Machine Learning training on Hadoop + Mahout

Machine Learning Training

machine learning training with Mahout

Apache Mahout Training @ BigDataTraining.IN

Hadoop provides a framework for implementing large-scale data processing applications. Often, the users implement their applications on MapReduce from scratch or write their applications using a higher-level programming model such as Pig or Hive.

However, implementing some of the algorithms using MapReduce can be very complex. For example, algorithms such as collaborative filtering, clustering, and recommendations need complex code. This is further agitated by the need to maximize parallel executions.

Mahout is an effort to implement well-known machine learning and data mining algorithms using MapReduce framework, so that the users can reuse them in their data processing without having to rewrite them from the scratch. This recipe explains how to install Mahout.

The quality of recommendations is largely determined by the quantity and quality of data. “Garbage in, garbage out,” has never been more true than here. Having high-quality data is a good thing, and generally, having lots of it is also good.

Recommender algorithms are data-intensive by nature; their computations access a great deal of information. Runtime performance is therefore greatly affected by the quantity of data and its representation. Intelligently choosing data structures can affect performance by orders of magnitude, and, at scale, it matters a lot.

Tackling large scale with Mahout and Hadoop

How real is the problem of scale in machine learning algorithms? Let’s consider the size of a few problems where you might deploy Mahout.

According to an analysis, Google News sees about 3.5 million new news articles per day. Although this does not seem like a large amount in absolute terms, consider that these articles must be clustered, along with other recent articles, in minutes in order to become available in a timely manner.

The subset of rating data that Netflix published for the Netflix Prize contained 100 million ratings. Because this was just the data released for contest purposes, presumably the total amount of data that Netflix actually has and must process to create recommendations is many times larger!

Machine learning techniques must be deployed in contexts like these, where the amount of input is large—so large that it isn’t feasible to process it all on one computer, even a powerful one. Without an implementation such as Mahout, these would be impossible tasks. This is why Mahout makes scalability a top priority, and why this training at BigDataTraining.IN will focus, in a way that others don’t, on dealing with large data sets effectively.

Sophisticated machine learning techniques, applied at scale, were until recently only something that large, advanced technology companies could consider using. But today computing power is cheaper than ever and more accessible via open source frameworks like Apache’s Hadoop. Mahout attempts to complete the puzzle by providing quality, open source implementations capable of solving problems at this scale with Hadoop, and putting this into the hands of all technology organizations.

Hadoop implements the MapReduce paradigm, which is no small feat, even given how simple MapReduce sounds. It manages storage of the input, intermediate key-value pairs, and output; this data could potentially be massive and must be available to many worker machines, not just stored locally on one. It also manages partitioning and data transfer between worker machines, as well as detection of and recovery from individual machine failures. Understanding how much work goes on behind the scenes will help prepare you for how relatively complex using Hadoop can seem. It’s not just a library you add to your project. It’s several components, each with libraries and (several) standalone server processes, which might be run on several machines. Operating processes based on Hadoop isn’t simple, but investing in a scalable, distributed implementation can pay dividends later: your data may quickly grow to great size, and this sort of scalable implementation is a way to future-proof your application.

Recommender engines

Recommender engines are the most immediately recognizable machine learning technique in use today. You’ll have seen services or sites that attempt to recommend books or movies or articles based on your past actions. They try to infer tastes and preferences and identify unknown items that are of interest

Clustering

Clustering is less apparent, but it turns up in equally well-known contexts. As its name implies, clustering techniques attempt to group a large number of things together into clusters that share some similarity. It’s a way to discover hierarchy and order in a large or hard-to-understand data set, and in that way reveal interesting patterns or make the data set easier to comprehend.

Classification

Classification techniques decide how much a thing is or isn’t part of some type or category, or how much it does or doesn’t have some attribute. Classification, like clustering, is ubiquitous, but it’s even more behind the scenes. Often these systems learn by reviewing many instances of items in the categories in order to deduce classification rules.

  • Google’s Picasa and other photo-management applications can decide when a region of an image contains a human face.

  • Optical character recognition software classifies small regions of scanned text into individual characters.

  • Apple’s Genius feature in iTunes reportedly uses classification to classify songs into potential playlists for users.

Classification helps decide whether a new input or thing matches a previously observed pattern or not, and it’s often used to classify behavior or patterns as unusual. It could be used to detect suspicious network activity or fraud. It might be used to figure out when a user’s message indicates frustration or satisfaction.

Each of these techniques works best when provided with a large amount of good input data. In some cases, these techniques must not only work on large amounts of input, but must produce results quickly, and these factors make scalability a major issue. And, BigDataTraining.IN focuses to answer, one of Mahout’s key reasons for being is to produce implementations of these techniques that do scale up to huge input.

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