From 62155dfa62bc83674f4b34ee0f8299940e6311ed Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 27 Jul 2016 10:01:27 -0700
Subject: Fix a few bugs in the release notes.

---
 site/releases/spark-release-2-0-0.html | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'site')

diff --git a/site/releases/spark-release-2-0-0.html b/site/releases/spark-release-2-0-0.html
index 66ca23bf1..83f949a21 100644
--- a/site/releases/spark-release-2-0-0.html
+++ b/site/releases/spark-release-2-0-0.html
@@ -282,13 +282,13 @@
 <h4 id="new-features-1">New features</h4>
 
 <ul>
-  <li>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)</li>
-  <li>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.</li>
+  <li>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 <a href="https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html">blog post</a> and the following JIRAs for details: SPARK-6725, SPARK-11939, SPARK-14311.</li>
+  <li>MLlib in R: SparkR now offers MLlib APIs for generalized linear models, naive Bayes, k-means clustering, and survival regression.  See <a href="https://spark-summit.org/2016/events/recent-developments-in-sparkr-for-advanced-analytics/">this talk</a> to learn more.</li>
   <li>Python: PySpark now offers many more MLlib algorithms, including LDA, Gaussian Mixture Model, Generalized Linear Regression, and more.</li>
   <li>Algorithms added to DataFrames-based API: Bisecting K-Means clustering, Gaussian Mixture Model, MaxAbsScaler feature transformer.</li>
 </ul>
 
-<p>This talk lists many of these new features.</p>
+<p><a href="https://spark-summit.org/2016/events/apache-spark-mllib-20-preview-data-science-and-production/">This talk</a> lists many of these new features.</p>
 
 <h4 id="speedscaling">Speed/scaling</h4>
 <p>Vectors and Matrices stored in DataFrames now use much more efficient serialization, reducing overhead in calling MLlib algorithms. (SPARK-14850)</p>
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