Until now, we have discussed multiple optimization algorithms and techniques that can influence your model's performance and MLOps pipeline, which can save both resources and hours for your team and investors. Still, in all that clutter, you may have been looking for the most fundamental of them all!

Ignoring any fancy architectures and hardware, the most significant influencer on your model after your data are the hyperparameters your model is trained upon. Number of Epochs, Number of K-Folds, Hours of Training, Learning Rate, and much more, all parameters can be classified as hyperparameters in a Machine Learning pipeline.

Unlike other standard parameters, hyperparameters require an acute knowledge of your model's work. This reiterative method works towards gaining a set of values that can be used to gain the best from the hardware in the minimum amount of time.

Some broader points for why you need to tune your hyperparameters are:

1. Arriving at the desired loss value sooner

2. Not miss the learnable patterns in the dataset without falling short due to the learning rate

3. Save a bunch of Dollars spent training a model.

Hyperparameters that are available in every model, no matter the complexity, are the ones that can be mastered quickly, and some of them are:

1. Number of Hidden Layers

2. Neurons in layer

3. Learning Rate

4. Numbers of Epoch

5. Momentum

If you wish to know more about this topic and better understand how you can optimize your hyperparameters, head to our blog to work at your model's very best potential.