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| author | Reynold Xin <rxin@databricks.com> | 2016-07-27 10:01:27 -0700 |
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| committer | Reynold Xin <rxin@databricks.com> | 2016-07-27 10:01:27 -0700 |
| commit | 62155dfa62bc83674f4b34ee0f8299940e6311ed (patch) | |
| tree | 8343ee6f15a195bb374666db0c608f9f807fc808 /releases/_posts/2016-07-26-spark-release-2-0-0.md | |
| parent | 46fb65a409296036cd5ffcb153d2a24e9f229323 (diff) | |
| download | spark-website-62155dfa62bc83674f4b34ee0f8299940e6311ed.tar.gz spark-website-62155dfa62bc83674f4b34ee0f8299940e6311ed.tar.bz2 spark-website-62155dfa62bc83674f4b34ee0f8299940e6311ed.zip | |
Fix a few bugs in the release notes.
Diffstat (limited to 'releases/_posts/2016-07-26-spark-release-2-0-0.md')
| -rw-r--r-- | releases/_posts/2016-07-26-spark-release-2-0-0.md | 8 |
1 files changed, 4 insertions, 4 deletions
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) |