_confidence, feature_freshness, model_staleness), business metrics tied to models. Implements a unified metrics library for inference services, standardizes labels and naming conventions for Prometheus, and configures composite alerts for ML system degradation.

Defines distributed tracing standards for the MLOps team: mandatory spans for inference pipeline, standard attributes (model.version, prediction.confidence, feature.freshness). Implements OTel SDK as part of the inference framework, configures trace-based alerting on anomalies, and ensures end-to-end visibility for all ML requests in production.

Defines monitoring standards for the MLOps team: mandatory metrics for each inference service, standard dashboards, alerting policies. Implements SLI/SLO framework for ML services, configures on-call processes for model incidents, and ensures model metric visibility for data science and product teams.

Defines logging standards for the MLOps team: mandatory fields for inference logs (model_version, features_hash, prediction_confidence), retention policies. Implements a unified logging SDK for ML services, configures alerting on prediction log anomalies, and ensures compliance requirements for ML decision auditing.

Defines code review standards for the MLOps team: checklists for different PR types (training pipeline, inference service, infrastructure), review SLA. Builds a culture of constructive ML code review, configures CODEOWNERS for critical components, and trains the team on effective reviews focusing on ML-specific aspects.

Defines Git standards for the MLOps team: branching model for ML projects, commit conventions for training code and infrastructure. Implements Git best practices for ML — mandatory review for pipeline changes, main branch protection, automated checks in pre-commit hooks for ML code and Kubeflow/Airflow configurations.