When it comes to weather events that may affect operations, today's enterprises have great insights into the future -- thanks to satellites and advanced forecasting systems that continue to advance technologically. The same holds true for sales and revenue forecasting, as companies leverage sophisticated predictive analytics to gain a clearer view of their financial future. Now, enterprises are taking their predictive capabilities to new heights, thanks to the power of artificial intelligence applications driven by high performance computing systems. This new breed of predictive applications is a cornerstone to making better business decisions, keeping systems and equipment in top shape, understanding the movement of markets and much more. In many cases, these forward-looking applications are both predictive and prescriptive, meaning they tell you what's likely to happen and recommend steps you can take to address emerging issues and influence outcomes.

The effective use and adoption of Machine Learning requires algorithms that are not only accurate, but also understandable. To address this need, BigML now includes functionality that allows for Prediction Explanation, model-independent explanations of classification and regression predictions. In this post, we will summarize what it means for a prediction to be "explainable," why this is important, and share a use case in which prediction explanation plays a key role. Rather than being hard-programmed with an exhaustive set of "if-then" rules, Machine Learning algorithms "learn" rules based on large datasets of examples. Understanding what these rules are, and how they are applied to new data, is generally referred to as the interpretability or explanation of the model.

The effective use and adoption of machine learning requires algorithms that are not only accurate but also understandable. To address this need, BigML now includes functionality that allows for prediction explanation, model-independent explanations of classification, and regression predictions. In this post, we will summarize what it means for a prediction to be explainable, why this is important, and share a use case in which prediction explanation plays a key role. Rather than being hard-programmed with an exhaustive set of "if-then" rules, machine learning algorithms "learn" rules based on large datasets of examples. Understanding what these rules are and how they are applied to new data is generally referred to as the interpretability or explanation of the model.

As happens when boundless potential meets hard reality, enterprises now face a long, painful slog through the trenches of disillusionment and disappointment as they pursue the business transformation promised by Machine Learning for the Enterprise. The machine learning hype cycle is in overdrive, inflating expectations for magically easy and automated solutions to complex business problems decades in the making. Machine Learning learns from, of course, data, with algorithms continually improving themselves. But typical enterprise data is siloed and dirty, noisy and disparate – the natural byproduct of decades of idiosyncratic business process automation initiatives and M&A activity. So when those machines try to learn from stinky, messy data, how smart do you really think they'll get?

Join our next free training with Kenji Lefèvre, Product Manager at Dataiku, to understand how Dataiku streamlines the deployment and production of predictive applications with Dataiku 3.0. If the timing is not convenient, register to receive the recording of the session afterwards. Kenji Lefevre is Dataiku's Product Manager, a collaborative platform to design, build, and run predictive applications from start to finish. Before joining Dataiku, Kenji worked as a freelance data scientist. He holds a PhD in mathematics on homotopical algebra and is particularly interested in the popularizing of science.

PAPIs is the premier forum for the presentation of new machine learning APIs, techniques, architectures and tools to build predictive applications. It is a community conference that brings together practitioners from industry, government and academia to present new developments, identify new needs and trends, and discuss the challenges of building real-world predictive applications. PAPIs '16 is the 3rd International conference on predictive applications and APIs, featuring 3 tracks (Technical, Business, Research) and the 1st AI Startup Battle where the jury is an AI. The audience is a mix of developers, software engineers, all-round data scientists, machine learning specialists, researchers, decision makers, managers, strategists and innovators. Previous editions took place in Sydney (PAPIs '15), Barcelona (PAPIs '14), Paris and Valencia (PAPIs Connect).

PAPIs is the premier forum for the presentation of new machine learning APIs, techniques, architectures and tools to build predictive applications. It is a community conference that brings together practitioners from industry, government and academia to present new developments, identify new needs and trends, and discuss the challenges of building real-world predictive applications. PAPIs '16 is the 3rd International Conference on Predictive APIs and Apps, featuring 3 tracks (Technical, Business, Research) and the 1st AI Startup Battle where the jury is an AI. The audience is a mix of developers, software engineers, all-round data scientists, machine learning specialists, researchers, decision makers, managers, strategists and innovators. Previous editions took place in Sydney (PAPIs '15), Barcelona (PAPIs '14), Paris and Valencia (PAPIs Connect).