Who should use the Build and Deploy an AI Model workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
4-step pipeline — follow each step in order
1Setup · Step 1 of 4
Open task pagePrepare and train initial models
Set up and train foundational machine learning models using Kaggle to create a baseline model that will be the starting point for further building and refinement.
Why this matters
Training a baseline model provides a reliable starting point, reducing the risk of errors in later stages.
What you get
A trained baseline model is ready for the build step, ensuring foundational data and parameters are set.
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2Input · Step 2 of 4
Open task pageBuild and refine AI model
Use Lightning AI to build the core AI model based on the trained foundation, incorporating additional architectures and configurations to achieve desired performance.
Why this matters
This is the central step where the final model is constructed; its success directly impacts the outcome quality.
What you get
A fully built AI model is produced, ready for evaluation.
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3Refine · Step 3 of 4
Open task pageEvaluate and validate model performance
Assess the built AI model using Together AI Platform to measure accuracy, precision, and other metrics, identifying areas for improvement before deployment.
Why this matters
Evaluation catches performance issues and ensures the model meets accuracy requirements before deployment.
What you get
A validated model with clear metrics is ready for development and deployment.
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4Deliver · Step 4 of 4
Open task pageDeploy and integrate the model
Use MathWorks MATLAB AI to package the validated model into a production-ready application, enabling integration into real-world systems.
Why this matters
Development turns the validated model into a usable application, enabling real-world use.
What you get
The final AI model is deployed and integrated, ready for end users.
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