Introduction to Custom Machine Learning Models

Machine learning is a rapidly evolving field that offers endless possibilities for various industries. With new research and advancements being released regularly, it’s now possible to leverage state-of-the-art machine learning models for your own data and applications. This article will guide you through the process of customizing machine learning models to suit your specific needs, without the need for extensive expertise in mathematics or statistics. By harnessing the power of transfer learning and utilizing tools like TensorFlow Lite Model Maker and TensorFlow Hub, you can easily adapt existing models to your own data and achieve impressive results.

Understanding Transfer Learning

Transfer learning is a technique that allows you to leverage pre-trained models and customize them to your specific problem or dataset. Pre-trained models are networks that have been trained on large-scale datasets and have learned general features of the visual or data world. By using these pre-trained models, you can take advantage of the knowledge they have acquired and apply it to your own data without starting from scratch.

There are two main ways to customize a pre-trained machine learning model:

  1. Feature Extraction: This approach involves using the representations learned by a pre-trained model to extract significant features from new samples. By adding a new classifier on top of the pre-trained model, you can repurpose the feature maps learned by the model for your own dataset. This method is faster to train and is suitable for cases where the pre-trained model’s final classification part is not specific to your target classes.
  2. Fine-Tuning: Fine-tuning allows you to unfreeze a few of the top layers of a pre-trained model and jointly train both the newly-added classifier layers and the last layers of the base model. This process enables you to fine-tune the higher-order feature representations in the base model to make them more relevant for your specific task. Fine-tuning generally produces better results but requires more computational resources and expertise.

Using TensorFlow Lite Model Maker

One way to implement transfer learning is by utilizing the TensorFlow Lite Model Maker library. This open-source library simplifies the process of transfer learning and makes it more accessible to developers, even those without extensive machine learning experience. The library automates most of the data pipeline and model creation steps, making it easier to customize models for your own data.

To get started with TensorFlow Lite Model Maker, you’ll need a basic knowledge of TensorFlow and the Keras API. You can execute all the code in Google Colaboratory, so there’s no need to install anything on your local machine. The library also allows you to export the resulting model for execution on mobile devices or in web applications.

The steps involved in using TensorFlow Lite Model Maker are as follows:

  1. Load the Data: Begin by loading your data into the library. This step involves preparing your dataset and splitting it into appropriate training and validation sets.
  2. Create and Train the Model: Use the dataset loaded in the previous step to create and train your customized model. TensorFlow Lite Model Maker will handle most of the model creation process, making it easier for you to focus on your specific problem.
  3. Evaluate the Model: After training, evaluate the performance of your model using the validation set. This step allows you to assess the accuracy and effectiveness of your customized model.
  4. Export the Model: Once you’re satisfied with the performance of your model, you can export it for use in mobile or web applications. TensorFlow Lite Model Maker makes this process simple and streamlined.

It’s important to note that while TensorFlow Lite Model Maker offers convenience and ease of use, it may have fewer configuration possibilities compared to building the full pipeline and model from scratch. Additionally, not all models can be used as a base for transfer learning, and the library may not be suitable for handling large datasets with complex data pipelines.

Leveraging TensorFlow Hub Models

Another way to leverage transfer learning is by utilizing TensorFlow Hub, which is a model repository for TensorFlow models. TensorFlow Hub provides access to a vast collection of machine learning models contributed by researchers and the community. These models are often state-of-the-art and cover a wide range of tasks, including image classification, text analysis, and more.

To use TensorFlow Hub models, you’ll need to find a model suitable for your task. You can search and explore the models available on the TensorFlow Hub website, where you’ll find detailed documentation and code snippets for each model. Once you’ve selected a model, you can easily access it using its URL or handle.

To customize a TensorFlow Hub model, you’ll need to load it using the KerasLayer method from the TensorFlow Hub library. This method allows you to use the pre-trained model as a layer in your own model architecture. By building your model around this pre-trained layer, you can take advantage of the learned representations without starting from scratch. After customizing the model, you can train it using your own data and evaluate its performance.

