Algorithms & Data Structures 2
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Understands basic algorithm complexity O(n), O(n log n), O(n²). Knows main sorting and searching algorithms. Applies knowledge when choosing data structures for feature engineering.
Establishes algorithmic efficiency standards for ML systems in the team. Makes trade-off decisions between accuracy and speed at the architecture level. Evaluates ML pipeline scalability.
Defines algorithmic strategy for ML platform. Researches novel algorithms for specific ML tasks. Publishes optimization results at conferences.
Knows basic data structures: arrays, dictionaries, sets. Uses pandas DataFrame and numpy arrays. Understands the difference between list and numpy array for ML tasks.
Defines data structure standards for ML team. Designs data abstractions for reuse across projects. Evaluates memory-efficiency of solutions.
Designs data abstractions at ML platform level. Researches novel data structures for ML tasks (LSH, HNSW indexes). Establishes standards for the entire organization.
Batch Processing 3
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Understands Apache Spark fundamentals for ML engineering: Spark MLlib pipelines, feature transformers, and distributed model training/inference. Follows team patterns for PySpark ML workflows, model serialization, and integration with MLflow tracking.
Uses PySpark for large-scale feature engineering. Optimizes Spark jobs (partitioning, caching, broadcast joins). Uses Spark ML for distributed model training.
Designs Spark-based ML pipelines for production. Optimizes Spark for ML workloads: memory tuning, shuffle optimization. Integrates Spark with ML platform (MLflow, feature store).
Defines Spark strategy for ML data processing. Evaluates Spark vs alternatives (Dask, Ray) for ML workloads. Designs distributed computing architecture for ML.
Effectively uses pandas for ML: data loading, EDA, feature engineering. Knows main operations: groupby, merge, pivot. Understands dtypes for memory optimization.
Optimizes pandas code for ML: vectorized operations, category dtype, chunked reading. Uses Polars for faster processing. Writes efficient feature engineering pipelines.
Designs data processing pipelines for ML. Chooses pandas vs Polars vs Spark for different scales. Optimizes memory usage for large datasets. Writes reusable feature transformers.
Defines data processing standards for ML team. Creates feature engineering framework. Trains the team on efficient data handling.
Writes SQL for extracting training data. Understands ETL for ML: extract features, transform, load into training format. Uses pandas.read_sql for data loading.
Designs SQL ETL for feature computation. Uses dbt for ML feature transformation. Writes incremental ETL for updating training data. Automates through Airflow.
Designs ETL architecture for ML data pipeline. Optimizes ETL for large data volumes. Configures data quality checks in ETL. Integrates ETL with feature store.
Defines ETL strategy for ML data. Coordinates with Data Engineering on ML data requirements. Designs data contracts for ML features.
Classical Machine Learning 2
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Trains baseline models with scikit-learn: Linear Regression, Logistic Regression, Random Forest. Performs cross-validation and train/test split. Uses Pipeline for preprocessing + model.
Designs sklearn Pipelines for production. Performs feature selection (SelectKBest, RFE). Configures hyperparameter tuning (GridSearchCV, RandomizedSearchCV, Optuna). Handles imbalanced data (SMOTE, class_weight).
Designs ML systems on scikit-learn for production. Creates custom transformers and estimators. Optimizes pipeline performance. Integrates sklearn with MLflow for tracking and serving.
Defines scikit-learn usage standards in the organization. Evaluates sklearn vs deep learning for different tasks. Creates feature engineering framework based on sklearn.
Trains XGBoost/LightGBM/CatBoost models with default parameters. Understands gradient boosting concept. Uses feature importance for model analysis.
Performs hyperparameter tuning for gradient boosting (learning_rate, max_depth, n_estimators, regularization). Handles categorical features (CatBoost native, target encoding). Configures early stopping and cross-validation. Analyzes SHAP values.
Designs production gradient boosting systems. Optimizes inference speed (model pruning, quantization). Builds ensembles from multiple gradient boosting models. Integrates with feature store and model serving.
Defines gradient boosting usage strategy in ML organization. Evaluates gradient boosting vs deep learning for tabular data. Creates AutoML pipeline.
Clean Code & Refactoring 1
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Writes readable Python code for ML. Follows PEP 8 and uses black/ruff for formatting. Writes docstrings for functions. Understands the importance of reproducibility in ML.
Shapes code quality culture in ML team. Introduces code review practices for ML. Standardizes approaches to ML component testing.
Defines code quality strategy for ML organization. Creates ML-specific coding standards. Introduces industry best practices.
Code Review 1
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Participates in code review as reviewer. Understands feedback from senior developers. Learns to write code following team standards.
