AI Coding Assistants 1
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Uses GitHub Copilot to accelerate ML code writing: generating boilerplate for data preprocessing functions, suggestions for pandas transformations and sklearn pipelines. Formulates comment-prompts to get relevant suggestions for feature engineering and basic MLflow API operations.
Effectively uses Copilot for MLOps tasks: generating Dockerfiles for ML environments, Terraform modules for ML infrastructure, pytest tests for pipelines. Critically evaluates AI suggestions — checks Kubernetes GPU configuration correctness, validates generated SQL queries for feature extraction, and adapts code to project specifics.
Uses AI tools for boosting MLOps development productivity: Copilot for complex Kubeflow Pipeline configurations, Helm chart generation for inference services. Defines AI assistant effectiveness boundaries for ML-specific code, combines multiple AI tools, and develops prompt engineering for domain-specific tasks.
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.
Algorithms & Data Structures 2
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Understands basic algorithmic concepts for ML pipelines: simple data sampling methods, basic sorting and filtering for data preprocessing, configuration parameter search. Follows team guidance on algorithm selection for pipeline automation tasks.
Independently applies algorithmic thinking in MLOps: evaluates hyperparameter search strategies, understands model selection algorithm trade-offs, selects efficient data sampling methods for training. Analyzes computational cost implications of ML pipeline configuration choices.
Applies algorithmic thinking to MLOps: hyperparameter search algorithms (Bayesian optimization, grid/random search), model selection algorithms for A/B testing, efficient data sampling algorithms for training data management. Designs resource scheduling algorithms for GPU cluster utilization optimization.
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.
Understands basic data structures for ML pipelines: configuration dictionaries, feature arrays, model metadata objects. Applies simple data structures following team patterns for pipeline orchestration and artifact management scripts.
Independently selects appropriate data structures for MLOps: feature store schemas, model metadata formats, experiment parameter structures for tracking. Understands trade-offs between data serialization formats for model artifact storage and serving performance.
Selects optimal data structures for MLOps pipelines: feature store data formats for efficient serving, model registry metadata structures, experiment tracking data models for lineage. Optimizes data serialization formats for model artifact storage and distribution. Designs efficient data structures for A/B test result aggregation and model performance tracking.
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.
Background Jobs & Task Queues 1
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Uses Task Queues at a basic level in Kubeflow/MLflow. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.
Independently implements Task Queue tasks in Kubeflow/MLflow. Understands internals and optimizes performance. Writes tests using model monitoring.
Implements task queues for MLOps processes: Celery for async model training, batch prediction processing, and experiment report generation. Configures task prioritization, retry policies for long-running training jobs, and dead-letter queues for handling ML pipeline failures.
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.
CI/CD 1
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Understands GitLab CI/CD pipeline structure (stages, jobs, artifacts). Applies basic practices for triggering ML training pipelines via .gitlab-ci.yml. Follows team conventions for model artifact storage and pipeline variable usage.
Independently creates multi-stage GitLab pipelines for ML workflows (data validation, training, evaluation, registry push). Configures GPU runners, DVC integration, and model registry artifacts. Automates experiment tracking and model promotion with pipeline triggers and API calls.
Architects CI/CD with GitLab CI/CD Advanced. Optimizes pipeline speed and reliability. Implements progressive delivery.
Defines the DevOps strategy with GitLab CI/CD Advanced. Establishes CI/CD standards. Implements platform engineering approaches.
Clean Code & Refactoring 1
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Understands basic code quality principles for ML pipeline scripts. Follows team conventions for Python code formatting and configuration management. Writes simple, clean automation scripts for model training and deployment tasks. Accepts code review feedback on pipeline code.
Independently applies code quality practices in MLOps development. Writes clean pipeline orchestration code with proper configuration management and logging. Understands trade-offs between automation flexibility and pipeline determinism. Reviews MLOps code for experiment reproducibility, model versioning, and deployment rollback safety.
Designs code quality standards for MLOps infrastructure: training pipeline structure, model registry automation code, feature store integration patterns. Refactors ad-hoc ML deployment scripts into production-grade CI/CD for models. Implements quality gates for experiment reproducibility and model serving reliability.
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.
Cloud Providers 1
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Understands core AWS services for MLOps: S3 for storing models and datasets, EC2/GPU instances for training, ECR for ML images. Uses AWS Console for resource monitoring, configures AWS CLI for uploading model artifacts, and understands IAM role basics for ML data access.
Uses AWS services for building ML pipelines: SageMaker for model training and deployment, Step Functions for orchestration, Lambda for lightweight inference tasks. Configures EKS for Kubeflow, sets up CloudWatch metrics for inference service monitoring, and manages spot instances for cost-effective training jobs.
Architects ML infrastructure on AWS: SageMaker Pipelines architecture for end-to-end ML workflow, EKS configuration with GPU node groups for Kubeflow, S3 integration with Feature Store. Optimizes GPU instance costs via Savings Plans and spot fleet, implements multi-AZ architecture for fault-tolerant model serving.
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.
Concurrency & Parallelism 1
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Understands basic async programming in Python: asyncio for concurrent pipeline operations, async model deployment workflows, basic concurrent feature store access patterns. Follows team conventions for async code in ML infrastructure.
