Easy Way To Bring Ai Model To Production With Fast Ai

Nowadays, deep learning can solve a lot of challenging problems quickly and with a few codes. But underestimating the constraints and…

Easy Way to Bring AI Model to Production with Fast.AI

Nowadays, deep learning can solve a lot of challenging problems quickly and with a few codes. But underestimating the constraints and overestimating deep learning capabilities may lead to frustratingly low results, at least until you gain some experience and can solve the problems that arise. Conversely, overestimating the constraints and underestimating deep learning capabilities may mean you do not attempt a solvable problem because you talk yourself out of it.

Here we will see several steps how smoothly bring to production your model.

First, we need the planning stages, which is includes below checklist:

• What kinds of data we are working with
• How to get the list of items
• How to label these items
• How to create the validation set

The import part of modeling is data augmentation. Data augmentation refers to creating random variations of our input data. They appear different but do not actually change the meaning of the data. Significantly data augmentation will help when you don’t have enough data to train your model. Examples of standard data augmentation techniques for images are rotation, flipping, perspective warping, brightness changes, and contrast changes.

In practice, a deep learning model will be just one piece of a much bigger system. A data product requires thinking about the entire end-to-end process, from conception to use in production. The complexity of managing deployed data products contains multiple versions of models, A/B testing, canarying, refreshing the data, handling data labeling, monitoring all this, detecting model rot, and so forth.

The significant thing to do after bringing the model to production is to omit disaster results. There is an example machine learning model for identifying strokes in CT scans that can alert high priority cases for expedited review. Instead of directly informing patients, it’s better to send to a radiologist fo review.

To decrease disaster results, we can do below:

• Increase the quality of input, training data.
• Ensuring products are returned fast enough to be useful in practice
• Check the development of the model in real-time. If the wrong answers quantity increase, update or upgrade the model.

Now, we are ready to implement the model, let’s create our own model.

By Dr. Farruh on October 26, 2020.

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Exported from Medium on May 25, 2024.