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MOSTLY AI

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Once you've created and deployed automated workflows in Mostly AI, it's essential to monitor their performance and make necessary adjustments to ensure they operate efficiently and effectively.  Proper monitoring and management allow you to identify issues, optimize workflows, and maximize their value.

  Review the workflow dashboard to track key performance metrics:

  Examine detailed error logs to identify any problems that occurred during workflow execution.  

  Create visualizations to gain insights into workflow performance over time.

By actively monitoring and managing your automated workflows, you can optimize their efficiency, ensure their accuracy, and maximize their value within your machine learning processes.

Tips on monitoring your workflow's performance, managing errors, and optimizing efficiency.

Monitoring and Managing Workflows

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.

 Within your project, go to the "Workflows" section.

  Click "New Workflow" and give it a descriptive name.

  Build your workflow step by step using a visual editor.  

  Define conditions or events that trigger the workflow: 

  Use connector lines to link different steps in your workflow, defining the sequence of operations.

  Thoroughly test your workflow with sample data to ensure it functions as intended.

  Automated workflow to ingest new data, preprocess it, train a model, and update the model for live predictions.

  Workflow to generate predictions, analyze results, and trigger alerts if certain conditions are met (e.g., fraud detection).

  Automated workflow to generate reports and visualizations based on prediction results.

  Streamline your machine learning processes, saving time and effort.

  Reduce errors and inconsistencies by automating tasks.

  Easily scale your workflows as your data volume or needs grow.

  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.

A step-by-step guide on building automated workflows to streamline data processing, model training, and prediction generation.

Creating Automated Workflows

Triggers and actions are fundamental components of automated workflows in Mostly AI, allowing you to control when and how your workflows execute.  By strategically using triggers and actions, you can create more powerful and dynamic workflows that adapt to your specific needs and data patterns.

Triggers initiate the execution of your workflow under specific conditions or events.

Actions: Performing Tasks Within Your Workflow

Actions are the steps performed within your workflow, driven by triggers. 

 A workflow could be triggered by an alert from the fraud detection model, initiating a sequence of actions that investigate the suspected fraud, report it to a security team, and block potentially fraudulent transactions. 

By combining thoughtfully selected triggers and actions, you can build powerful workflows in Mostly AI that automate complex processes and enhance your machine learning operations.

Learn about different triggers and actions that can be incorporated into your workflows for dynamic behavior.