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July 14, 2020
There is growing consensus from data, digital, and analytics leaders that automated machine learning (AutoML) is a critical enabling technology for enterprise analytics. The arrival of AI automation, notably feature engineering automation, has been a game-changer. It has enabled BI, analytics, and data science professionals to quickly build predictive analytics applications. By providing automated feature engineering, model generation, and deployment with a transparent workflow, AutoML and FE automation has brought AI to the masses, thereby accelerating data science adoption. In this blog, we review details about feature engineering automation and its impact on the business.
What is Feature Engineering?
Feature engineering (FE) is the process of applying domain knowledge to extract analytical representations from raw data, making it ready for machine learning. It involves using business knowledge, mathematics, and statistics to transform data into a format that machine learning models can directly consume. It starts from many tables spread across disparate databases, then joined, aggregated, and combined into a single flat table using statistical transformations and relational operations. To implement FE, you need to write hundreds or even thousands of SQL-like queries, performing a lot of data manipulation, as well as a multitude of statistical transformations.
Enterprise data to ML ready data using AI-powered Feature Engineering
Why is Feature Engineering Important?
FE is about extracting the business hypothesis from historical data. Based on the hypothesis, you can predict the likely outcome. That’s why hypothesis-driven feature creation is critical to get refined models. If you don’t have relevant features, you cannot train a model no matter which algorithm you use. As AI expert Andrew Ng says, “Coming up with features is difficult, time-consuming, and requires expert knowledge. Applied machine learning is basically feature engineering”.
FE is critical because if you provide the wrong hypotheses as an input, ML cannot make accurate predictions. The quality of any provided hypothesis is vital for the success of an ML model. Also, from an accuracy and interpretability perspective, the quality of features is critically important. FE is the most iterative, time-consuming, and resource-intensive process involving interdisciplinary expertise. It requires technical knowledge but, more importantly, domain knowledge. The data science team builds features by working with domain experts, testing hypotheses, creating and evaluating ML models, and repeating the process until the results become acceptable for businesses.
Automated Feature Engineering
One of the most pressing challenges that organizations face is the sheer length of the AI and ML project, which usually takes months to complete, and the incredible lack of qualified talent available to handle it. While AutoML products have undoubtedly made significant inroads in accelerating the AI and machine learning process, they fail to address the most critical step: preparing the input of machine learning from raw business data or features.
ML models can be only as good as input data which is called a feature table. To develop great ML models, you need great features. With AutoFE, the AI engine automatically hypothesizes, transforms, and validates features and prepares AI features. Data scientists can plug AI-generated features into features that they have developed and dramatically save time.
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dotData’s Automated Feature Engineering
Auto FE Benefits
Automated FE brings a host of benefits depending on the end-user. For experienced data scientists, it dramatically accelerates the speed of analysis with AI features. First, it allows data scientists to quickly test new datasets without writing complex feature queries upfront. This makes the traditional trial-and-error process of building an ML model much faster.
Second, AI features expand the feature space and improve model accuracy. The only principled way to enhance an ML model is to leverage more data and more features. With Automated FE, one can quickly explore ten times more data and 100 times more features. It complements the ability to scan much more datasets and analyze broader feature hypotheses. Using Auto FE, you often find unknown and interesting patterns that you have never experimented with.
If you are intrigued by Auto FE and wondering which product is right for your business, you have a choice. dotData Py offers data scientists automated feature engineering integrated with Python workflow. For your Python data science team, dotData Py directly augments and enhances your ML workflow. If you have an analytics team consisting primarily of citizen data scientists, you need end-to-end data science automation. In this case, dotData Enterprise is the right solution for your predictive analytics needs. dotData Enterprise is GUI-based end-to-end ML automation combining AutoFE and AutoML. If you want to leverage your BI team to do ML projects, dotData Enterprise provides no-coding automation experience from data through feature engineering to ML.
Learn more about dotData Py or dotData Enterprise.
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