Build an AI App with Python in 10 Easy Steps

There are good reasons why machine learning has become so famous. Many companies decide to create products taking advantage of this opportunity.

You need to follow a few steps to distinguish your app from the average one and choose the best options for your project.

This article will explore the fundamental steps and options for creating a successful ai application with Python and other tools.

Start by defining the problem you aim to solve with your ai model. This could range from predicting customer behavior to automating a routine task. If you're having trouble finding an idea, use ChatGPT or Bard with the following message.

Generate 5 ideas about ai Applications that I'll build with Python.

Now, let's look at ChatGPT's response.

Now we have options. The next step will be to collect your data. This step includes fetching data sets from different repositories or finding them using different APIs or web scraping. If you are considering using data sets that are clean and processed, you can use the following resources to collect the data set:

GitHub repositories: He is a platform for developers where millions of developers collaborate on projects.
Kaggle Dataset: A machine learning and data science website which hosts datasets, contests and learning resources.
UCL Irvine data set: It is a collection of data sets for machine learning research.
Google Dataset Search: It is a data set search engine that can be used to search by keyword or location. Here is the link.
AWS Open Data: This The program provides access to open data on AWS.

Now you have your goal and your data is ready. It's time to do your thing. So, the next step will be to prepare your data to apply the model you want. This model can be a machine learning or deep learning model. Either way, there are specific characteristics that your data must have:

Clean: This step will be more complicated if you collect data through web scraping or using APIs. You need to remove duplicates, irrelevant entries, fix types, handle missing values, and more using methods like imputation or elimination. ai+application” rel=”noopener” target=”_blank”>https://www.stratascratch.com/blog/data-cleaning-101-avoid-these-5-traps-in-your-data/
Formatted correctly: Now, to apply your model, the features must be consistent and appropriate. If you have categorical data, it needs to be encoded to apply machine learning. Its numerical features should be scaled and normalized to have a better model.
Balanced: Machine learning needs iteration, which requires steps that you must follow, such as This. Your data set needs to be balanced, meaning you need to make sure it doesn't favor one class over others to ensure your predictions aren't biased.
Designed Feature: Sometimes, you need to adjust your functions to increase the performance of your model. You can remove some features that ruin the performance of your model or combine them to improve it. https://www.linkedin.com/posts/stratascratch_feature-selection-for-machine-learning-in-activity-7082376269958418432-iZWb
Split: If you are new to machine learning and your model performs exceptionally well, be careful. In machine learning, some models may be too good to be true, indicating an overfitting problem. To address this, one approach is to split the data into training, testing, and sometimes even validation sets.

ai+application” rel=”noopener” target=”_blank”>https://platform.stratascratch.com/technical/2246-overfitting-problem

Ok, in this step, everything is ready to go. Now, what model will you apply? Can you guess which one is the best? Or should you think? Of course, you should have an initial suggestion, but one thing you should do is try different models.

You can choose a model from the following Python libraries:

Science Kit Learn: It is ideal for beginners. You can implement machine learning code with minimal code. Here is the official documentation: https://scikit-learn.org/stable/
tensor flow: Tensorflow can be great for scalability and deep learning. Allows you to develop complex models. Here is the official documentation: https://www.tensorflow.org/
Hard: Runs on top of TensorFlow, making deep learning easier. Here is the official documentation: https://keras.io/
PyTorch: Generally preferred for research and development because it is easy to change models on the fly. Here is the official documentation: https://pytorch.org/

Now it's time to train your model. It involves entering data into the model, which allows us to learn from patterns and adjust its parameters later. This step is simple.

You've trained your model, but how can you determine if it's good or bad? Of course, there are various methods to evaluate different models. Let's explore a variety of model evaluation metrics.

Regression – MAE measures the average magnitude of the errors between the predicted and actual values without considering their direction. Additionally, the R2 score can be used.
Classification– Precision, recall and F1 scores evaluate the performance of the classification model.
Group: The evaluation metrics here are less straightforward because we generally need true labels to compare against. However, metrics such as the Silhouette Score, the Davies-Bouldin index, and the Calinski-Harabasz index are commonly used.

Depending on the result, which was collected in Step 6, there are several actions you can take. These actions can affect the performance of your models. We'll see.

Adjust hyperparameters: Tuning your model's hyperparameters can significantly change its performance. Controls the learning process and the structure of the model.
Select different algorithms: Sometimes, there may be better options than the initial model. That's why exploring different algorithms might be a better idea, even if you're already halfway through the process.
Add more data: More data often leads to a better model. Therefore, adding more data would be a good choice if you need to improve model performance and have a budget for data collection.
Feature Engineering: Sometimes the solution to your problem may be out there, waiting for you to discover it. Feature engineering could be the most cost-effective solution.

Your model is ready, but it needs to have an interface. You're now in Jupyter Notebook or PyCharm, but you need an easy-to-use interface. To do this, you need to develop a web application and these are your options.

Django: It is full-featured and scalable, but needs to be more beginner-friendly.
Flask: Flask is a beginner-friendly microweb framework.
Fast API: It is a modern and fast way to create web applications.

Their model could be the best ever developed. However, you can't be sure if it stays on your local drive. Sharing your model with the world and putting it to work will be good options to get feedback, see real impacts, and grow it more efficiently.

To do that, here are your options for doing so.

1. AWS: AWS offers greater scale of applications, with multiple options for each action. For example, for databases, they have options that you can choose and scale.

heroku: Heroku is a platform as a service that allows developers to build, run, and operate applications entirely in the cloud.
Pythonanywhere.com: Pythonanywhere is a cloud service for specific Python applications. It is excellent for beginners.

There are too many ways to share your ai model with the world, but let's look at one of the most famous and easiest ways if you like to write.

Content Marketing: Content marketing involves creating valuable content, such as blog posts or videos, to showcase the capabilities of your ai model and attract potential users. To learn more about effective content marketing strategies, check out this.
Community participation: online communities such as reddit allows you to share information about your ai model, build credibility, and connect with potential users.
Partnership and collaboration: Partnering with other professionals in the field can help expand the reach of your ai model and access new markets. If you were writing about your application in HalfTry to collaborate with writers who wrote in the same niche.
Paid advertising and promotion: paid advertising channels, such as Google Ads or other social media ads, can help increase visibility and attract users to your ai model.

After finishing the ten steps listed above, it's time to be consistent and keep your app developed.

In this article, we go over the final ten steps for creating and deploying ai applications with Python.

Nate Rosidi He is a data scientist and in product strategy. He is also an adjunct professor of analysis and is the founder of StrataScratch, a platform that helps data scientists prepare for their interviews with real questions from top companies. Connect with him on Twitter: StrataScratch either LinkedIn.