Engineering Automatic Dataset Normalization for Feature Engineering in Python A normalized, relational dataset makes it easier to perform feature engineering. Unfortunately, raw data for machine learning is often stored as a single table, which makes the normalization process tedious and time-consuming.
Leadership Featuretools Year in Review Cheers to a fun year helping data scientists and developers build better machine learning models with automated feature engineering. Here's 2018 by the numbers.
Engineering Modeling: Teaching a Machine Learning Algorithm to Deliver Business Value How to train, tune, and validate a machine learning model
Engineering Feature Engineering: What Powers Machine Learning How to Extract Features from Raw Data for Machine Learning
Engineering Prediction Engineering: How to Set Up Your Machine Learning Problem An explanation and implementation of the first step in solving problems with machine learning.
Engineering How to Create Value with Machine Learning A General-Purpose Framework for Defining and Solving Meaningful Problems in 3 Steps.
Engineering Featuretools: One year of automating feature engineering Happy birthday, Featuretools! One year ago, we open sourced Featuretools, making it available to the entire world.
Engineering Featuretools on Spark Distributed feature engineering in Featuretools with SparkApache Spark is one of the most popular technologies on the big data landscape. As a framework for distributed computing, it allows users to scale to massive
Use Cases Faster, more accurate data-based predictions lead to better business decisions A growing number of businesses are looking to leverage machine learning (ML)—and they want to do it in the most effective and efficient ways possible. Automated feature engineering gives them the opportunity
Use Cases With data, insurers predict healthcare outcomes, improve decision-making Few industries can benefit more from the ability to make predictions about future outcomes than the insurance sector. Much of the business model of being an insurer involves using models of past behavior
Engineering Scaling Featuretools with Dask How to scale automated feature engineering using parallel processingWhen a computation is prohibitively slow, the most important question to ask is: “What is the bottleneck?” Once you know the answer, the logical next
Engineering Why Automated Feature Engineering Will Change the Way You Do Machine Learning Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakageThere are few certainties in data science — libraries, tools, and algorithms constantly change as better methods
Leadership 3 steps for better feature engineering As the enterprise struggles to incorporate machine learning into data analytics processes, it is becoming clear that the biggest machine learning challenges do not stem from the algorithms at the heart of these
Leadership The real reason your company isn't benefiting from machine learning Machine learning (ML) is gaining a lot of attention in the business world because of the potential benefits: greater automation, increased efficiencies and enhanced processes—to name a few.But in many cases
Leadership What would a data scientist ask? One of the biggest challenges data scientists need to address is the creation of prediction problems from large data sets.A typical data science endeavor begins when domain experts decide to solve a
Leadership Feature Labs, Carahsoft join forces to provide public sector with automated feature engineering Today, Feature Labs and Carahsoft Technology Corp. announced a strategic partnership to include Feature Labs’ automated feature engineering capabilities within the Carahsoft portfolio.
Leadership What to do when your machine learning efforts stall out Maybe you’ve been in a meeting like this: Weeks ago, your executives posed a question to your team that had critical business implications.
Use Cases From bank fraud to retail, problem-solving with feature engineering We like to think of machine learning as this perfectly intelligent, efficient process. In reality, machine learning is still driven by humans that can take longer than enterprises would like.
Leadership How does feature engineering fit into your data science workflow? The data science ecosystem is vast. It encompasses many technologies – from those focused on cleaning and curating data sources in the most initial steps, to those performing machine learning and predictive analytics in the final step of disseminating results.
Leadership At MIT STEX event, demand for easier data science tools Some of the largest companies in banking, defense, energy, pharmaceuticals and other industries sent tech leaders to Cambridge earlier this month to help answer questions many of them have in common.
Engineering What Is Machine Learning 2.0? As the demand from businesses to leverage machine learning continues growing at an exponential rate, the current time-intensive process that heavily relies on highly-skilled ML experts won’t suffice.
Research Machine learning 2.0 - Engineering data driven AI products A paradigm shift from the current practice of creating machine learning models.
Leadership Putting Machine Learning to Work We started Feature Labs with a straightforward goal: build products that enable any organization to create and deploy machine learning solutions.In 2015 while working in the MIT Computer Science and Artificial Intelligence
Engineering Deep Feature Synthesis: How Automated Feature Engineering Works The artificial intelligence market is fueled by the potential to use data to change the world. While many organizations have already successfully adapted to this paradigm, applying machine learning to new problems is
Engineering Feature Engineering vs Feature Selection All machine learning workflows depend on feature engineering and feature selection. However, they are often erroneously equated by the data science and machine learning communities. Although they share some overlap, these two ideas
