What is AutoML?
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. With autoML you’ll accelerate the time it takes to get production-ready ML models with great ease and efficiency.
AutoML provides processes to build ML models available for non-ML experts, to improve efficiency of Machine Learning. As of now business relies on Data Scientist to perform the following tasks:
- Preprocess and clean the data
- Features selection
- Selecting the right model
- Optimize hyperparameters
- Postprocess machine learning models
- Analyze the results obtained.
As the complexity of these tasks is often beyond non ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call it automation of machine learning (AutoML).
What can be Automated in ML Processes?
AutoML aims to automate all of the steps of machine learning processes such as,
- Data pre-processing: This process includes improving data quality and converting unstructured, raw data to a structured format for modeling.
- Feature engineering: Feature engineering refers to the process of using domain knowledge to select and transform the most relevant variables from raw data. It also includes combining different features to generate new features that will enable more accurate results and reduce the size of data being processed.
- Feature selection: Feature selection is a process of reducing the number of input variables using a statistical feature selection based method when developing a model.
- Algorithm selection & hyperparameter optimization: This process includes finding the best model suitable for the usecase and best parameter for the model. For that a lot of experiments must be performed in order to discover what works best for a given predictive modeling task. This can feel overwhelming given the large number of data.
Since accuracy of machine learning solutions can be measured, automated systems can fine-tune data, features, algorithms and hyperparameters of algorithms to generate accurate models relying on established machine learning knowledge and trial-and-error.
Why AutoML?
There are so many challenges in building an end to end machine learning pipeline which includes data preprocessing, feature selection, model building, hyperparameter tuning, deployment, etc. But for this we need highly skilled ML experts, a long process with multiple iterations and heavy investment. With AutoML, all the above can be managed with little to none human intervention and minimal cost.
Benefits of AutoML
Bridge the skill gap — Almost all companies need skilled ML experts’ domain knowledge in various subjects like statistics, programming, deployment, for successful execution of ML projects. Finding the right ML talent is a time and resource intensive activity and most of the time, a difficult one. AutoML will automate most of the steps from a machine learning pipeline and is beneficial for non-machine learning experts to adopt ML and innovate quickly.
Best Models — With the adoption of AutoML, one can increase the efficiency of models because AutoML iterates through different models and does hyperparameter optimization resulting in high-performance models, which require plenty of time if done manually. And AutoML systems are less prone to errors than manual ML activities.
Cost efficient — Building an end-to-end machine learning is very tedious and costly. The costs include: Salaries of skilled employees. Cost of services used. Machines, whereas AutoML tools are usually more pocket friendly than all the above costs.
