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 oâr 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>
