Artificial intelligence and machine learning have allowed companies to learn from data and make predictions. Today, companies are experimenting with AI and machine learning algorithms to create self-learning algorithms that make predictions based on patterns and data.
Automated Machine Learning (AutoML) provides methods and processes to make Machine Learning available for non-Machine Learning experts, improve the efficiency of Machine Learning, and accelerate research on Machine Learning. This article will show four benefits of automated machine learning (AutoML).
- Increase productivity
Although, many tasks that humans can do have not been automated yet. For example, extensive medical terminology makes medical transcription hard to automate. Nevertheless, if automated, it would drastically reduce the human effort required. One way to do this is to use machine learning to help rewrite the text.
This has already been done for natural language processing tasks like image captioning. AI could also search the web for the correct information to help with diagnosis or legal research tasks.
Data scientists’ value lies in extracting actionable insights that the business can execute. AutoML takes care of repetitive tasks and lets the data scientists focus on what matters. AutoML can help prototype quickly but is not the endpoint.
- Reproducibility and transparency
Scientists and researchers, in particular, are concerned with reproducibility and transparency. When they publish their research, they want to make it reproducible. Reproducibility means you can use their techniques to recreate the same results.
Ideally, scientists want to publish their algorithms and data to verify the results. It benefits the entire scientific community if the results can be reproduced.
Using AutoML can make this possible as the same ML pipeline is applied for the whole dataset. It removes room for doubts and questions about the pipeline evaluation.
- Analyse data in less time
Data has the potential to improve everything from customer services to product development. It can also help companies find new products and services their customers want. AutoML can help companies analyse data in less time. It can help companies increase revenue by finding new products or making operations more efficient by predicting customer needs.
However, it is essential to evaluate all outcomes. AutoML tools usually contain explainability features that help understand the data and the model’s decision-making process.
- It is matured and trusted
AutoML has been around for a while, which means that both the technology and its teams have gained experience. Companies can use this experience to test the algorithms and ensure the technology works as expected.
Trust is vital for products with significant impacts, such as autonomous vehicles or medical devices. When you trust a mature technology like AutoML, it can be used confidently.
AutoML is great for most everyday use cases, and it can help diversify your data science team as it reduces the entry barrier to adopting predictive modelling. Data science teams broadly have benefitted from adding AutoML to their toolkit. AutoML pipeline limits the occurrence of risk as the operational process is transparent.
AutoML algorithms are already helping to improve life. They make you more productive, save time, and are transparent and reproducible while analysing data. You can also trust that the technology works as expected as it matures. To make the most of AutoML, you must familiarise yourself with these uses.
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