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Some slides I liked on cloud computing infrastructure as offered by Amazon, IBM, Google , Windows and Oracle
Latest from the Amazon Cloud-
hi1.4xlarge instances come with eight virtual cores that can deliver 35 EC2 Compute Units (ECUs) of CPU performance, 60.5 GiB of RAM, and 2 TiB of storage capacity across two SSD-based storage volumes. Customers using hi1.4xlarge instances for their applications can expect over 120,000 4 KB random write IOPS, and as many as 85,000 random write IOPS (depending on active LBA span). These instances are available on a 10 Gbps network, with the ability to launch instances into cluster placement groups for low-latency, full-bisection bandwidth networking.
High I/O instances are currently available in three Availability Zones in US East (N. Virginia) and two Availability Zones in EU West (Ireland) regions. Other regions will be supported in the coming months. You can launch hi1.4xlarge instances as On Demand instances starting at $3.10/hour, and purchase them as Reserved Instances
High I/O Instances
Instances of this family provide very high instance storage I/O performance and are ideally suited for many high performance database workloads. Example applications include NoSQL databases like Cassandra and MongoDB. High I/O instances are backed by Solid State Drives (SSD), and also provide high levels of CPU, memory and network performance.
High I/O Quadruple Extra Large Instance
60.5 GB of memory
35 EC2 Compute Units (8 virtual cores with 4.4 EC2 Compute Units each)
2 SSD-based volumes each with 1024 GB of instance storage
I/O Performance: Very High (10 Gigabit Ethernet)
Storage I/O Performance: Very High*
API name: hi1.4xlarge
*Using Linux paravirtual (PV) AMIs, High I/O Quadruple Extra Large instances can deliver more than 120,000 4 KB random read IOPS and between 10,000 and 85,000 4 KB random write IOPS (depending on active logical block addressing span) to applications. For hardware virtual machines (HVM) and Windows AMIs, performance is approximately 90,000 4 KB random read IOPS and between 9,000 and 75,000 4 KB random write IOPS. The maximum sequential throughput on all AMI types (Linux PV, Linux HVM, and Windows) per second is approximately 2 GB read and 1.1 GB write.
I had a chance to dekko the new startup BigML https://bigml.com/ and was suitably impressed by the briefing and my own puttering around the site. Here is my review-
1) The website is very intutively designed- You can create a dataset from an uploaded file in one click and you can create a Decision Tree model in one click as well. I wish other cloud computing websites like Google Prediction API make design so intutive and easy to understand. Also unlike Google Prediction API, the models are not black box models, but have a description which can be understood.
2) It includes some well known data sources for people trying it out. They were kind enough to offer 5 invite codes for readers of Decisionstats ( if you want to check it yourself, use the codes below the post, note they are one time only , so the first five get the invites.
BigML is still invite only but plan to get into open release soon.
3) Data Sources can only be by uploading files (csv) but they plan to change this hopefully to get data from buckets (s3? or Google?) and from URLs.
4) The one click operation to convert data source into a dataset shows a histogram (distribution) of individual variables.The back end is clojure , because the team explained it made the easiest sense and fit with Java. The good news (?) is you would never see the clojure code at the back end. You can read about it from http://clojure.org/
As cloud computing takes off (someday) I expect clojure popularity to take off as well.
Clojure is a dialect of Lisp
5) As of now decision trees is the only distributed algol, but they expect to roll out other machine learning stuff soon. Hopefully this includes regression (as logit and linear) and k means clustering. The trees are created and pruned in real time which gives a slightly animated (and impressive effect). and yes model building is an one click operation.
The real time -live pruning is really impressive and I wonder why /how it can ever be replicated in other software based on desktop, because of the sheer interactive nature.
Making the model is just half the work. Creating predictions and scoring the model is what is really the money-earner. It is one click and customization is quite intuitive. It is not quite PMML compliant yet so I hope some Zemanta like functionality can be added so huge amounts of models can be applied to predictions or score data in real time.
If you are a developer/data hacker, you should check out this section too- it is quite impressive that the designers of BigML have planned for API access so early.
BigML.io gives you:
- Secure programmatic access to all your BigML resources.
- Fully white-box access to your datasets and models.
- Asynchronous creation of datasets and models.
- Near real-time predictions.
Note: For your convenience, some of the snippets below include your real username and API key.
Please keep them secret.
BigML.io conforms to the design principles of Representational State Transfer (REST). BigML.io is enterely HTTP-based.
BigML.io gives you access to four basic resources: Source, Dataset, Model and Prediction. You cancreate, read, update, and delete resources using the respective standard HTTP methods: POST, GET,PUT and DELETE.
All access to BigML.io must be performed over HTTPS
and https://bigml.com/developers/quick_start ( In think an R package which uses JSON ,RCurl would further help in enhancing ease of usage).
Overall a welcome addition to make software in the real of cloud computing and statistical computation/business analytics both easy to use and easy to deploy with fail safe mechanisms built in.
Check out https://bigml.com/ for yourself to see.
The invite codes are here -one time use only- first five get the invites- so click and try your luck, machine learning on the cloud.
If you dont get an invite (or it is already used, just leave your email there and wait a couple of days to get approval)
I was wondering why the planet spends so much money in the $150-billion business process outsourcing industry, especially in voice calls to call centers.
If your iPhone Siri phone can be configured to answer any query, Why can’t it be configured to be a virtual assistant, customer support, marketing outbound or even a super charged call center interactive voice response .
Can we do and run some tests on this?
