blob: 86e69e0307d170f88995c78eb74cb99ea534ec74 [file] [log] [blame]
extend ../_components/base.pug
block pagetitle
| Machine Learning APIs
block css
link(rel="stylesheet", href="../css/native-persistence.css")
link(rel="stylesheet", href="../css/compute-apis.css")
link(rel="stylesheet", href="../css/machinelearning.css")
block main
- global.pageHref = "features"
- config.hdrClassName = "hdr__blue"
include ../_components/header.pug
.innerhero__pre.pb-3 Apache Ignite
h1.h1.innerhero__h1 Machine Learning<br> APIs
Continuously train, execute and update your machine learning<br> models at scale and in real-time
a.button.innerhero__button(href="") Start Coding
img.innerhero__pic.innerhero__pic--machine(src="/public/img/features/machinelearning/machine.svg", alt="Machine-hero")
// /.innerhero
h2.compute2__h2 Ignite Machine Learning APIs Overview
h3.machine1__title.machine-top What is it?
p.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.
h3.machine1__title.machine-top How does Apache Ignite support ML APIs?
p.machine1__text You have two options:
.machine1__number 01
.machine1__subtext Use built-in ML APIs for some of the typical ML and deep learning (DL) tasks, such as:
span Classification
span Regression
span Clustering
span Recommendation
span Preprocessing
.machine1__number 02
.machine1__subtext Use external ML and DL libraries that use Apache Ignite as scalable and high-performance distributed data storage:
span TensorFlow
span Scikit
span Spark
span And more
img.machine1__image(src="/public/img/features/machinelearning/image.svg", alt="image")
h2.compute2__h2 Benefits of Apache Ignite Machine Learning API
h3.machine__title Expedite the training process with horizontal cluster scalability
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__subtext Avoid, or minimise ETL
.machine__subtext Load all your training data sets in the same cluster
.machine__subtext Minimise network utilization during the training process
h3.machine__title Execute your ML models with in-memory speed from your application code
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.
h3.machine__title Continue updating your models with new data in real-time
p.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.
// /.compute2
img(src="/public/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="", target="_blank") Performing Machine Learning
img(src="/public/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="", target="_blank") Updating Trained Models