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What Does the Future of Machine Learning Look Like?


With machine learning now being behind many technologies, from Netflix’s recommendation algorithm to self-driving cars, it’s time for businesses to start taking a closer look. In this article, we will discuss the future of machine learning and its value throughout industries.

Machine learning solutions continue to incorporate changes into businesses’ core processes and are becoming more prevalent in our daily lives. The global machine learning market is predicted to grow from $8.43 billion in 2019 to $117.19 billion by 2027.

Despite being a trending topic, the term ‘machine learning’ is often used interchangeably with the concept of artificial intelligence. In fact, machine learning is a subfield of artificial intelligence based on algorithms that can learn from data and make decisions with minimal or no human intervention.

Many companies have already begun using machine learning algorithms due to their potential to make more accurate predictions and business decisions. In 2020, $3.1 billion in funding was raised for machine learning companies. Machine learning has the power to bring transformative changes across industries.

With machine learning being so prominent in our lives today, it’s hard to imagine a future without it. Here are our predictions for the development of machine learning in 2021 and beyond.

Quantum computing can define the future of machine learning

Quantum computing is one advancement that has the potential to boost machine learning capabilities. Quantum computing allows the performance of simultaneous multi-state operations, enabling faster data processing. In 2019, Google’s quantum processor performed a task in 200 seconds that would take the world’s best supercomputer 10,000 years to complete.

Quantum machine learning can improve the analysis of data and obtain more profound insights. Such increased performance can help businesses to get better results than via more traditional machine learning methods.

So far, there is no commercially ready quantum computer available. However, a handful of big tech companies are investing in technology, and the rise of quantum machine learning is not that far off.

AutoML will facilitate the end-to-end model development process

Automated machine learning or AutoML is automating the process of applying machine learning algorithms to complete real-life tasks. AutoML simplifies the process so that a person or business can apply complex machine learning models and techniques without being an expert in machine learning.

AutoML enables a wider audience to use machine learning, which indicates its potential to change the technology landscape. A data scientist can use AutoML to their benefit, for instance, to quickly find the algorithms they can use or whether any algorithms have been missed. Here are some stages of development and deployment of a machine learning model that AutoML can automate:

  • Data pre-processing – improve data quality, transform unstructured data into structured data with the help of data cleaning, data transformation and data reduction, etc.
  • Feature engineering – use automation with machine learning algorithms to help create more adaptable features based on the input data.
  • Feature extraction – use different features or datasets to produce new features that will improve results and reduce the size of data processed.
  • Feature selection – choose only useful features for processing.
  • Algorithm selection and hyperparameter optimisation – automatically choose the best possible hyperparameters and algorithms.
  • Model deployment and monitoring – deploy a model based on the framework and monitor the condition of the model via dashboards.

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