Independently applies async programming in MLOps: concurrent model deployment operations, async pipeline stage orchestration, non-blocking feature store queries. Understands trade-offs between sync and async patterns for ML infrastructure operations.
Designs async architectures for ML infrastructure: concurrent pipeline orchestration, async model deployment with health checking, non-blocking feature computation. Mentors team on async patterns for ML infrastructure reliability.
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.
Container Security 1
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Understands the fundamentals of Container Security Scanning. Applies basic practices in daily work. Follows recommendations from the team and documentation.
Integrates security scanning into CI/CD for ML images: configuring Trivy/Grype in the inference container build pipeline, filtering false positives for scientific Python packages. Configures vulnerability admission policies considering ML dependency specifics (numpy, scipy, CUDA) and blocks deployment of images with critical CVEs.
Architects container security strategy for the ML platform: automatic scanning on every model update, supply chain verification via SBOM for ML dependencies. Configures Kubernetes admission controller to block unsafe ML images and implements runtime security monitoring for inference containers with GPU access.
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.
Containerization 1
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Understands basic Docker concepts for MLOps: containerized model training environments, Docker images for ML pipeline stages, basic Dockerfile for Python ML dependencies. Follows team conventions for GPU-enabled container configurations.
Creates optimized Docker images for ML pipelines: multi-stage builds for size reduction, layer caching with pip dependencies, GPU-compatible base images on NVIDIA CUDA. Configures containerization for Kubeflow components, sets up volume mounts for datasets and model artifacts.
Architects containerization strategy for ML services: base image hierarchy for training and inference, GPU image size optimization via distroless, Docker BuildKit configuration for parallel builds. Implements model-in-container and model-on-mount patterns, configures health checks for inference containers with model load verification.
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.
Database Optimization 1
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Understands basic indexing principles in the MLOps context: why indexes are needed on tables with model and experiment metadata. Uses EXPLAIN to analyze simple queries against training data and creates B-tree indexes on key columns to speed up feature extraction.
Creates composite indexes for optimizing ML metadata queries: searching experiments by parameters, filtering models by metrics and versions. Analyzes query execution plans for feature store and optimizes indexing of historical prediction tables for fast data drift monitoring.
Architects indexing strategy for MLOps databases: GiST indexes for embedding search via pgvector, partial indexes for active experiments, BRIN indexes for partitioned prediction time-series tables. Optimizes query performance as ML metadata grows to millions of records.
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.
Git & Workflows 1
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Understands basic Git advanced features for MLOps: branching for pipeline version management, LFS for tracking model artifacts and datasets, stash for switching between infrastructure and pipeline tasks. Follows team conventions for versioning ML pipeline definitions and training configurations.
Applies advanced Git practices for MLOps: branching strategy for ML experiments, DVC integration for data and model versioning. Configures Git hooks for automatic ML config and Jupyter notebook validation (nbstripout), uses git-lfs for artifacts, and manages .dvc files for training pipeline reproducibility.
Designs Git workflows for MLOps projects: monorepo vs polyrepo for ML platforms, branching strategy for data scientist and ML engineer collaboration. Configures automation via Git — trigger training on merge to main, Helm chart and Terraform module versioning, git-ops integration for ML infrastructure management.
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.
gRPC 1
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Understands basics of gRPC & Protocol Buffers for MLOps platform service communication. Reads proto definitions for pipeline orchestration, model registry, and deployment service schemas. Follows team conventions for defining ML infrastructure service contracts.
Develops gRPC services for ML inference: defines proto schemas for prediction requests with typed tensors, implements streaming RPC for sequence processing. Configures gRPC clients with retry policies and deadlines for model serving calls, optimizes large tensor serialization via Protobuf.
Architects gRPC API for the MLOps platform: bidirectional streaming for real-time inference, efficient batch prediction serialization, integration with Triton Inference Server via custom proto extensions. Optimizes gRPC performance for ML serving — zero-copy tensor deserialization, connection pooling, and load balancing across GPU replicas.
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.
Kubernetes & Orchestration 2
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Understands basic Helm concepts. Uses ready-made configurations. Performs simple operations under senior guidance.
Independently configures and manages Helm. Writes IaC for typical tasks. Understands networking and security basics.
Architects infrastructure solutions with Helm. Optimizes cost and performance. Implements best practices and security hardening.
Defines infrastructure strategy with Helm. Establishes IaC standards. Conducts architecture reviews. Optimizes FinOps.
Understands basic Kubernetes Advanced concepts. Uses ready-made configurations. Performs simple operations under senior guidance.
Independently configures and manages Kubernetes Advanced. Writes IaC for typical tasks. Understands networking and security basics.
Architects infrastructure solutions with Kubernetes Advanced. Optimizes cost and performance. Implements best practices and security hardening.
Defines infrastructure strategy with Kubernetes Advanced. Establishes IaC standards. Conducts architecture reviews. Optimizes FinOps.
