How can you develop AI apps with machine learning for healthcare, finance, and transportation?

2 Select a ML framework and a platform

The next step is to choose a ML framework and a platform that suit your needs and preferences. A ML framework is a library or a tool that provides pre-built algorithms, models, and functions for ML tasks. Some popular ML frameworks are TensorFlow, PyTorch, Scikit-learn, and Keras. A platform is a service or an environment that allows you to run, deploy, and manage your ML models and apps. Some common platforms are Google Cloud AI Platform, Amazon SageMaker, Microsoft Azure Machine Learning, and IBM Watson Studio.

3 Build and train your ML model

The third step is to build and train your ML model using your chosen framework and platform. You need to define the features, the labels, and the architecture of your model, such as the layers, the activation functions, and the loss function. Then, you need to split your data into training, validation, and test sets, and feed them to your model. You also need to tune the hyperparameters, such as the learning rate, the batch size, and the number of epochs, to optimize the performance of your model.

4 Evaluate and improve your ML model

The fourth step is to evaluate and improve your ML model using various metrics and techniques. You need to measure the accuracy, the precision, the recall, the F1-score, and the ROC curve of your model on the test set, and compare them with the baseline or the expected results. You also need to check for overfitting, underfitting, bias, and variance, and apply regularization, dropout, data augmentation, or feature engineering to reduce them. You can also use cross-validation, grid search, or random search to find the best hyperparameters for your model.

5 Deploy and monitor your ML model

The final step is to deploy and monitor your ML model as an AI app for your target users or customers. You need to export your model as a file or a container, and upload it to your platform or a server. Then, you need to create an API or a UI that allows users to interact with your model and get the outputs. You also need to monitor the performance, the reliability, and the security of your model and app, and update them as needed.

6 Examples of AI apps with ML for different industries

For inspiration, we can look at some AI applications with ML for healthcare, finance, and transportation. For example, Medtronic uses AI and ML to monitor glucose levels and adjust insulin delivery for diabetes patients. Zest AI uses AI and ML to assess credit risk and provide fair and transparent lending decisions for financial institutions. Waymo also uses AI and ML to power self-driving cars and provide safe and convenient transportation services.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?