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Chapter 12 There are three modes of use: a reserved mode, whereby you prepay to have cheaper per-hour access; a fixed per-hour rate; and a variable rate which depends on the overall compute market (when there is less demand, the costs are lower; when there is more demand, the prices go up). For testing, you can use a single machine in the free tier. This allows you to play around with the system, get used to the interface, and so on. However, this is a very slow CPU machine. Thus, doing heavy computation using it is not advised. On top of this general system, there are several types of machines available with varying costs; from a single core to a multicore system with a lot of RAM, or even graphical processing units (GPUs). We will later see that you can also get several of the cheaper machines and build yourself a virtual cluster. You can also choose to get a Linux or Windows server, with Linux being slightly cheaper. In this chapter, we will work our examples on Linux but most of this information would be valid for Windows machines as well. The resources can be managed through a web interface. However, it is also possible to do so programmatically and by setting up scripts which allocate virtual machines, setting up disks, and all the operations that are possible through the web interface. In fact, while the web interface changes very frequently (and some of the screenshots that we show in the book may be out-of-date by the time it goes to the press), the programmatic interface is more stable and the general architecture has remained stable since the service was introduced. Access to AWS services is performed through a traditional username/password system, although Amazon calls the username an access key and the password a secret key. They probably do so to keep it separate from the username/password you use to access the web interface. In fact, you can create as many access/secret key pairs as you wish and give them different permissions. This is helpful for a larger team where a senior user with access to the full web panel can create other keys for developers with less privileges. Amazon regions Amazon.com has several regions. These correspond to physical regions of the world: West Coast U.S., East Coast US, several Asian locations, a South American one, and a European one. If you will be transferring data, it is best to keep it close to where you will be transferring to and from. Additionally, if you are handling user information, there may be regulatory issues if you transfer it to another jurisdiction. In that case, do check with an informed counsel on what the implications of transferring data about European customers to the US or vice versa are. [ 249 ]
Big(ger) Data Amazon Web Services is a very large topic, and there are various books exclusively available which cover AWS entirely. The purpose of this chapter is to give you an overall impression of what is available and what is possible with AWS. In the practical spirit of this book, we do this by working through examples, but we will not exhaust all possibilities. Creating your first machines The first step is to go to http://aws.amazon.com/ and create an account. These steps are similar to any other online service. If you want to have more than a single low-powered machine, you will need a credit card. In this example, we will use a few machines, so it may cost you a few dollars if you want to run through it. If you are not ready to take out a credit card just yet, you can certainly read the chapter to learn what AWS provides without going through the examples. Then you can make a more informed decision on whether to sign up. Once you sign up for AWS and log in, you will be taken to the console. Here you will see the many services that AWS provides: [ 250 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Learning How to Classify with Real-
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Chapter 2 We are using Matplotlib;
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Chapter 2 The last few lines select
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Chapter 2 error = 0.0 for ei in ran
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Chapter 2 We can play around with t
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Chapter 2 Features and feature engi
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Chapter 2 In the preceding screensh
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Chapter 2 Binary and multiclass cla
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Clustering - Finding Related Posts
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Chapter 3 How to do it More robust
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Chapter 3 This means that the first
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Chapter 3 ... post = posts[i] ... i
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Chapter 3 If you have a clear pictu
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Chapter 3 Extending the vectorizer
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Chapter 3 0.0 >>> print(tfidf("b",
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Chapter 3 Flat clustering divides t
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Because the cluster centers are mov
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Chapter 3 'D:\\data\\379\\raw\\comp
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As we have learned previously, we w
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Chapter 3 Position Similarity Excer
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Chapter 3 But before you go there,
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Topic Modeling For those who are in
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Topic Modeling Sparsity means that
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Topic Modeling Although daunting at
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Topic Modeling … for tj,v in t:
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Topic Modeling Finally, we build th
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Topic Modeling Alternatively, we ca
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Topic Modeling Topic modeling was f
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Classification - Detecting Poor Ans
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Classification II - Sentiment Analy
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Chapter 6 Getting to know the Bayes
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Using Naive Bayes to classify Given
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Chapter 6 This denotation "" leads
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Chapter 6 Similarly, we do this for
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Chapter 6 A quick look at the previ
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Chapter 6 To keep our experimentati
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Chapter 6 Y = np.zeros(Y.shape[0])
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Chapter 6 ° ° Experiment with whe
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Chapter 6 We have to be patient whe
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Chapter 6 First, we define a range
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Chapter 6 Determining the word type
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Chapter 6 Successfully cheating usi
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Chapter 6 Our first estimator Now w
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Chapter 6 for d in documents: allca
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Regression - Recommendations You ha
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Chapter 7 The preceding graph shows
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Chapter 7 Root mean squared error a
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Penalized regression The important
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Chapter 7 P greater than N scenario
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Chapter 7 So, we can see that the d
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Chapter 7 Fortunately, scikit-learn
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[ 161 ] Chapter 7 The loading of th
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Chapter 7 Summary In this chapter,
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Regression - Recommendations Improv
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Weights Regression - Recommendation
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Classification III - Music Genre Cl
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Chapter 9 Matplotlib provides the c
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[ 185 ] Chapter 9
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Chapter 9 def create_fft(fn): sampl
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Chapter 9 ax.set_yticks(range(len(g
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Chapter 9 On the left-hand side gra
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[ 193 ] Chapter 9 Improving classif
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We get the following promising resu
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Chapter 9 Summary In this chapter,
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