Reviews ML code: checks for data leakage, feature correctness, model evaluation. Gives constructive feedback. Verifies experiment reproducibility.
Defines code review practices for ML team. Conducts architecture review for ML projects. Trains the team on effective review.
Defines code review culture for ML organization. Conducts cross-team architecture reviews. Establishes review standards.
Containerization 1
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Writes Dockerfile for ML projects. Installs ML dependencies (PyTorch, scikit-learn) in Docker. Uses docker-compose for local ML development. Understands multi-stage builds.
Creates optimized Docker images for ML: multi-stage for training and serving, CUDA-based images. Configures GPU access in Docker. Uses .dockerignore for ML artifacts.
Defines Docker standards for ML organization. Designs container strategy for ML platform. Coordinates with DevOps on ML-specific container requirements.
Defines containerization strategy for enterprise ML. Evaluates container runtimes for ML workloads. Designs container orchestration for ML platform.
Data Quality 1
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Understands importance of data quality for ML. Performs basic checks: null values, duplicates, distribution shifts. Uses pandas profiling for EDA.
Uses Great Expectations/Soda for data validation. Configures automated data quality checks in ML pipeline. Monitors data drift before retraining.
Designs data quality framework for ML. Integrates data validation into ML pipeline. Configures alerting on data anomalies. Defines data quality SLAs for ML.
Defines data quality strategy for ML organization. Introduces data quality culture in ML team. Coordinates with Data Engineering on data quality.
Deep Learning 1
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Trains neural networks in PyTorch using nn.Module. Writes training loops with DataLoader, loss computation, backpropagation and optimizer steps. Understands tensor shapes, device management (CPU/GPU) and model checkpointing. Uses torchmetrics for evaluation.
Designs custom models in PyTorch. Configures training loop: optimizer, scheduler, early stopping. Uses transfer learning (fine-tuning pretrained models). Logs experiments in MLflow/W&B.
Designs custom architectures and training frameworks. Optimizes inference: ONNX export, TensorRT. Configures distributed training (DDP, FSDP). Works with PyTorch Lightning for production training.
Defines deep learning strategy for the organization. Designs training infrastructure. Standardizes training patterns and evaluation. Coordinates GPU resources.
Git & Workflows 1
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Uses Git for ML projects: branching, committing, PRs. Knows .gitignore for ML artifacts (models, data). Understands DVC basics for data versioning.
Uses DVC for version control of data and models. Organizes ML code by branches: experiments, features, releases. Resolves conflicts in ML configurations.
Defines Git practices for ML organization. Designs repository strategy for ML. Standardizes branching model and review process.
Defines version control strategy for enterprise ML. Designs repository architecture for ML platform. Evaluates tools for ML version control.
Kubernetes & Orchestration 1
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Understands basic Kubernetes concepts for deploying ML training jobs and model serving endpoints. Follows kubectl commands to check training pod status and GPU resource allocation. Uses team-provided manifests for deploying inference services with KServe or Seldon.
Deploys ML services in Kubernetes. Configures resource limits for CPU/GPU workloads. Uses ConfigMaps for model configuration. Configures HPA for ML serving autoscaling.
Defines Kubernetes strategy for ML platform. Designs multi-tenant ML infrastructure. Coordinates with Platform Engineering on ML requirements.
Defines infrastructure strategy for enterprise ML. Evaluates managed Kubernetes vs self-managed for ML. Designs multi-cluster ML architecture.
Logging 1
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Adds logs to code (info, warning, error). Uses logging framework (logging/winston/logback). Logs errors with traceback/stacktrace.
Writes structured logs in JSON format. Adds correlation IDs for tracing. Uses proper log levels. Configures log aggregation (EFK/Loki). Does not log sensitive data (PII, passwords).
Designs logging strategy: retention, sampling, costs. Configures centralized logging (EFK/Loki/Datadog). Optimizes log volume and cost. Integrates logs with traces (via OpenTelemetry). Configures log-based alerting.
MLOps 6
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Logs ML experiments: parameters, metrics, models. Uses MLflow or W&B for comparing experiments. Understands the importance of reproducibility.
Designs experiment tracking workflow. Organizes experiments by projects/tasks. Configures hyperparameter sweeps (Optuna, W&B Sweeps). Analyzes results for decision making.
Designs experiment tracking infrastructure. Automates experiment analysis. Integrates tracking with CI/CD for automated model promotion.
Defines experiment tracking standards. Introduces culture of experimentation. Standardizes metrics and evaluation.
Understands feature store concept: online vs offline store, feature reuse. Reads features from Feast for training. Understands feature freshness and consistency.
Configures Feast for the project. Defines feature definitions (entities, feature views). Configures materialization for online store. Integrates feature store with training pipeline.
