Machine Learning

Automation in Machine Learning

Automation in Machine Learning

What is automation?

Automation is a term for technology applications where human input is minimized. This includes business process automation, IT automation, personal applications such as home automation, and more.

What is Automated machine learning?

Automated machine learning, it is also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.

Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. With automated machine learning, you’ll accelerate the time it takes to get production-ready ML models with great ease and efficiency.

Why is automated machine learning important?

Manually constructing a machine learning model is a multistep process that requires domain knowledge, mathematical expertise, and computer science skills – which is a lot to ask of one company, let alone, one data scientist. Not only that, there are countless opportunities for human error and bias, which degrades model accuracy and devalues the insights you might get from the model. Automated machine learning enables organizations to use the baked-in knowledge of data scientists without expending time and money to develop the capabilities themselves, simultaneously improving return on investment in data science initiatives and reducing the amount of time it takes to capture value.

Automated machine learning makes it possible for businesses in every industry – healthcare, financial markets, fintech, banking, the public sector, marketing, retail, sports, manufacturing,
and more – to leverage machine learning and AI technology — technology previously only available to organizations with vast resources at their disposal. By automating most of the modeling tasks necessary in order to develop and deploy machine learning models, automated machine learning enables business users to implement machine learning solutions with ease, thereby allowing an organization’s data scientists to focus on more complex problems.

How does AutoML work?

AutoML is typically a platform or open-source library that simplifies each step in the machine learning process, from handling a raw dataset to deploying a practical machine learning model. In traditional machine learning, models are developed by hand, and each step in the process must be handled separately.

AutoML automatically locates and uses the optimal type of machine learning algorithm for a given task. It does this with two concepts:

  • Neural architecture search, which automates the design of neural networks. This helps AutoML models discover new architectures for problems that require them.
  • Transfer learning, in which pretrained models apply what they’ve learned to new data sets. Transfer learning helps AutoML apply existing architectures to new problems that require it.

Users with minimal machine learning and deep learning knowledge can then interface with the models through a relatively simple coding language like Python.

More specifically, here are some of the steps of the machine learning process that AutoML can automate, in the order, they occur in the process:

  • Raw data processing
  • Feature engineering and feature selection
  • Model selection
  • Hyperparameter optimization and parameter optimization
  • Deployment with consideration for business and technology constraints
  • Evaluation metric selection
  • Monitoring and problem checking
  • Analysis of results

Uses of AutoML

  • Fraud detection in finance. It can improve the accuracy and precision of fraud detection models.
  • Research and development in healthcare, where it can analyze large data sets and draw insights.
  • Image recognition, which is useful for facial recognition.
  • Risk assessment and management in banking, finance, and insurance.
  • Cybersecurity, where it can be used for risk assessment, monitoring, and testing.
  • Customer support can be used for sentiment analysis in chatbots and to increase the efficiency of the customer support team.
  • Malware and spam, where it can be used to generate adaptive cyberthreats.
  • Agriculture, where it can be used to expedite the quality testing process.
  • Marketing, where it can be used for predictive analytics and improved engagement rates. It can also be used to improve the efficiency of behavioral marketing campaigns on social media.
  • Entertainment, where it can be used as a content selection engine.
  • Retail, where it can be used to improve profits and reduce waste/inventory carryover.

Pros and cons of AutoML

The main benefits of AutoML are:

  • Efficiency — It speeds up and simplifies the machine learning process and reduces the training time of machine learning models.
  • Cost savings — Having a faster, more efficient machine learning process means a company can save money by devoting less of its budget to maintaining that process.
  • Accessibility — Having a simpler process allows companies to save money on training staff or hiring experts. It also makes machine learning a viable possibility for a wider range of companies.
  • Performance — AutoML algorithms also tend to be more efficient than hand-coded models.

A main challenge of AutoML is the temptation to view it as a replacement for human knowledge. Like most automation, AutoML is designed to perform rote tasks efficiently with accuracy and precision, freeing up employees to focus on more complex or novel tasks. Things that AutoML automates, like monitoring, analysis, and problem detection, are rote tasks that are faster if automated. A human should still be involved to assess and supervise the model, but no longer needs to participate in the machine learning process step-by-step. AutoML should help improve data scientist and employee efficiency, not replace them.

Another challenge is that AutoML is a relatively new field and some of the most popular tools are not yet fully developed.

Examples

1. Saving Lives by Delivering Antiretroviral Drugs
2. Delivering Cash with ATMs
3. Online Grocery Shopping
4. Coal Mining for Fuel
5. Feeding, Milking, and Mucking Out Cows
6. Picking up Recycling in your Street
7. Driverless Cars
8. Android Home Automation
9. Painting Cars
10. Drones to Deliver your Amazon Order

About the author

Pavan Malipatil

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