Who should use the AI Model Development workflow?
Teams or solo builders working on ai development tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
3-step pipeline — follow each step in order
1Setup · Step 1 of 3
Open task pageBuild AI Models
Construct the initial model architecture using high-level frameworks like Lightning AI, which simplify prototyping and experimentation. This step sets the foundation before training.
Why this matters
A well-designed model architecture is critical for achieving desired performance; building efficiently saves time and resources.
What you get
You will have a draft model structure ready for training, with baseline layers and hyperparameters defined.
More optionsCompare
2Input · Step 2 of 3
Open task pageTrain AI Models
Train the built model on a large dataset using a high-performance platform like Together AI, leveraging distributed compute and optimized kernels for faster convergence.
Why this matters
Training is the most compute-intensive phase; using efficient infrastructure reduces cost and time to deployment.
What you get
You will have a trained model with learned weights, ready for evaluation and fine-tuning.
More optionsCompare
3Deliver · Step 3 of 3
Open task pageEvaluate AI Models
Assess the trained model's performance on validation benchmarks using tools like Together AI's evaluation suite to measure accuracy, loss, and generalization.
Why this matters
Rigorous evaluation ensures the model meets quality standards and identifies overfitting or underfitting issues early.
What you get
You will have a performance report and decision to proceed to deployment or iterate on training.
More optionsCompare