subject: some alternatives to the data science process [print this page]
DataOps (Data Operations) Phases: Data Collection: Collect data from various sources. Data Processing: Clean, preprocess, and transform data. Data Integration: Integrate data into a unified format. Model Development: Develop and train models. Model Deployment: Deploy models into production. Monitoring and Maintenance: Continuously monitor and maintain models.
Agile Data Science Phases: Sprint Planning: Define project goals and deliverables. Data Exploration: Explore and understand data. Model Prototyping: Develop initial model prototypes. Model Iteration: Continuously refine models based on feedback. Deployment: Deploy models into production environments.
Learn Data Science Principles: Eliminate Waste: Focus on activities that add value. Build Quality In: Ensure data quality and model reliability. Deliver Fast: Deliver solutions quickly and iterate. Respect People: Encourage collaboration and communication. Optimize the Whole: Optimize the entire data science process.
Data-Centric AI Development Phases: Data Collection: Focus on collecting high-quality data. Data Labeling: Accurately label data for training. Data Augmentation: Enhance data with additional information. Model Training: Train models with an emphasis on data quality. Evaluation: Evaluate models with diverse datasets. Deployment: Deploy models and monitor performance.
Six Sigma in Data Science Phases: Define: Define project goals and customer requirements. Measure: Collect and measure data. Analyze: Analyze data to identify patterns and root causes. Improve: Develop and implement solutions to improve processes. Control: Monitor and control processes to ensure sustained improvements.
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