Insights on how AutoML will effect a Data Scientist job

Automated Machine Learning (AutoML) helps in automating some critical components of the machine learning pipeline. This machine learning pipeline consists of data understanding, data engineering, feature engineering, model training, hyperparameter tuning, model monitoring, etc.

In this article, In what part of the pipeline AutoML plays a role, and how a data scientist job will not be affected because of it, instead, they can use AutoML to their favor to accelerate their work.

What is AutoML, and will it affect a Data Scientist Job?

AutoML is used to automate some parts of the machine learning pipeline. Some of the available AutoML tools are TPOT, AutoKeras, AutoViML, etc. Some parts of the machine learning pipeline are repetitive and time-consuming. These activities can be automated using an AutoML library. AutoML can not replace a data scientist in the future, instead, it will assist a data scientist to optimize its work.

The entire end to end machine learning project comprises 4 aspects:

• Data Collection
• Data Preparation
• Modeling
• Deployment

In an end-to-end machine learning project, the complexity of each aspect of the pipeline is dependent on the project. Generally, most of the time is spent on and data preparation and modeling.

Some of the popular available AutoML tools are:

and many more.

AutoML can help make AI an affordable process

AutoML for automating Modeling pipeline:

AutoML helps to automate the modeling training and hyperparameter tuning to get the best performing model. It trains various ML algorithms such as SVM, Logistic Regression, Ensemble models, etc on the data and returns the best model. Every model has its own set of hyperparameters. AutoML tunes the parameters of every model and returns the best model with its best performing set of hyperparameters.

AutoML tools also train advanced deep learning with the best set of hyperparameters such as number of layers, number of neurons, etc. There are plenty of parameters for a deep learning model, thus the AutoML framework can accelerate a data scientist’s work and return the best deep learning model in comparatively less time.

AutoML for automating Data preparation pipeline:

Data preparation and data analysis requires a lot of time and is an important aspect of an end-to-end model development pipeline. Some AutoML frameworks can also automate data cleaning, feature selection, and other data preprocessing, to accelerate the work of a data scientist.

Auto ViML is an AutoML framework that does data preprocessing, such as handling missing values, feature engineering, feature encoding, prior to the model selection, and hyperparameter optimization pipeline.

An AutoML can never replace a data scientist job, instead, it can serve as a tool to accelerate a data scientist’s work. A lot of time is wasted during the model and its set of hyperparameter selection, which can be optimized using an AutoML framework.

AutoML can do a minimal amount of data preprocessing, as it requires a lot of domain knowledge.

[1] Automated Machine Learning — Wikipedia [15 Dec 2020]: https://en.wikipedia.org/wiki/Automated_machine_learning

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