Build an AI application using Python in 10 easy steps

There's a good reason why machine learning has become so popular. Many companies decide to take advantage of this opportunity to create products.

To differentiate your application from the average one and choose the best option for your project, you need to follow a few steps.

This article provides basic steps and options for building successful AI applications using Python and other tools.

First, define the problem you want your AI model to solve. This ranges from predicting customer behavior to automating routine tasks. If you can't find an idea, use ChatGPT or Bard to prompt you for:

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

Now let's take a look at ChatGPT's answer.

Now we have a choice. The next step is to collect data. This step involves retrieving datasets from various repositories and searching for datasets using various APIs and web scraping. If you are looking to use clean and processed datasets, you can collect them using the following resources:

1. GitHub repository: It is a platform for developers where millions of developers collaborate on projects.
2. Kaggle dataset: A machine learning and data science website that hosts datasets, contests, and learning resources.
3. UCL Irvine dataset: A collection of datasets for machine learning research.
4. Google dataset search: A search engine for datasets that can be used to search by keyword or location. Here is the link.
5. AWS Open Data: This program provides access to open data on AWS.

Now you have your goals and your data is ready. It's time to do your thing. Therefore, the next step is to prepare the data to apply the required model. This model can be a machine learning model or a deep learning model. In either case, the data must have certain characteristics, such as:

1. clean: This step becomes more complex if you collect data using web scraping or APIs. Methods such as assignment and deletion should be used to remove duplicates, irrelevant entries, correct types, handle missing values, etc. https://www.stratascratch.com/blog/data-cleaning-101-avoid-these-5-traps-in-your-data/
2. formatted correctly: Now, for the model to apply, the features need to be consistent and relevant. If you have categorical data, you need to encode it to apply machine learning. To create better models, we need to scale and normalize numerical features.
3. balanced: Machine learning requires iteration, which requires the following steps: The dataset must be balanced. That is, we need to ensure that the dataset does not favor one class of hers over another so that the predictions are not biased.
4. Functional design: In some cases, you may need to tune features to improve model performance. You might want to remove some features that hurt the model's performance, or combine them to improve it. https://www.linkedin.com/posts/stratascratch_feature-selection-for-machine-learning-in-activity-7082376269958418432-iZWb
5. Split: Be careful if you are new to machine learning and your model performs very well. In machine learning, some models may not be true and may indicate an overfitting problem. One approach to address this is to split the data into training, testing, and even validation sets.

https://platform.stratascratch.com/technical/2246-overfitting-problem

Now, in this step, everything is ready. Now, which model should we apply? Do you know which one is best? Or should I think about it? Of course, you need an initial suggestion, but one thing you should do is test different models.

You can choose a model from the following Python libraries:

1. Learn with science kits: Perfect for beginners. You can implement machine learning code with minimal code. The official documentation is here: https://scikit-learn.org/stable/
2. tensorflow: Tensorflow is great for scalability and deep learning. This allows you to develop complex models. The official documentation is here: https://www.tensorflow.org/
3. Keras: Runs on TensorFlow, making deep learning easier. Official documentation is here: https://keras.io/
4. pie torch: Generally preferred for R&D because models can be easily modified on the fly. Official documentation is here: https://pytorch.org/

Next, train the model. This involves feeding data into the model. This allows you to learn from patterns and tune their parameters later. This step is easy.

You've trained a model, but how can you tell if it's good or bad? Of course, there are different ways to evaluate different models. Let's explore different model evaluation metrics.

1. regression – MAE measures the average magnitude of the error between predicted and actual values ​​without considering direction. You can also use R2 scores.
2. classification– Precision, recall, and F1 score evaluate the performance of the classification model.
3. Clustering: The evaluation metrics here are usually not that simple, as you need a true label to compare against. However, metrics such as the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index are commonly used.

There are several actions you can take based on the results collected in step 6. These actions can affect model performance. Let's take a look.

1. Fine-tuning hyperparameters: Tuning a model's hyperparameters can significantly change its performance. Control the learning process and model structure.
2. Choice of different algorithms: In some cases, you may find a better option than the initial model. Therefore, it is a good idea to consider different algorithms, even if you are already in the process.
3. Add more data: More data often leads to better models. Therefore, if you need to improve the performance of your model and have the budget for data collection, adding more data is a wise choice.
4. Features engineering: In some cases, the solution to your problem may be out there, waiting for you to discover it. Feature engineering can be the most cost-effective solution.

The model is ready, but we need an interface. It's currently on Jupyter Notebook or PyCharm, but I need a user-friendly front end. To do this, you need to develop a web application. Its options are:

1. django: Fully featured and extensible, but should be made more beginner-friendly.
2. flask: Flask is a beginner-friendly micro web framework.
3. Fast API: This is the modern, fast way to build web applications.

Your model could be the best ever developed. However, I don't know if it remains on my local drive. Sharing your model with the world and publishing it is a good choice to get feedback, see real-world impact, and grow your model more efficiently.

To do so, you have the following options:

1. AWS: AWS offers larger applications with multiple options for each action. For example, with databases, there are options that you can choose to extend.

2. Heroc: Heroku is a platform-as-a-service that allows developers to build, run, and operate entire applications in the cloud.
3. Pythonanywhere.com: Pythonanywhere is a cloud service for Python-specific applications. Perfect for beginners.

There are many ways to share your AI models with the world, but if you like writing, let's talk about one popular and easy way.

1. Content marketing: Content marketing involves creating valuable content, such as blog posts and videos, to showcase the capabilities of your AI model and attract potential users. Want to learn more about effective content marketing strategies?
2. Community participation: Online communities like Reddit allow you to share insights about your AI models, increase credibility, and connect with potential users.
3. Partnerships and collaborations: By partnering with other experts in the field, you can expand the scope of your AI models and access new markets. If you've been writing about apps on Medium, try collaborating with writers who write in the same field.
4. Paid advertising and promotions: Paid advertising channels such as Google Ads and other social media ads can help increase awareness and attract users to your AI models.

After completing all 10 steps above, maintain the developed application consistently.

In this article, we have described the 10 ultimate steps to building and deploying AI applications using Python.

Nate Rossidi I am a data scientist and work in product strategy. He is also an adjunct professor teaching analytics and the founder of StrataScratch, a platform that helps scientists prepare for interviews by providing real interview questions from top companies. Nate writes about the latest trends in the career market, offers interview advice, shares his projects in the science of data, and covers all things SQL.