| extend ../_components/base.pug |
| |
| block pagetitle |
| title Continuous Machine Learning, Scalable Deep Learning - Apache Ignite |
| meta(name="description", content="Apache Ignite Machine Learning is a set of simple and efficient APIs to enable continuous learning. It relies on Ignite's multi-tier storage that bring massive scalability for machine learning and deep learning tasks.") |
| link(rel="canonical", href="https://ignite.apache.org/features/machinelearning.html") |
| |
| meta(property="og:title", content="Continuous Machine Learning, Scalable Deep Learning - Apache Ignite") |
| meta(property="og:type", content="article") |
| meta(property="og:url", content="https://ignite.apache.org/features/machinelearning.html") |
| meta(property="og:image", content="/img/og-pic.png") |
| meta(property="og:description", content="Apache Ignite Machine Learning is a set of simple and efficient APIs to enable continuous learning. It relies on Ignite's multi-tier storage that bring massive scalability for machine learning and deep learning tasks.") |
| |
| block css |
| link(rel="stylesheet", href="../css/native-persistence.css?ver=" + config.version) |
| link(rel="stylesheet", href="../css/compute-apis.css?ver=" + config.version) |
| link(rel="stylesheet", href="../css/machinelearning.css?ver=" + config.version) |
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| block main |
| - global.pageHref = "features" |
| - config.hdrClassName = "hdr__blue" |
| include ../_components/header.pug |
| |
| |
| section.innerhero |
| .container.innerhero__cont |
| .innerhero__main |
| .innerhero__pre.pb-3 Apache Ignite |
| h1.h1.innerhero__h1 Machine Learning<br> APIs |
| .innerhero__descr.pt-2.h5. |
| Continuously train, execute and update your machine learning<br> models at scale and in real time |
| .innerhero__action |
| a.button.innerhero__button(href="https://ignite.apache.org/docs/latest/index") Start Coding |
| img.innerhero__pic.innerhero__pic--machine(src="/img/features/machinelearning/machine.svg", alt="Machine-hero") |
| // /.innerhero |
| |
| |
| |
| section.machine1 |
| .container |
| .machine1__block.flexi |
| .machine1__info |
| h2.compute2__h2 Ignite Machine Learning APIs Overview |
| p.machine1__text.pt-5 Ignite Machine Learning (ML) is a set of simple, scalable, and efficient tools that allow building predictive machine learning models without costly data transfers. |
| h3.machine1__title.machine-top How does Apache Ignite support ML APIs? |
| p.machine1__text You have two options: |
| .machine1__options.flexi |
| .machine1__option |
| .machine1__number 01 |
| .machine1__subtext Use built-in ML APIs for some of the typical ML and deep learning (DL) tasks, such as: |
| .machine1__subtext.flexi |
| span — Classification |
| span — Regression |
| span — Clustering |
| span — Recommendation |
| span — Preprocessing |
| .machine1__option |
| .machine1__number 02 |
| .machine1__subtext Use external ML and DL libraries that use Apache Ignite as scalable and high-performance distributed data storage: |
| .machine1__subtext.flexi |
| span — TensorFlow |
| span — Scikit |
| span — Spark |
| span — And more |
| |
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| img.machine1__image(src="/img/features/machinelearning/image.svg", alt="image") |
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| section.compute2 |
| .container |
| h2.compute2__h2 Benefits of Apache Ignite Machine Learning APIs |
| .machineitem.machineitem1.flexi |
| h3.machine__title Expedite the training process <br>with horizontally scalable cluster |
| .machine__info |
| p.machine__text 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: |
| .machine__part.flexi |
| .compute2-points__item.fz20 |
| .machine__subtext Avoid, or minimise ETL |
| .machine__part.flexi |
| .compute2-points__item.fz20 |
| .machine__subtext Load all your training data sets in the same cluster |
| .machine__part.flexi |
| .compute2-points__item.fz20 |
| .machine__subtext Minimise network utilization during the training process |
| .machineitem.machineitem1.flexi |
| h3.machine__title Execute your ML models with in-memory speed from your application code |
| .machine__info |
| p.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. |
| .machineitem.machineitem1.flexi |
| h3.machine__title Continue updating your models with new data in real time |
| .machine__info |
| p.machine__text Data and user behavior change rapidly, so you must constantly update your models. With Apache Ignite, you can update your already deployed ML models with new data sets. |
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| // /.compute2 |
| |
| section.native-bottom.container |
| .native-bottom__grid |
| article.nativebotblock |
| .h4.nativebotblock__title |
| img(src="/img/features/native-rocket.svg", alt="").nativebotblock__icon |
| span Ready to Start? |
| p.nativebotblock__text Start coding machine learning APIs |
| a.nativebotblock__link.arrowlink(href="https://ignite.apache.org/docs/latest/machine-learning/machine-learning", target="_blank") Performing Machine Learning |
| article.nativebotblock.nativebotblock--learn |
| .h4.nativebotblock__title |
| img(src="/img/features/native-docs.svg", alt="").nativebotblock__icon |
| span Want to Learn More? |
| p.nativebotblock__text Check out how Apache Ignite updates<br> trained models in real time |
| a.nativebotblock__link.arrowlink(href="https://ignite.apache.org/docs/latest/machine-learning/updating-trained-models", target="_blank") Updating Trained Models |
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