Hybrid Machine Learning

What is hybrid machine learning?

Forrester reports that, “98 percent of organizations said that analytics are important to driving business priorities, yet fewer than 40 percent of workloads are leveraging advanced analytics or artificial intelligence.”

Automation and AI become even more important when you consider the exponential rate at which companies are producing data in need of continuous analysis. AI powered by machine learning (ML) will be critical to managing future insights for data scientists. ML uses algorithms and statistical models to identify patterns, mine data, and apply labels across different datasets. These models learn from the data as they go and will help data scientists develop increasingly sophisticated and accurate predictions.

With the right BI and ML tools in place, companies will be able to extract even greater insights from their data.

Defining hybrid machine learning

Most learning algorithms used in ML are really good at completing one task or working with one dataset. While helpful and infinitely better than doing it manually, these algorithms won’t help you realize the full potential of AI across all of your data.

That’s where hybrid machine learning (HML) comes in. Multiple simple algorithms work together to complement and augment each other. Together they can solve problems that alone they were not designed to solve.

Semi-supervised learning

In semi-supervised learning, you provide the algorithm with a small set of labelled data. Then, you give it a much larger set of unlabeled data and put it to work. This type of algorithm is helpful when you need (or have) to start with a smaller batch of data upfront. It learns from all the data, not just the labelled data, and helps you organize it.

This form of HML is especially helpful with data that changes over time. For example, use it to track things like the cost of supplies. As costs change, it will impact production and forecasts. You can use this method of HML in your inventory and supply chain management to forecast future costs.

Or, it can be useful to track brand sentiment for customer retention. Track how current customers are engaging with or discussing your brand on social media, and use it to develop targeted mitigation strategies when customers fall below a designated threshold.

Often, you can use semi-supervised learning in tandem with unsupervised and supervised learning methods. These additional models can help with grouping and training on unlabeled data.

Self-supervised learning

A self-supervised learning model combines unsupervised and supervised learning problems, then applies a supervised learning algorithm. You can create the model for the algorithm to follow, and it begins applying that to unlabeled data.

This type of learning is commonly used on unlabeled images and defines actions that can be taken on those images—like rotating them, identifying color or grayscale, or distinguishing between real and fake photos.

Multi-instance learning

Multi-instance learning is a method where you are labeling groups or collections of data, rather than the individual members of the group. This is a helpful method when you’re working with large sets of similar data and have a lot of duplicates.

This method uses supervised learning models to identify labels for groups of data. You train the models to recognize attributes of a few pieces of data within a group, and then it predicts labels for future groups based on attributes of some of the data within the new groups.

How Hybrid Machine Learning Works: Combining Algorithms for Better Results

Hybrid machine learning systems are designed to combine the strengths of different algorithms into a single, more powerful model. Rather than relying on one type of learning method, HML blends traditional machine learning techniques with deep learning strategies—or combines multiple models in a layered or parallel architecture—to achieve better accuracy, scalability, and adaptability.

Step 1: Feature Extraction with Deep Learning

Hybrid models often begin by using deep learning methods like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Transformers to extract rich, high-dimensional features from complex data such as images, text, or time series. These models excel at recognizing patterns and learning abstract representations without the need for manual input.

Step 2: Analysis with Traditional Machine Learning

Once the features are extracted, traditional machine learning algorithms—such as Decision Trees, Random Forests, or Support Vector Machines (SVMs)—can be used to analyze those features for classification, regression, or clustering tasks. These models are often more interpretable and computationally efficient, which makes them ideal for business scenarios that require transparency and rapid insights.

Step 3: Fusion Strategies to Optimize Performance

Fusion strategies like model stacking, ensemble learning, or attention mechanisms are used to combine the outputs of both deep learning and traditional machine learning components. These strategies help refine the final prediction by leveraging the strengths of each model type. For example, an ensemble might average multiple model outputs to reduce variance, while stacking uses a meta-model to learn how best to combine the predictions.

Step 4: Data Preprocessing and Dimensionality Reduction

In some hybrid ML architectures, traditional algorithms also play a key role early in the pipeline by performing data cleaning, feature selection, or dimensionality reduction (e.g., using PCA or clustering methods). This ensures the input data is optimized before being processed by more resource-intensive deep learning models.

Benefits of the Hybrid Approach

By combining multiple algorithm types, HML offers several advantages over single-model solutions:

Higher Predictive Accuracy

Each component model in a hybrid machine learning (HML) system focuses on specific aspects of the data, combining their strengths to deliver more reliable and accurate predictions. For example, traditional machine learning can handle structured data effectively, while deep learning excels at unstructured data like images and text. By leveraging the strengths of both, HML systems significantly improve predictive performance across diverse datasets.

Greater Robustness

HML systems are designed to handle a wide variety of data types and tasks, making them highly adaptable. Whether dealing with structured numerical data, unstructured text, or multimedia content, these systems can integrate multiple approaches to ensure consistent results, even in changing conditions or when working with incomplete data.

Improved Interpretability

Traditional machine learning components in HML systems provide valuable transparency, helping to demystify the decision-making processes of more complex deep learning models. For instance, decision trees or linear regression models can complement deep learning by offering clear, step-by-step insights into specific patterns, making it easier for stakeholders to understand results and identify potential biases.

Scalability and Flexibility

Hybrid models are designed to operate seamlessly across multiple environments, from on-premise systems to cloud platforms. This flexibility allows organizations to scale their operations efficiently, meeting growing demands without compromising performance. With the ability to integrate with modern cloud infrastructure, HML systems also support real-time processing and deployment across a range of industries and use cases.

Tools to support hybrid machine learning

The point of incorporating ML into your data science and analytics processes is to allow you to begin looking forward with your data. Rather than relying on datasets that have been cleaned and organized, you’ll be able to quickly group and label data in real-time for the most accurate analysis and forecasts.

When considering how to manage this data, you’ll need a few key features in your business intelligence tools to support this type of advanced analysis. Your tool will need to support:

Integration from all your data sources
You’ll need one place to manage your data and train your ML models. Find a tool that will allow for easy integration of all your data sources.

Real-time analysis
Many of the HML models mentioned here function best as they’re learning from new data. Find tools that will support real-time ingestion and analysis, and then will push that data out to workers who can use it to improve performance right then.

Automatic decisions
Find a tool that will support automatic decisions for your team, with alerts and notifications for when your data passes specific thresholds.

No matter your industry, your data will continue to play an increasingly important role in how you do business. Incorporating hybrid-machine learning techniques will be one of the best ways you’ll be able to create tools that will allow you to get value from your data now and as your business grows.