Is automated machine learning adoption growing?
As organisations around the world continue in their digital transformation journeys, top-rated technology companies such as Microsoft, Amazon, Google, and others have incorporated AutoML in their processes to enhance the effectiveness of their applications.
Automated machine learning (AutoML) is the process of applying machine learning (ML) models to real-world problems using automation. Automating the machine learning process makes it more user-friendly and often provides faster, more accurate outputs than hand-coded algorithms.
By automating a major portion of the machine learning process, the solution has changed the data science landscape. Based on forecasts from P&S Intelligence, the automated machine learning (ML) market is expected to grow from $346.2 million in 2020 to $14.8 billion by 2030.
Rising demands for personalised services is increasing ML investment
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.
Leading the market is a surge in demand for fraud detection solutions, personalised product recommendations, and predictive lead scoring.
According to P&S Intelligence, the COVID-19 pandemic has provided a significant drive to the evolution of digital business models, with many healthcare firms adopting machine-learning-powered chatbots to detect COVID-19 symptoms without human intervention.
Among other key findings, P&S Intelligence stated that the service segment would exhibit the fastest growth in coming years. The increase is attributed to the rising demand for implementation and integration services, consulting, and maintenance services, which aid in enhancing business productivity and coding proficiency.
What are some advantages of AutoML?
- Improve efficiency by automatically running repetitive tasks. Time is money, and the more effectively you can use your machine learning resources, the better. This can allow data scientists to focus more on problems instead of models.
- Automated ML pipelines also help avoid potential errors caused by manual work.
- AutoML can also add value to your company by allowing you to scale your use of AI
- Make predictions on new/unseen data
- Save/load a model for future use
One of the downsides of having ML models making automated decisions on a daily basis is that it makes it difficult to determine who is accountable for these decisions. In regulated industries like Financial Services or Healthcare, accountability is key. Complex data types can also be a struggle when it comes to AutoML. Data is one of the most valuable commodities today, but not all data is equal. Data comes in different shapes and sizes, and the ability to extract patterns from it heavily depends on its format and complexity.