It runs factories, controls transportation networks, and defines the way we interact with personal devices. It is estimated that somewhere in the neighborhood of 2.8 trillion lines of code have been written over the last two decades alone.a Yet it is easy to overlook a basic fact: people have to write software--and that is often a long, tedious, and error-prone process. Although low-code and no-code environments have simplified things--and even allowed non-data scientists to build software through drag-and-drop interfaces--they still require considerable time and effort. Over the last several years, various systems and frameworks have appeared that can automate code generation.

Machine learning (ML) and artificial intelligence (AI) were once concepts relegated to only the most optimistic observers, much like self-driving electric vehicles and smartphones once were. But if it isn't obvious, the times have changed. Today, ML and AI--along with the immensely powerful data collection and analytics tools that power those processes--are a mainstay of modern life. Every day, people interact with products and services powered by some of the world's most ground-breaking technology. In the financial sector specifically, ML and AI present an enormous opportunity to institutions to revolutionise their businesses and generate both top- and bottom-line results.

The accepted best practices for formulating and validating assumptions that we would hear from experts were not written down anywhere. So, my collaborator Emre Kiciman and I thought: Why not create a library for Causal Inference that enabled these best practices for everyone?

If you came here looking for an introduction to water, or a synopsis of the 2003 TV series about teenage mermaids you have sadly come to the wrong place. The H2O that we will talk about is H2O.ai, a company which develops products for easy, scalable, machine learning and artificial intelligence. Machine learning and artificial intelligence (or AI for short) are topics which have had a lot of interest over the past 4-5 years. Some of this interest has come from businesses as they begin to utilise the information they collect on a day-to-day basis to streamline/automate processes or gain insight. A lot of companies are now looking to hire data scientists/engineers and in turn this is making a lot more people interested in machine learning and AI.

Summary: Explaining data science to a non-data scientist isn't as easy as it sounds. You may know a lot about math, tools, techniques, data, and computer architecture but the question is how do you explain this briefly without getting buried in the detail. You might try this approach. You're at a party or maybe striking up a conversation with that pretty girl at the bar and sooner or later the question comes up, "what do you do?" Since you have what is reported to be the sexiest job in the world you proudly respond "I'm a data scientist". OK, what happens next depends on exactly what you say.

Machine learning/AI capability is increasingly transforming how we engage with technology. Just look at how mainstream digital voice assistants such as Alexa and Siri, customer-service chat bots and bank-fraud detection tools have become. I see promise in healthcare using machine learning/AI technology. One example is using AI to detect breast cancer by analyzing mammograms. Leveraging the expertise of subject matter experts in healthcare is crucial to successfully applying AI to solving healthcare challenges.

A Technical Journalist who loves to take everything creatively. When not writing, you can find her drawing or painting on a canvas or traveling.

Summary: The shortage of data scientists is driving a growing number of developers to fully Automated Predictive Analytic platforms. Who are these players and what does it mean for the profession of data science? In a recent poll the question was raised "Will Data Scientists be replaced by software, and if so, when?" Data Scientists automated and unemployed by 2025. Are we really just grist for the AI mill? As part of the broader digital technology revolution we data scientists regard ourselves as part of the solution not part of the problem.

Summary: A year ago we wrote about the emergence of fully automated predictive analytic platforms including some with true One-Click Data-In Model-Out capability. We revisited the five contenders from last year with one new addition and found the automation movement continues to move forward. We also observed some players from last year have now gone in different directions. Just about a year ago we wrote the original article for which this is the update. Although it was just a year ago it seems like much longer ago that several newly emerged analytic platform vendors were touting fully automated machine learning platforms, the most extreme and interesting of which were literally One-Click Data-In-Model-Out.

Summary: Recently we've been profiling Automated Machine Learning (AML) platforms, both of the professional variety, and particularly those proprietary one-click-to-model variety that are being pitched to untrained analysts and line-of-business managers. Since our first article, readers have suggested some additional companies we should look at which are profiled here along with some interesting observations about who is buying and why. Recently we've written a series of articles on Automated Machine Learning (AML) which are platforms or packages designed to take over the most repetitive elements of preparing predictive models. Typically these cover cleaning, preprocessing, some feature engineering, feature selection, and then model creation using one or several algorithms including hyperparameter optimization. Most will then offer code export and an API for scoring. These are grouped into two major schools.