What is AutoML?
Auto Machine Learning or AutoML enables developers with limited machine learning expertise to train high-quality models specific to their business needs. Build your own custom machine learning model in minutes. Automated Machine Learning (AutoML) is tied in with producing Machine Learning solutions for the data scientist without doing unlimited inquiries on data preparation, model selection, model hyper-parameters, and model compression parameters.
How does the AutoML process work?
Auto Machine Learning is typically a platform or open source library that simplifies each step in the machine learning process, from handling a raw data-set 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 maps 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 or R. These are some standard 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
Why is AutoML a game changer?
I think AutoML is game changer because it represents a milestone in the fields of machine learning and artificial intelligence (AI). AI and machine learning have been subject to the "black box" criticism -- meaning that machine learning algorithms can be difficult to reverse engineer. Although they improve efficiency and processing power to produce results, it can be difficult to track how the algorithm delivered that output. Consequently, this also makes it difficult to choose the correct model for a given problem, because it can be difficult to predict a result if a model is a black box.
AutoML makes machine learning less of a black box by making it more accessible. This process automates parts of the machine learning process that apply the algorithm to real-world scenarios. A human performing this task would need an understanding of the algorithm's internal logic and how it relates to the real-world scenarios. It learns about learning and makes choices that would be too time-consuming or resource-intensive for humans to do with efficiency at scale.
Fine-tuning the end-to-end machine learning process -- or machine learning pipeline -- through meta learning has been made possible by AutoML. We can say AutoML represents a step towards general AI and making AI accessible for non techy domain experts.
Getting started with Auto ML
You can get started by trying some popular AutoML platforms like :
- Google AutoML - Google's proprietary, cloud-based automated machine learning platform.
- Azure Automated Machine Learning - a proprietary, cloud-based platform.
- Auto Keras - an open-source software library developed by the DATA lab at Texas A&M university.
- Auto-sklearn, - evolves from Scikit learn, which was an open source, commercially usable collection of simple machine learning tools in Python. You can find it on GitHub.
Would AutoML replace Data Scientist?
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 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. Will AutoML reduce dependency of business on data scientist? Yes to a limit it will reduce the dependency on data scientist to do menial machine learning tasks but it also helps in enabling domain experts to use machine learning and applying across less complex tasks.
As of 2021 Auto Machine Learning is a relatively developing area and even the most popular tools are not yet fully developed. If you look back at history of software it is inevitable for automated tools to evolve and automate the mundane tasks reducing dependency on developers. Those who have entered the field of data science without programming background should be wary that one day Machine Learning will become a packaged software and the demand for ML developers my prediction is the demand for ML developers would reduce in another 3 to 4 years maybe around 2025. For every developer/data scientist entering the data science or machine learning space my advice would be to build a strong programming base along with machine learning and AI which will enable them to adapt to the inevitable change in demand of the IT industry.