I almost missed this because of my vacation and traveling
Rapid Miner has a tonne of new stuff (Statuary Ethics Declaration- Rapid Miner has been an advertising partner for Decisionstats – see the right margin)
Great New Graphical Plotters
and some flashy work
and a great series of educational lectures
A Simple Explanation of Decision Tree Modeling based on Entropies
Description of some of the basics of decision trees. Simple and hardly any math, I like the plots explaining the basic idea of the entropy as splitting criterion (although we actually calculate gain ratio differently than explained…)
Logistic Regression for Business Analytics using RapidMiner
Same as above, but this time for modeling with logistic regression.
Easy to read and covering all basic ideas together with some examples. If you are not familiar with the topic yet, part 1 (see below) might help.
and lastly a new research project for collaborative data mining
e-LICO Architecture and Components
The goal of the e-LICO project is to build a virtual laboratory for interdisciplinary collaborative research in data mining and data-intensive sciences. The proposed e-lab will comprise three layers: the e-science and data mining layers will form a generic research environment that can be adapted to different scientific domains by customizing the application layer.
- Drag a data set into one of the slots. It will be automatically detected as training data, test data or apply data, depending on whether it has a label or not.
- Select a goal. The most frequent one is probably “Predictive Modelling”. All goals have comments, so you see what they can be used for.
- Select “Fetch plans” and wait a bit to get a list of processes that solve your problem. Once the planning completes, select one of the processes (you can see a preview at the right) and run it. Alternatively, select multiple (selecting none means selecting all) and evaluate them on your data in a batch.
The assistant strives to generate processes that are compatible with your data. To do so, it performs a lot of clever operations, e.g., it automatically replaces missing values if missing values exist and this is required by the learning algorithm or performs a normalization when using a distance-based learner.
You can install the extension directly by using the Rapid-I Marketplace instead of the old update server. Just go to the preferences and enter http://rapidupdate.de:8180/UpdateServer as the update URL
Of course Rapid Miner has been of the most professional open source analytics company and they have been doing it for a long time now. I am particularly impressed by the product map (see below) and the graphical user interface.
Just click on the products in the overview below in order to get more information about Rapid-I products.
In part 3 of the series for predictions for 2012, here is Jill Dyche, Baseline Consulting/DataFlux.
Part 2 was Timo Elliot, SAP at http://www.decisionstats.com/timo-elliott-on-2012/ and Part 1 was Jim Kobielus, Forrester at http://www.decisionstats.com/jim-kobielus-on-2012/
Ajay: What are the top trends you saw happening in 2011?
Well, I hate to say I saw them coming, but I did. A lot of managers committed some pretty predictable mistakes in 2011. Here are a few we witnessed in 2011 live and up close:
1. In the spirit of “size matters,” data warehouse teams continued to trumpet the volumes of stored data on their enterprise data warehouses. But a peek under the covers of these warehouses reveals that the data isn’t integrated. Essentially this means a variety of heterogeneous virtual data marts co-located on a single server. Neat. Big. Maybe even worthy of a magazine article about how many petabytes you’ve got. But it’s not efficient, and hardly the example of data standardization and re-use that everyone expects from analytical platforms these days.
2. Development teams still didn’t factor data integration and provisioning into their project plans in 2011. So we saw multiple projects spawn duplicate efforts around data profiling, cleansing, and standardization, not to mention conflicting policies and business rules for the same information. Bummer, since IT managers should know better by now. The problem is that no one owns the problem. Which brings me to the next mistake…
3. No one’s accountable for data governance. Yeah, there’s a council. And they meet. And they talk. Sometimes there’s lunch. And then nothing happens because no one’s really rewarded—or penalized for that matter—on data quality improvements or new policies. And so the reports spewing from the data mart are still fraught and no one trusts the resulting decisions.
But all is not lost since we’re seeing some encouraging signs already in 2012. And yes, I’d classify some of them as bona-fide trends.
Ajay: What are some of those trends?
Job descriptions for data stewards, data architects, Chief Data Officers, and other information-enabling roles are becoming crisper, and the KPIs for these roles are becoming more specific. Data management organizations are being divorced from specific lines of business and from IT, becoming specialty organizations—okay, COEs if you must—in their own rights. The value proposition for master data management now includes not just the reconciliation of heterogeneous data elements but the support of key business strategies. And C-level executives are holding the data people accountable for improving speed to market and driving down costs—not just delivering cleaner data. In short, data is becoming a business enabler. Which, I have to just say editorially, is better late than never!
Ajay: Anything surprise you, Jill?
I have to say that Obama mentioning data management in his State of the Union speech was an unexpected but pretty powerful endorsement of the importance of information in both the private and public sector.
I’m also sort of surprised that data governance isn’t being driven more frequently by the need for internal and external privacy policies. Our clients are constantly asking us about how to tightly-couple privacy policies into their applications and data sources. The need to protect PCI data and other highly-sensitive data elements has made executives twitchy. But they’re still not linking that need to data governance.
I should also mention that I’ve been impressed with the people who call me who’ve had their “aha!” moment and realize that data transcends analytic systems. It’s operational, it’s pervasive, and it’s dynamic. I figured this epiphany would happen in a few years once data quality tools became a commodity (they’re far from it). But it’s happening now. And that’s good for all types of businesses.
Jill Dyché has written three books and numerous articles on the business value of information technology. She advises clients and executive teams on leveraging technology and information to enable strategic business initiatives. Last year her company Baseline Consulting was acquired by DataFlux Corporation, where she is currently Vice President of Thought Leadership. Find her blog posts on www.dataroundtable.com.