Author: dmagda
Date: Tue Feb 11 23:58:49 2020
New Revision: 1873923
URL: http://svn.apache.org/viewvc?rev=1873923&view=rev
Log:
Updated machine learning and tensorflow pages.
Modified:
ignite/site/branches/ignite-redisign/features/machinelearning.html
ignite/site/branches/ignite-redisign/features/tensorflow.html
Modified: ignite/site/branches/ignite-redisign/features/machinelearning.html
URL:
http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/machinelearning.html?rev=1873923&r1=1873922&r2=1873923&view=diff
==============================================================================
--- ignite/site/branches/ignite-redisign/features/machinelearning.html
(original)
+++ ignite/site/branches/ignite-redisign/features/machinelearning.html Tue Feb
11 23:58:49 2020
@@ -39,7 +39,13 @@ under the License.
<meta http-equiv="Cache-Control" content="no-cache, no-store,
must-revalidate" />
<meta http-equiv="Pragma" content="no-cache" />
<meta http-equiv="Expires" content="0" />
+
<title>Machine Learning - Apache Ignite</title>
+
+ <meta name="description"
+ content="Apache Ignite Machine Learning is a set of simple,
scalable, and efficient APIs that
+ allow building predictive machine learning models at
scale and to enable continuous learning."/>
+
<!--#include virtual="/includes/styles.html" -->
<!--#include virtual="/includes/sh.html" -->
@@ -50,16 +56,16 @@ under the License.
<main id="main" role="main" class="container">
<section id="machine-learning" class="page-section">
- <h1 class="first">Machine Learning</h1>
+ <h1 class="first">Apache Ignite Machine Learning</h1>
<div class="col-sm-12 col-md-12 col-xs-12" style="padding-left:0;
padding-right:0;">
<div class="col-sm-6 col-md-7 col-xs-12"
style="padding-left:0; padding-right:0;">
- <p>Apache Ignite Machine Learning (ML) is a set of simple,
scalable and efficient tools that allow
- building predictive machine learning models without
costly data transfers.
+ <p>
+ Apache Ignite Machine Learning (ML) is a set of
simple, scalable, and efficient tools that
+ allow building predictive machine learning models
without costly data transfers.
</p>
<p>
The rationale for adding machine and deep learning
(DL) to Apache Ignite is quite simple.
Today's data scientists have to deal with two major
factors that keep ML from mainstream adoption.
-
</p>
<div class="page-heading">Problem #1: Constant Data
Movement (ETL)</div>
@@ -80,12 +86,11 @@ under the License.
<div class="page-heading">Problem #2: Lack of Horizontal
Scalability</div>
<p>
- 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
it a burden of the developers to
- decide how do deploy the models in production later.
+ The second factor relates to scalability. ML and DL algorithms
that have to process data sets that no
+ longer fit within a single server unit are continually
growing. That requires data scientists to come
+ up with sophisticated solutions or 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 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</div>
@@ -99,16 +104,16 @@ under the License.
These implementations deliver in-memory speed and unlimited
horizontal scalability when running
in place against massive data sets or incrementally against
incoming data streams, without
requiring the data to be moved into another store. By
eliminating the data movement and the
- long processing wait times, Ignite Machine learning enables
continuous learning that can
+ lengthy 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.
+ Apache Ignite Machine Learning is tolerant to node failures.
That 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" rel="noopener">Read more</a></p>
</section>
@@ -118,7 +123,8 @@ under the License.
<div class="col-sm-6 col-md-7 col-xs-12"
style="padding-left:0; padding-right:15px;">
<h2 style="padding-bottom: 5px; margin-bottom:
20px;">Genetic Algorithms</h2>
- <p>Machine learning component goes with a set of genetic
algorithms (GA) which is a method of
+ <p>
+ Machine learning component goes with a set of genetic
algorithms (GA) which is a method of
solving optimization problems by simulating the
process of biological evolution.
</p>
<p>
@@ -136,6 +142,23 @@ under the License.
<p class="img-caption">Click on the image to view full
size.</p>
</div>
</div><p> </p>
+
+ <div class="page-heading">Learn More</div>
+ <p>
+ <a href="https://apacheignite.readme.io/docs/machine-learning";
target="docs">
+ <b>Ignite Machine Learning Documentation <i class="fa
fa-angle-double-right"></i></b>
+ </a>
+ </p>
+ <p>
+ <a
href="https://apacheignite.readme.io/docs/ml-partition-based-dataset";
target="docs">
+ <b>Partition-Based Data Sets <i class="fa
fa-angle-double-right"></i></b>
+ </a>
+ </p>
+ <p>
+ <a href="/features/tensorflow.html">
+ <b>Apache Ignite integration for TensorFlow <i class="fa
fa-angle-double-right"></i></b>
+ </a>
+ </p>
</section>
</main>
Modified: ignite/site/branches/ignite-redisign/features/tensorflow.html
URL:
http://svn.apache.org/viewvc/ignite/site/branches/ignite-redisign/features/tensorflow.html?rev=1873923&r1=1873922&r2=1873923&view=diff
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