svn commit: r1831364 - /ignite/site/trunk/features/machinelearning.html

[dm...@apache.org]

Author: dmagda
Date: Thu May 10 17:54:58 2018
New Revision: 1831364

URL: http://svn.apache.org/viewvc?rev=1831364&view=rev
Log:
updated ML page

Modified:
    ignite/site/trunk/features/machinelearning.html

Modified: ignite/site/trunk/features/machinelearning.html
URL: http://svn.apache.org/viewvc/ignite/site/trunk/features/machinelearning.html?rev=1831364&r1=1831363&r2=1831364&view=diff
==============================================================================
--- ignite/site/trunk/features/machinelearning.html (original)
+++ ignite/site/trunk/features/machinelearning.html Thu May 10 17:54:58 2018
@@ -82,15 +82,15 @@ under the License.
             <div class="page-heading">Problem #2: Lack of Horizontal Scalability</div>
 
             <p>
-                The second factor is related to scalability. A number of ML and DL algorithms that have to
-                process data sets which no longer fit within a single server unit is constantly growing. This urges
-                the data scientist to come up with sophisicated solutions or turn to distributed
+                The second factor is related to scalability. ML and DL algorithms that have to
+                process data sets which no longer fit within a single server unit are constantly growing.
+                This urges the data scientist to come up with sophisticated solutions o​r turn to distributed
                 computing platforms such as Apache Spark and TensorFlow. However, those platforms mostly solve
-                only a part of the puzzle which is the models training, making its a burden of the developers
-                to decide how do deploy the models in production later.
+                only a part of the puzzle which is the models training, making it a burden of the developers to
+                decide how do deploy the models in production later.
             </p>
 
-            <div class="page-heading">Zero ETL and Massive Scalability With Ignite</div>
+            <div class="page-heading">Zero ETL and Massive Scalability</div>
 
             <p>
                 Ignite Machine Learning relies on Ignite's memory-centric storage that brings massive scalability
@@ -104,6 +104,14 @@ under the License.
                 long processing wait times, Ignite Machine learning enables continuous learning that can
                 improve decisions based on the latest data as it arrives in real-time.
             </p>
+
+            <div class="page-heading">Fault Tolerance and Continuous Learning</div>
+            <p>
+                Apache Ignite Machine Learning is tolerant to node failures. This means that in the case of node
+                failures during the learning process, all recovery procedures will be transparent to the user,
+                learning processes won't be interrupted, and we will get results in the time similar to the case when
+                all nodes work fine.
+            </p>
             <p><a href="https://apacheignite.readme.io/docs/machine-learning" target="_blank">Read more</a></p>
         </section>