AI News, PredictionIO (Open Source Version) vs Microsoft Azure Machine Learning

PredictionIO (Open Source Version) vs Microsoft Azure Machine Learning

The former is a platform for creating predictive APIs hosted on the Microsoft Azure cloud, whereas the latter is an open source machine learning server that you run on your own infrastructure, also to expose predictive models as APIs.

It’s clear that both companies have similar visions and similar features, but when digging deeper you’ll notice key differences and key advantages to each...

Similar visions One major hurdle that companies encounter in their machine learning projects is taking data scientists’ work to production in order to deliver predictions to end users (who’ll ultimately benefit from them).

Another aspect on which both organizations have been working on is the ability to create and reuse predefined templates and workflows to help their users launch predictive APIs more quickly than with traditional development methods.

Azure’s strengths Although PredictionIO’s install is super easy (just a one-line command, or you can fire up an already provisioned Amazon instance or a Terminal.com snap in 5 seconds), with Azure there’s nothing to install at all.

Azure ML’s interface with its canvas (in the middle) One advantage of working with a cloud platform such as Azure is its auto-scaling feature: models are deployed in a way that’s elastic and you don’t have to worry about scaling out your APIs.

Designing Predictive Algorithms for Machine Learning

In 2014, Machine learning was one of the newest and most utilizable tools in a Data Scientist's arsenal. In this TechEd talk you will learn key architectural ...

PredictionIO:­ Building Smarter Apps With Machine Learning - Time Series (Part 1 of 3)

Successfully leveraging Machine Learning (ML) at scale in production environments is challenging (data collection, real-time querying, large-scale data storage ...

code.talks 2017 - Creating recommender systems with Spark, Scala and Prediction.Io (Florian Krause)

Creating recommender systems with Spark, Scala and Prediction.Io Speaker: Florian Krause (Director Software Development @ Performance Advertising ...

7 Machine Learning as a Service Platforms for Beginners and Pros

If you plan to deal with machine learning and you are searching for machine learning platforms -- this video is for you. We will tell you about the most popular ML ...

Forecasting with Predictive Analytics

Forecasting with predictive analytics offers the opportunity to leverage the huge amounts of data, now readily available, that exhibit the following characteristics: ...

Intro to Azure ML & Cloud Computing

Azure Machine Learning Studio is a fully featured graphical data science tool in the cloud. You will learn how to upload, analyze, visualize, manipulate, and ...

Displaying all Patterns Recognized: Machine Learning for Algorithmic Trading in Forex and Stocks

In this video, you are shown how to display all of the patterns at the same time, to make comparing visually easier. Plus it makes for pretty pictures...for all you ...

1. Desgning a recommendation System - Data Science and Machine Learning in Practice

In this episode we have designed a movie recommendation system by implementing concepts of Data Science and Machine Learning. We've taken data from ...

#171: Data, Internet of Things, and Machine Learning with Adam Bosworth and Gary Flake, Salesforce

171: Data, Internet of Things, and Machine Learning with Adam Bosworth and Gary Flake, Salesforce.com The future of customer relationships is data. From the ...

Soraya Hausl - Leveraging recommender systems to personalise search results

Filmed at PyData London 2017 Description This talk discuses an approach to personalise search results by leveraging techniques of ..