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| <title>Machine Learning - Apache Ignite</title> |
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| <section id="machine-learning" class="page-section"> |
| <h1 class="first">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> |
| <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> |
| |
| <p> |
| First, the models are trained and deployed (after the training is over) in different systems. |
| The data scientists have to wait for ETL or some other data transfer process to move the data |
| into a system like Apache Mahout or Apache Spark for a training purpose. Then they have to wait |
| while this process completes and redeploy the models in a production environment. The whole |
| process can take hours moving terabytes of data from one system to another. Moreover, the |
| training part usually happens over the old data set. |
| </p> |
| </div> |
| <div class="col-sm-6 col-md-5 col-xs-12" style="padding-right:0; top: -10px;"> |
| <img class="img-responsive" src="/images/machine_learning.png" width="440px" style="float:right;"/> |
| </div> |
| </div> |
| |
| <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 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> |
| |
| <p> |
| Ignite Machine Learning relies on Ignite's memory-centric storage that brings massive scalability |
| for ML and DL tasks and eliminates the wait imposed by ETL between the different systems. |
| For instance, it allows users to run ML/DL training and inference directly on data stored across |
| memory and disk in an Ignite cluster. Next, Ignite provides a host |
| of ML and DL algorithms that are optimized for Ignite's collocated distributed processing. |
| 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 |
| 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" rel="noopener">Read more</a></p> |
| </section> |
| |
| <section id="ga-grid" class="page-section"> |
| <div class="col-sm-12 col-md-12 col-xs-12"> |
| <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 |
| solving optimization problems by simulating the process of biological evolution. |
| </p> |
| <p> |
| GAs are excellent for searching through large and complex data sets for an optimal solution. |
| Real world applications of GAs include: automotive design, computer gaming, robotics, investments, |
| traffic/shipment routing and more. |
| </p> |
| |
| <div class="page-links"> |
| <a href="https://apacheignite.readme.io/docs/genetic-algorithms" target="_blank" rel="noopener">Genetic Algorithms<i class="fa fa-angle-double-right"></i></a> |
| </div> |
| </div> |
| <div class="col-sm-6 col-md-5 col-xs-12" style="padding-right:0;"> |
| <a href="/images/GAGrid_Overview.png"><img class="img-responsive" src="/images/GAGrid_Overview.png"></a> |
| <p class="img-caption">Click on the image to view full size.</p> |
| </div> |
| </div><p> </p> |
| </section> |
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