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.

Within HML there are various types of techniques that interact with the data in different ways. Which technique you use depends on the problems you’re trying to solve, the technical expertise available, and the tools you’re using. 

Here are some types of hybrid machine learning.

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. 

In business, it is common to be in situations without a lot of labeled data. Semi-supervised learning reduces the upfront costs and burdens with organizing that data and allows you to work with a dynamic dataset and start working much faster. 

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.

This HML method is trained using supervised learning and applied to problems that are generally solved with unsupervised learning. It’s helpful for analyzing things like photos, that will have a lot of context about the image that may not be initially machine readable. However, it is important to only use it in use cases where unsupervised learning is helpful. Identifying features about the image—like color, size, or orientation—can be performed with unsupervised models. However, it won’t be effective at identifying data contained within the images, such as what the picture is about.

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.

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.

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