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| <div class="innerhero__pre pb-3">Apache Ignite</div> |
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| Machine Learning<br /> |
| APIs |
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| Continuously train, execute and update your machine learning<br /> |
| models at scale and in real-time |
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| <h2 class="compute2__h2">Ignite Machine Learning APIs Overview</h2> |
| <h3 class="machine1__title machine-top">What is it?</h3> |
| <p class="machine1__text">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> |
| <h3 class="machine1__title machine-top">How does Apache Ignite support ML APIs?</h3> |
| <p class="machine1__text">You have two options:</p> |
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| <div class="machine1__number">01</div> |
| <div class="machine1__subtext">Use built-in ML APIs for some of the typical ML and deep learning (DL) tasks, such as:</div> |
| <div class="machine1__subtext flexi"><span>— Classification</span><span>— Regression</span><span> — Clustering</span><span>— Recommendation</span><span>— Preprocessing</span></div> |
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| <div class="machine1__number">02</div> |
| <div class="machine1__subtext">Use external ML and DL libraries that use Apache Ignite as scalable and high-performance distributed data storage:</div> |
| <div class="machine1__subtext flexi"><span>— TensorFlow</span><span>— Scikit</span><span>— Spark</span><span>— And more</span></div> |
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| <h2 class="compute2__h2">Benefits of Apache Ignite Machine Learning API</h2> |
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| <h3 class="machine__title">Expedite the training process with horizontal cluster scalability</h3> |
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| You can distribute your training data set over an unlimited number of cluster nodes and train your models with the speed of memory.<br /> |
| With built-in Ignite ML APIs, you: |
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| <div class="machine__subtext">Avoid, or minimise ETL</div> |
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| <div class="machine__subtext">Load all your training data sets in the same cluster</div> |
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| <div class="machine__subtext">Minimise network utilization during the training process</div> |
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| <h3 class="machine__title">Execute your ML models with in-memory speed from your application code</h3> |
| <div class="machine__info"><p class="machine__text">Once the model is trained, deploy it on the cluster and execute it with in-memory speed. Use built-in Ignite APIs or 3rd party libraries.</p></div> |
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| <h3 class="machine__title">Continue updating your models with new data in real-time</h3> |
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| <p class="machine__text">Data and user behaviours change rapidly, so you always need to advance your models. With Apache Ignite, you can update your already deployed ML models with new data sets.</p> |
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| <h3 class="h4 nativebotblock__title"><img class="nativebotblock__icon" src="/img/features/native-rocket.svg" alt="" /><span>Ready to Start?</span></h3> |
| <p class="nativebotblock__text">Start coding machine learning APIs</p> |
| <a class="nativebotblock__link arrowlink" href="https://ignite.apache.org/docs/latest/machine-learning/machine-learning" target="_blank">Performing Machine Learning</a> |
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| Check out how Apache Ignite updates<br /> |
| trained models in real time |
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| <a class="nativebotblock__link arrowlink" href="https://ignite.apache.org/docs/latest/machine-learning/updating-trained-models" target="_blank">Updating Trained Models</a> |
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