MLOps 3
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Uses MLflow Tracking for logging basic training metrics: accuracy, loss, F1-score. Can run mlflow ui to view experiments, log model parameters via mlflow.log_param, and save training artifacts. Understands MLflow structure — experiments, runs, artifacts — and navigates previous run results.
Configures MLflow Tracking Server for the team: remote backend store on PostgreSQL, artifact store in S3. Implements automatic logging via mlflow.autolog for PyTorch/TensorFlow/XGBoost, configures custom metrics and tags for experiment filtering. Integrates MLflow into training pipelines for result reproducibility.
Architects MLflow for production: highly available Tracking Server with load balancing, artifact storage optimization for large models. Implements custom MLflow plugins for integration with internal systems, configures MLflow Model Registry workflows with stage transitions and automated quality checks before production promotion.
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.
Understands basic ML model monitoring concepts: why tracking prediction quality in production matters, what data drift and concept drift are. Can view dashboards with model metrics, read quality degradation alerts, and perform simple input data distribution checks using pandas and matplotlib.
Configures ML model monitoring in production: collecting prediction logs, calculating quality metrics on new data, detecting data drift via statistical tests (KS-test, PSI). Integrates Evidently AI or Whylogs for automated distribution monitoring, configures Grafana dashboards with model metrics, and sets up accuracy/F1 degradation alerts.
Architects ML model monitoring system: real-time drift detection via streaming pipeline, automatic retraining trigger on metric degradation. Implements monitoring for complex scenarios — multi-model pipelines, concept drift with delayed ground truth, fairness and bias monitoring. Configures A/B testing with automatic decisions based on statistical significance.
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.
Understands basic model serving concepts: difference between batch and real-time inference, main model formats (ONNX, SavedModel, pickle). Can deploy a simple model via Flask/FastAPI endpoint, load a model from file, and return predictions. Knows about specialized serving systems — TFServing, Triton, Seldon.
Deploys models to production via specialized serving platforms: TensorFlow Serving for TF models, Triton Inference Server for multi-framework serving. Configures BentoML for packaging models with dependencies, implements batch inference via Spark/Ray, and configures model versioning for seamless production model updates.
Architects model serving for complex scenarios: multi-model serving with dynamic loading, ensemble inference via Triton, model A/B testing. Optimizes latency through model optimization (TensorRT, ONNX Runtime), implements GPU sharing for efficient resource utilization, and designs autoscaling based on inference metrics.
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.
OOP & Design Patterns 2
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Understands the fundamentals of Design Patterns at a basic level. Applies simple concepts in work tasks using Python. Follows recommendations from senior developers when solving problems.
Independently applies design patterns for MLOps: pipeline pattern for ML workflow orchestration, strategy for interchangeable model training backends, factory for pipeline step instantiation, observer for experiment tracking events. Explains pattern trade-offs for ML infrastructure extensibility.
Has deep expertise in design patterns for ML infrastructure: designs extensible pipeline architectures with plugin and strategy patterns, implements orchestration patterns for distributed training workflows, optimizes pattern usage for ML system scalability. Mentors team on architectural patterns for production MLOps platforms.
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.
Understands basic OOP concepts in Python: classes, inheritance, data classes. Applies simple SOLID principles when structuring ML infrastructure code. Follows team patterns for pipeline step classes and model registry client design.
Independently applies OOP/SOLID in MLOps tooling: proper abstraction for model registry clients, interface-based feature store connectors, single responsibility in training/serving/monitoring pipeline modules. Understands trade-offs between OOP patterns and declarative config approaches in ML infrastructure code.
Applies OOP/SOLID in MLOps architecture: abstract interfaces for model training/serving backends, strategy pattern for deployment strategies, template method for standardized ML pipeline stages. Designs extensible MLOps frameworks that support multiple ML frameworks and deployment targets.
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.
Performance Testing 1
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Understands load testing basics for ML infrastructure: testing model inference endpoints under concurrent request load, measuring GPU utilization during batch predictions, and basic throughput/latency benchmarking for model APIs. Follows team practices for ML service load test configuration.
Conducts load testing of inference services using Locust or k6: measuring prediction latency at different RPS, verifying stability under batch requests. Analyzes test results — p50/p95/p99 latency, throughput, error rate — and identifies performance degradation with increasing input data size.
Architects load testing strategy for ML services: realistic scenarios with request type distribution, GPU replica autoscaling testing. Implements load tests for Kubeflow Pipelines under parallel training, profiles inference services under load, and defines capacity planning based on GPU utilization metrics.
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.
Relational Databases 1
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Performs basic SQL queries against PostgreSQL to extract data needed by ML pipelines: selecting training datasets, checking prediction results. Understands table schemas with model and experiment metadata, writes simple JOIN queries for preparing features from relational sources.
Develops medium-complexity SQL queries for feature engineering from PostgreSQL: window functions for temporal features, aggregations for statistical features. Configures ML pipeline connections to PostgreSQL via SQLAlchemy, optimizes queries for training set extraction, and implements incremental loading for dataset updates.
Designs PostgreSQL schemas for ML platform metadata: tables for model registry, experiment tracking, and feature metadata. Optimizes heavy data extraction queries for model training, configures table partitioning for historical predictions, and implements materialized views for feature pipelines.
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.