Historically, machine learning algorithms and their models had to be created manually. This is changing. In recent years there has been a lot of progress increasing automation in parts of the building and deployment workflow. 

Without automating certain parts of the process, it can take an army of data scientists and software engineers to effectively build, test and deploy algorithms to a live environment. 

Using automated machine learning techniques provides the following benefits: 

  • Increased speed. Being able to automate things such as data pipelines, data validation and deployment means the algorithm can be pushed to production in a shorter timespan. 
  • Increased stability. Using tried and tested parts that have already been battle-hardened means there are fewer risks of errors happening, so your team can sleep a little easier at night and focus on other parts of the algorithm that are custom to your requirements. 
  • You can leverage data science best practices. So instead of reinventing the ML wheel, you can use a robust approach that has already been proven to work.  
  • Smaller team. It also means that you don’t need to be a hi-tech software company based in Silicon valley so other industries and companies can now leverage these solutions.

How it works

With automated machine learning, follow these five steps to start tapping into the above benefits: 

  1. Identify what the ML problem is – for example classification, forecasting or regression
  2. Specify the source for the training data
  3. Configure the parameters for the algorithm, such as model iteration, hyperparameter settings etc
  4. Perform the training 
  5. Review the results. 

Avora and automated machine learning 

Automated machine learning is at the core of what we do at Avora. We use it to allow all types of business users and organisations to solve business challenges in ways that weren’t possible before. 

As shown in the image below our anomaly detection algorithms can be deployed across most datasets very quickly, allowing us to surface insights and provide value within weeks instead of months or years as would have happened before. 

AutoML Anomaly Detection
AutoML Root Cause Analysis

Our customers have been able to answer questions using their data that they wouldn’t have had access to before, simply by implementing AutoML. It can provide huge cost savings or significant added value to certain business activities like marketing. 

Want to know more about AutoML? Get in touch.