Designs feature store architecture. Optimizes materialization for large volumes. Configures streaming feature computation. Ensures feature consistency between training and serving.
Defines feature store strategy for the organization. Evaluates Feast vs Tecton vs custom solution. Designs feature governance and discovery.
Understands ML pipeline concept: data → features → training → evaluation → deployment. Writes simple pipeline scripts. Uses Airflow DAG for basic ML workflow.
Designs ML pipelines with Kubeflow/Airflow. Configures parameterized pipelines for different models. Automates retraining with data quality checks. Implements pipeline testing.
Designs ML pipeline architecture. Optimizes pipeline execution (caching, parallel steps). Configures CI/CD for pipeline deployment. Ensures reproducibility.
Defines ML pipeline strategy. Standardizes pipeline components. Designs pipeline templating for faster development.
Uses MLflow for experiment logging: parameters, metrics, artifacts. Compares experiments in MLflow UI. Saves models through mlflow.log_model.
Designs MLflow workflow: experiment naming, run tags, artifact storage. Uses Model Registry for versioning. Configures autologging for sklearn/PyTorch. Writes custom MLflow Plugins.
Designs MLflow infrastructure for the team. Configures MLflow on Kubernetes. Integrates MLflow with CI/CD for automated model promotion. Creates custom model flavors.
Defines experiment tracking strategy for the organization. Evaluates MLflow vs W&B vs ClearML. Designs model governance workflow. Standardizes ML lifecycle.
Understands model monitoring concept: prediction quality, data drift, latency. Configures basic model metrics (accuracy, latency). Visualizes model performance.
Configures data drift detection (Evidently, NannyML). Monitors feature distributions. Configures alerting on model degradation. Implements automated retraining trigger.
Designs model monitoring architecture. Configures custom monitoring for specific ML tasks. Integrates monitoring with ML pipeline for closed-loop retraining.
Defines model monitoring strategy. Standardizes monitoring practices. Designs model observability platform.
Deploys ML model as REST API through web framework/Flask. Understands inference pipeline: preprocessing → prediction → postprocessing. Uses pickle/joblib for model serialization.
Uses model serving frameworks: Triton, BentoML, Seldon. Configures batch and real-time inference. Optimizes inference latency (ONNX, model optimization). Configures A/B testing for models.
Designs model serving architecture. Optimizes throughput (batching, GPU scheduling). Configures autoscaling for ML serving. Implements model fallback and canary deployment.
Defines model serving strategy for the platform. Designs unified serving layer. Optimizes serving costs. Coordinates with DevOps on infrastructure.
OOP & Design Patterns 1
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Understands basic OOP concepts in Python: classes, inheritance, abstract base classes, encapsulation. Applies simple SOLID principles when structuring ML pipeline code. Follows team patterns for model wrapper classes and data processing module organization.
Establishes OOP architectural standards for ML codebase. Conducts code review focusing on proper abstractions. Trains the team on ML framework design.
Defines ML platform architectural strategy. Designs ML platform API with extensibility in mind. Influences open-source ML frameworks.
REST API 1
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Creates CRUD endpoints for ML services. Uses correct HTTP methods and status codes. Understands JSON request/response for ML API. Implements /predict endpoint.
Designs RESTful ML API: batch prediction, model versioning, health checks. Documents ML API with OpenAPI. Implements pagination for prediction results. Handles model errors.
Defines ML API design standards. Designs unified prediction API for all models. Coordinates ML API with frontend/backend teams.
Defines ML API strategy for the platform. Evaluates REST vs gRPC vs streaming for ML. Designs API architecture for enterprise ML serving.
Unit Testing 1
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Writes unit tests for ML code: data processing, feature engineering. Uses testing tools for ML projects. Tests input/output shapes and types.
Writes comprehensive tests for ML: data validation, model prediction format, edge cases. Uses fixtures for ML test data. Tests pipeline components in isolation.
Defines testing standards for ML organization. Introduces ML-specific testing practices (data tests, model tests). Trains the team on ML testing.
Defines ML testing strategy for enterprise. Designs ML quality assurance framework. Evaluates ML testing tools.
Web Frameworks 1
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Uses FastAPI/Flask for creating simple ML API endpoints. Understands request/response lifecycle. Implements predict endpoint for ML model.
Designs ML API with FastAPI: async endpoints, pydantic validation, batch prediction. Implements health checks and model versioning in API. Integrates API with model registry.
Defines ML API standards for the team. Designs unified API gateway for ML services. Coordinates ML API integration with backend teams.
Defines ML API strategy at platform level. Designs API architecture for ML platform. Evaluates REST vs gRPC vs streaming for ML serving.