GitHub’s plan for coding automation, TensorFlow Lattice release, and more in today’s data science news.
Introducing new features in what it called “just the start of a longterm roadmap” GitHub announced several automated coding features at its GitHub Universe conference this week. GitHub intends to leverage the data aggregated on its platform over the 10 years, and demonstrate how machine learning and data science can be applied to software development. The new tools will help developers track dependencies, keep code secure and discover new projects. Its new feature “dependency graph” provides developers insights into the projects, and suggests whether the software is up to date or still supported by a community, apart from giving detailed information on its license and security vulnerabilities.
TensorFlow team has announced the release of TensorFlow Lattice, which will ensure that your machine learning models follow the global trends, even when training data is noisy. The team said that TensorFlow Lattice is a library that implements Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow. The library includes a collection of regularizations and monotonicity constraints configurable per feature. It has a set of TensorFlow estimators for regression and classification with the most common set ups for lattice models, and includes lattices and piecewise linear calibration as layers that can be composed into custom models. TensorFlow Lattice is not an official Google product.
TensorFlow 1.4.0-rc0 has been released, as per the official announcement on the TensorFlow twitter page. Among the new features, tf.data is now part of the core TensorFlow API and several other custom transformation functions have been added. The release also resolves and fixes bugs that required attention, such as the race condition in TensorForest TreePredictionsV4Op. In TensorFlow 1.4.0, Google Cloud Storage file system and Hadoop file system support are now default build options. Changes in the API include doing away with the seldom used and unnecessary functions. The API is now subject to backwards compatibility guarantees.
Leading content distribution platform ViewLift has integrated artificial intelligence engine technology into its platform services. ViewLift Intelligence, or VLI, will use the advanced machine learning and AI algorithms to leverage its data and offer enhanced customer behavioral insights and retention tools for operators. ViewLift Intelligence can analyze user viewing behavior, content preferences, subscription packages, acquisition method, and device preferences. It could accurately predict which paying subscribers are likely to cancel subscriptions in the near future. This could help in making targeted strategies to reduce the churn rate across multiple channels.
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