How automated machine learning will help your business

Machine learning underpins all AI projects. But creating machine learning models is hard. You either have to employ data scientists, find a top AI consulting firm, or use automated machine learning.

Machine learning (ML) is the process of getting a computer to learn to recognize patterns in data. It underpins almost all forms of artificial intelligence. There are three families of machine learning: supervised learning, unsupervised learning, and reinforcement learning. We discussed these in detail in another blog.

Machine learning models can be used for many purposes. Here are just a few of the more common applications:

Forecasting . Predict future events based on present and historical trends. This can be used for things like forecasting electricity demand, predicting sales volumes, or supply chain planning.

. Predict future events based on present and historical trends. This can be used for things like forecasting electricity demand, predicting sales volumes, or supply chain planning. Anomaly detection . This is about identifying unexpected features in images or other data. It can be used for predictive maintenance of servers or industrial machinery, identifying abnormalities in mammograms, or spotting fraudulent transactions.

. This is about identifying unexpected features in images or other data. It can be used for predictive maintenance of servers or industrial machinery, identifying abnormalities in mammograms, or spotting fraudulent transactions. Knowledge discovery. Here, you get the model to parse and understand documents to extract meaning. It can be used for patent searching and discovery, eDiscovery for civil litigation or as the basis for an automated knowledgebase.

However, there are a number of challenges to solve for any machine learning problem. Getting it right is far from easy. This means it can be better to use automated machine learning or even a top AI consulting firm.

Why creating ML models is so hard

Creating any machine learning model is challenging. The process is complex and there are many opportunities to get things wrong. Even worse, some of the problems are so subtle that you may not realize they exist. Let’s look at the main issues you face.

Data selection

All machine learning approaches need data. For supervised learning, you need to start with a large collection of labeled data. That is data where you already know the “correct” answer. Even for unsupervised or reinforcement learning, the more data you have, the better your model. But there are two issues with data selection. Firstly, how do you decide what data is relevant (data selection)? And secondly, how do you get that data into a form where it can be used (data cleaning and preprocessing).

Model selection

Having found (and potentially labeled) your data, your next challenge is to choose an ML approach and select a model. Machine learning is a very active field of research. As a result, there are thousands of different approaches and models available. Selecting the correct one is often a combination of knowledge and gut instinct.

Model quality

Having selected and trained your model, you then face a number of issues with model quality. These include ensuring the outputs are robust and repeatable, avoiding overfitting (where the model is effectively overtrained), and testing that the model actually produces meaningful results.

Using the model

Having created a model, you then need to actually work out how to use it practically. This involves embedding it into your processes, ensuring you can feed it with all the data it needs, etc. You may even need to chain more than one model together to solve a particular uses case. This is especially true for things like intelligent chatbots.

Bias

The final big issue facing all machine learning is bias. There are many sources of bias in ML, and in all cases, they impact the utility and accuracy of the model. The most common sources of bias are inherent bias in the data used to train the model and unintentional bias during the data selection and cleaning.

How automated machine learning can help

Automated machine learning makes it easier to create ML models in several ways. Let’s use our Sonasoft NuGene engine as an example. NuGene is a unified AI platform based on automated machine learning.

Data selection. NuGene takes raw data in any form (numerical, visual, audio, time series, etc.). Rather than selecting data, the more data you provide, the better NuGene can solve your problem. It autonomously searches your data for patterns, creates its own hypotheses, and tests these for causation.

Model selection. NuGene has a library of thousands of ML models for different applications. It will create and test different models until it finds one that performs well enough for what you need.

Model quality. NuGene ensures that the resulting models are robust and perform well. It checks their quality and robustness, ensuring that you can trust the output.

A confusion matrix helps you assess the model’s quality.

Using the model. One of the most powerful aspects of NuGene is that it makes it easy to package the model into a working automation bot. Indeed, it makes it so easy that we refer to NuGene as a bot factory. This is something that many other automated machine learning approaches are unable to do. Because NuGene takes all your raw data, it eliminates unintentional bias that a data scientist might introduce during data cleaning and pre-processing. However, no one can address bias in the underlying data without actively altering that data.

When should you turn to AI consulting?

Sometimes, automated machine learning can’t solve your use case fully. Typically, this happens when you have legacy data or if you need to use multiple ML models to solve your problem. This is when it pays to bring in experts. A top AI consulting firm will be able to help you collect together all your legacy data and transform it into a usable form. They can move the data into the cloud so that ML can be done more efficiently. They can help create complex bots that chain together multiple different models to create a robust solution. Here at Sonasoft, we employ a huge team of data scientists and data engineers that can help you with all this.

You may be wondering what can be achieved if you combine multiple ML models into a single product. One classic example is virtual assistants like Amazon’s Alexa. These rely on a combination of 3 or 4 different types of ML models. Another example is Sonsaoft AURA. AURA is a suite of intelligent bots designed to help augment and improve any customer or HR support system. It does this in many ways. For instance, it uses forecasting to help predict demand. AURA also has knowledge discovery capabilities, which allow it to suggest relevant solutions without human assistance. It can even preempt a user issue and provide contextual help to prevent them from raising a ticket.

What does all this mean for your business?

AI is often touted as a transformative technology. It promised huge improvements in efficiency, significant ROI, and even opens up completely new business opportunities. However, the complexity of creating and using ML models often prevents businesses from benefiting. But using automated machine learning and AI consulting can help any business to reap the rewards of AI. Speak to us if you would like to learn more about how we help companies from multinationals to fledgling startups to benefit from AI.