Legacy

Legacy deployment refers to the traditional and older method of deploying artificial intelligence models for use in production environments. This approach is typically used when pre-trained models need to be deployed and utilized in production systems. Below are the details regarding development and deployment using the legacy approach:

  1. Development:

    • During the development phase, the AI model is trained and optimized using training data.

    • This phase involves selecting and training the appropriate model architecture, tuning parameters, applying training techniques, and evaluating model performance.

  2. Preparation for Deployment:

    • After training the model, it needs to be converted to an executable format and prepared for deployment.

    • This involves converting the model to standard formats such as TensorFlow SavedModel or ONNX and requires advanced configuration for execution in different environments.

  3. Deployment:

    • In this stage, the prepared model is deployed on servers or local systems.

    • It includes installing and configuring the necessary systems for execution, transferring the model to the production environment, and running the model to respond to input requests.

  4. Maintenance and Monitoring:

    • After deployment, the model needs to be maintained and its performance monitored.

    • This includes monitoring the model's performance, tracking resource consumption, troubleshooting, and necessary updates for improving performance.

In summary, in legacy deployment, pre-trained models are executed on servers or local systems to function as part of production systems and provide services.

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