From 62155dfa62bc83674f4b34ee0f8299940e6311ed Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 27 Jul 2016 10:01:27 -0700 Subject: Fix a few bugs in the release notes. --- releases/_posts/2016-07-26-spark-release-2-0-0.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) (limited to 'releases/_posts') diff --git a/releases/_posts/2016-07-26-spark-release-2-0-0.md b/releases/_posts/2016-07-26-spark-release-2-0-0.md index 1cc5cddd9..ab19aa389 100644 --- a/releases/_posts/2016-07-26-spark-release-2-0-0.md +++ b/releases/_posts/2016-07-26-spark-release-2-0-0.md @@ -73,16 +73,16 @@ In addition, when building without Hive support, Spark SQL should have almost al ### MLlib -The DataFrame-based API is now the primary API. The RDD-based API is entering maintenance mode. See the MLlib guide for details +The DataFrame-based API is now the primary API. The RDD-based API is entering maintenance mode. See the [MLlib guide](http://spark.apache.org/docs/2.0.0/ml-guide.html) for details #### New features -- ML persistence: The DataFrames-based API provides near-complete support for saving and loading ML models and Pipelines in Scala, Java, Python, and R. See this blog post for details. (SPARK-6725, SPARK-11939, SPARK-14311) -- MLlib in R: SparkR now offers MLlib APIs for generalized linear models, naive Bayes, k-means clustering, and survival regression. See this talk to learn more. +- ML persistence: The DataFrames-based API provides near-complete support for saving and loading ML models and Pipelines in Scala, Java, Python, and R. See this [blog post](https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html) and the following JIRAs for details: SPARK-6725, SPARK-11939, SPARK-14311. +- MLlib in R: SparkR now offers MLlib APIs for generalized linear models, naive Bayes, k-means clustering, and survival regression. See [this talk](https://spark-summit.org/2016/events/recent-developments-in-sparkr-for-advanced-analytics/) to learn more. - Python: PySpark now offers many more MLlib algorithms, including LDA, Gaussian Mixture Model, Generalized Linear Regression, and more. - Algorithms added to DataFrames-based API: Bisecting K-Means clustering, Gaussian Mixture Model, MaxAbsScaler feature transformer. -This talk lists many of these new features. +[This talk](https://spark-summit.org/2016/events/apache-spark-mllib-20-preview-data-science-and-production/) lists many of these new features. #### Speed/scaling Vectors and Matrices stored in DataFrames now use much more efficient serialization, reducing overhead in calling MLlib algorithms. (SPARK-14850) -- cgit v1.2.3