One advantage of using TensorFlow Hub models is the wide variety of models available for different domains and tasks. Researchers and the community contribute these models, ensuring that you have access to the latest advancements and state-of-the-art models. However, using TensorFlow Hub models may require a deeper understanding of TensorFlow and Keras APIs compared to TensorFlow Lite Model Maker.

Configuring Custom Machine Learning Models in OpenPages

In addition to the aforementioned methods of customizing machine learning models, there are other platforms and tools that allow you to integrate custom models into specific applications or systems. One such example is the Custom Machine Learning Models integration in OpenPages.

OpenPages is a software platform that enables organizations to manage and mitigate risk, compliance, and audit activities. The Custom Machine Learning Models integration in OpenPages allows users to deploy and use custom machine learning models within the OpenPages environment. This integration is available in OpenPages version and later.

To configure custom machine learning models in OpenPages, users need the Custom Machine Learning Models application permission. Once granted this permission, they can access the Integrations menu and find the Custom Machine Learning Models option.

The integration supports various machine learning engines, including H2O, Caret, and Chemprop. However, users also have the flexibility to build and configure their own customizable machine learning models using the libraries and languages of their choice. This capability gives users full control over the algorithms and models they want to use in their custom data pipelines.

The process of configuring custom machine learning models in OpenPages involves two main steps: training and applying the model.

In the training step, users need to train and test their custom models using the chosen machine learning engine, ensuring that the models have sufficient accuracy before deploying them in OpenPages. This step typically requires the expertise of a data scientist to ensure the models are properly trained and validated.

Once the model is trained and tested, it can be stored as an object in a given directory. In the application step, the trained model is applied to the provided feature columns, and the resulting predictions are returned as insights in OpenPages views and workflows.

To configure the model inputs and outputs in OpenPages, users need to understand the model output format and the specific components of the model’s output that they want to extract. They also need to familiarize themselves with JSONata syntax, which is used to extract relevant information from the model’s output.

After configuring the model inputs and outputs, users can add the model configuration to a view in OpenPages. This allows users to access the model and generate insights when viewing specific data or workflows.

Furthermore, OpenPages provides the capability to test the model within the platform. This feature allows users to validate the model’s performance and ensure that it generates accurate predictions before deploying it in production.

By integrating custom machine learning models into OpenPages, organizations can enhance their risk management, compliance, and audit activities. This integration enables the use of AI models built with AutoAI on Watson Studio or developed by data science teams, which can be deployed on IBM Watson Machine Learning on IBM Cloud. Users can connect data from OpenPages fields as inputs to these models, generating live predictions that can be used as valuable insights within the OpenPages platform.


Customizing machine learning models to suit your specific needs and data is now more accessible than ever. Whether you choose to utilize transfer learning with tools like TensorFlow Lite Model Maker and TensorFlow Hub, or integrate custom models into platforms like OpenPages, the possibilities for leveraging machine learning are vast.

Transfer learning allows you to take advantage of pre-trained models and adapt them to your unique datasets, saving time and resources in model creation and training. TensorFlow Lite Model Maker provides an easy-to-use interface for customizing models and exporting them for deployment on mobile and web applications. TensorFlow Hub offers a wide range of pre-trained models contributed by researchers and the community, allowing you to leverage state-of-the-art models for your specific tasks.

Integrating custom machine learning models into platforms like OpenPages enhances risk management, compliance, and audit activities. By deploying custom models within the OpenPages environment, organizations can generate valuable insights and predictions that aid in decision-making and risk mitigation.

As machine learning continues to advance, the possibilities for customization and integration of models will only grow. Whether you’re a developer, data scientist, or business professional, exploring and utilizing custom machine learning models can unlock new opportunities and drive innovation in your field. So, take the plunge into the world of custom machine learning models and unleash the power of AI in your applications and workflows.

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