Essential Tools, Activities, and Effort Estimates for Each Stage of the Machine Learning Lifecycle
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Below is a comprehensive breakdown of the Machine Learning Lifecycle stages along with detailed information on the activities involved, tools commonly used, and estimates of human effort required for each stage (considering medium complexity and low data volume).
ML Stage | Stage's Major Activity | ML Open Source Tools | ML Cloud Tools | Human Efforts (Hrs) |
|---|---|---|---|---|
Data Collection | Data sourcing, Data scraping, Data labeling, Data ingestion, Data storage | Scrapy, Apache Kafka, Label Studio | AWS S3, Google BigQuery, Azure Data Lake Storage | 40-80 hours |
Data Processing | Data cleaning, Handling missing values, Data transformation, Data augmentation, Outlier detection | Pandas, Dask, PySpark | AWS Glue, Google Dataflow, Azure Data Factory | 40-100 hours |
Feature Engineering | Feature selection, Feature scaling, Encoding categorical variables, Feature transformation | Scikit-learn, Feature-engine, tsfresh | AWS SageMaker Data Wrangler, GCP DataPrep, Azure ML | 40-80 hours |
Model Development | Model selection, Model training, Hyperparameter tuning, Cross-validation, Experiment tracking | TensorFlow, PyTorch, MLflow | AWS SageMaker, Google Vertex AI, Azure ML | 80-200 hours |
Model Evaluation | Model accuracy testing, Performance metrics calculation, Validation on test set, Bias and fairness analysis | Scikit-learn, Fairlearn, Alibi | AWS SageMaker Clarify, Google What-If Tool, Azure ML | 40-60 hours |
Model Deployment | Model packaging, API integration, Infrastructure setup, CI/CD pipeline setup | Docker, Kubernetes, BentoML | AWS SageMaker Endpoints, Google Cloud Run, Azure AKS | 40-80 hours |
Model Monitoring | Drift detection, Performance monitoring, Error tracking, Retraining pipeline | Evidently AI, Prometheus, Grafana | AWS CloudWatch, Google Monitoring, Azure Monitor | 40-60 hours |
Feedback Alert System | User feedback integration, Real-time alert setup, Issue tracking, Model update notifications | Kafka, Airflow, Prometheus | AWS SNS, Google Pub/Sub, Azure Event Grid | 20-40 hours |
This table outlines common tools and time estimates for projects, noting that actual requirements can vary depending on project intricacy and data volume.