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Phase III: Deployment and Maintenance
Phase III is divided into two stages: Deployment and Feedback. Let us discuss about them in detail.
Deployment and F eedb ack
Excellent! You've got a fantastic model that's ready for production. AI engineers now deploy a trained model, making it
available for external inference requests.
his is the final phase of the machine learning lifecycle. ut the task isn't done e can't ust sit back and ait for a ne
project. The deployed model’s performance is monitored to ensure that it continues to function at the level required by the
business. We've all heard of several detrimental effects that can occur over time: model deterioration is the most common.
Another useful technique would be to gather samples that were incorrectly processed by the model to determine the
root cause reasons for why this occurred and then use this information to retrain the model to make it more robust to
such data. Such constant research can assist you in better understanding any unforeseen occurrences that your current
model isn’t prepared for.
Experiential Learning
Video Session
Scan the QR code or visit the following link to watch the video: The Machine Learning Lifecycle
https://www.youtube.com/watch?v=ZmBUnJ7lGvQ
After watching the video, answer the following question:
hat do you mean by binary classification ive an e ample
At a Glance
• The AI model cycle provides the right framework to guide us to our goals.
• The AI model cycle basically consists of the phases: problem scoping, data acquisition, data exploration,
modelling, evaluation, deployment and feedback.
• In problem scoping, we specify the problem we want to solve.
• Data is collected from a variety of trusted and genuine sources.
• After examining the pattern, the type of model to be created is determined.
• Modelling is the process through which several models based on graphical data can be constructed and
even tested for advantages and disadvantages.
• engineers go through multiple models to determine the best model configuration.
• The design phase is an iterative process.
• After the modelling is complete, you need to test your model with the test data.
• Finally, after the evaluation, the model cycle is completed and you can deploy the AI model at the client site.
• The 4Ws Problem Canvas helps identify key factors related to the problem.
• In AI modelling, data is divided into two parts—training and testing data.
• Training data must be authentic and is used to teach the machine.
• After the modelling is complete, testing data is used to validate the AI model.
• The deployed model’s performance is monitored to ensure that it continues to function at the level required
by the business.
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