Creating Automated Workflows
Last updated July 29, 2024
Automating your machine learning processes with workflows in Mostly AI can streamline your operations, save time, and improve efficiency. Workflows allow you to combine different tasks, such as data preprocessing, model training, prediction generation, and data visualization, into a single automated sequence.
Building Your Workflow
- Navigate to the "Workflows" Section: Within your project, go to the "Workflows" section.
- Create a New Workflow: Click "New Workflow" and give it a descriptive name.
- Add Steps: Build your workflow step by step using a visual editor.
- Data Preparation: Include steps for data cleansing, transformation, feature engineering, and data splitting.
- Model Training: Select the model you want to train and configure any necessary parameters.
- Prediction Generation: Choose the prediction mode (batch or real-time) and specify the data to use for generating predictions.
- Output Handling: Define steps for handling the predictions, such as visualizing results, saving to a file, or sending notifications.
- Configure Triggers: Define conditions or events that trigger the workflow:
- Scheduled Triggers: Run the workflow at regular intervals (daily, weekly, monthly).
- Event-Driven Triggers: Initiate the workflow when specific events occur, such as new data arriving or predictions exceeding a certain threshold.
- Connect Steps: Use connector lines to link different steps in your workflow, defining the sequence of operations.
- Test and Validate: Thoroughly test your workflow with sample data to ensure it functions as intended.
Workflow Examples
- Data Ingestion and Model Training: Automated workflow to ingest new data, preprocess it, train a model, and update the model for live predictions.
- Prediction Generation and Alerting: Workflow to generate predictions, analyze results, and trigger alerts if certain conditions are met (e.g., fraud detection).
- Data Visualization and Reporting: Automated workflow to generate reports and visualizations based on prediction results.
Benefits of Automated Workflows
- Efficiency: Streamline your machine learning processes, saving time and effort.
- Reliability: Reduce errors and inconsistencies by automating tasks.
- Scalability: Easily scale your workflows as your data volume or needs grow.
- Flexibility: Create custom workflows tailored to your specific requirements.
By building and utilizing automated workflows in Mostly AI, you can significantly improve the efficiency and effectiveness of your machine learning projects.
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