Automated Machine Learning vs. Manual Machine Learning

As a data analyst or a data-driven business, you are already aware of the two popular approaches to machine learning- manual and automated. While manual machine learning has existed for a while, automated machine learning is now getting a lot of attention. The question is whether automated machine learning is better than manual machine learning, or is manual machine learning still relevant.

In this blog post, we will break down the differences between the two methods and analyze the pros and cons to help you make an informed decision.

What is Manual Machine Learning?

Manual machine learning involves data analysts manually selecting and optimizing algorithms and models to help recognize the patterns within the data. This method is prevalent among experienced data analysts as it requires coding skills and expertise in machine learning algorithms. Manual machine learning can be time-consuming, and the optimal algorithms and models can be difficult to find without a large amount of experimentation.

However, this approach offers complete control over data, models, and algorithms, allowing data analysts to create tailor-made solutions that are unique to their business needs.

What is Automated Machine Learning?

Automated Machine Learning or AutoML is the process of automating the entire process of creating a machine learning model.This approach uses sophisticated algorithms and machine learning modeling frameworks to automate the process of selecting and optimizing the models. AutoML allows data analysts with limited machine learning expertise to build models without the need to write complex code.

AutoML platforms use different techniques such as Bayesian optimization and genetic algorithms to identify the ideal model and tune its hyperparameters. This approach promises to reduce the amount of time required to find a suitable model and improve accuracy by taking advantage of powerful computing resources and automation.

Automated Machine Learning vs Manual Machine Learning: Pros and Cons

Accuracy

One of the primary considerations when selecting a machine learning method is the accuracy of the models. It is commonly believed that automated machine learning yields higher accuracy than manual machine learning. One study conducted by Google shows that AutoML outperformed the best human-designed model on the CIFAR-10 dataset. However, it is important to note that the accuracy of both methods depends majorly on the size and quality of the dataset.

Time and Cost

Automated machine learning takes less time than manual machine learning, and AutoML models can be produced at a lower cost primarily because of the reduced costs associated with having a smaller team. A study by DataRobot reported that AutoML, on average, took only two days to produce a model that was 12% more accurate, while manual machine learning took an average of 3-4 weeks. For organizations that require several models to be designed and implemented, automation can greatly save both time and costs.

Interpretability

The manual machine learning approach provides more transparency and interpretability than AutoML models. This is because, for AutoML models, the complex algorithms used to select and optimize the models make it difficult to directly understand how the final model was reached. On the other hand, manual machine learning allows data analysts to have complete control over the model and the algorithms applied, resulting in a model that can be better understood.

Conclusion

So, is Automated Machine Learning better than Manual Machine Learning? The answer is it depends. Automated Machine Learning is perfect for companies with limited data science resources that require quick and easy-to-understand models due to its speed and accuracy. It can also be used in circumstances where the data is relatively complicated, and trained professionals have a lower degree of understanding of the data in question. On the other hand, if you need transparency, interpretability, and specific functionality, manual machine learning may be the best option.

To maximize your business's data analytics performance, you need to choose the machine learning approach that best meets your needs, requirements, data quality, dataset size, and budget.

References

1. Delgado, M., & Escalera, S. (2019). A review of automatic machine learning. Neural Computing and Applications, 31(1), 35-50.
2. Feurer, M., Klein, A., Eggensperger, K., Springenberg, J. T., Blum, M., & Hutter, F. (2015). Efficient and robust automated machine learning. Advances in neural information processing systems, 28, 2962-2970.
3. Google. (2018). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Retrieved from https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html
4. Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014). Learning and transferring mid-level image representations using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1717-1724).
5. Roberts, D., & Bouzid, M. (2018). Is Automated Machine Learning the future of diagnostic modelling?. Diagnostics, 8(1), 13.
6. Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press.
7. DataRobot. (2019). Automated Machine Learning Drives Model Accuracy While Saving Time and Costs. Retrieved from https://www.datarobot.com/wp-content/uploads/2019/10/Automated-Machine-Learning-Drives-Model-Accuracy-While-Saving-Time-and-Costs.pdf