Free Azure Machine Learning? Yes, Please!

With Azure Machine Learning being released to General Availability this week (Feb 18th, 2015), more interesting news come to life.

There is a couple of (somewhat confusing) options to try and use AzureML. Better to be informed before you jump in and register your account with Azure…

AzureML Free Tier

With GA, Microsoft decided to release a free tier to make easy for you to try the service. The difference with the classic Azure trial is that you don’t need an Azure account for this (which requires a valid credit card).

Another difference is what you can do with this type of account: you’re not on trial time (one month, one year), but bound by other type of limitations such as: data storage (10GB), number of modules per experiment (100), max experiment duration (1 hour) and performance (throttled).

Still this is the best option if the only thing you want to do is to give AzureML a try, or even use it as a development environment before you move into production.

To use this, just go to and sign-in with your Microsoft Account.

Azure Free Trial

This is the classic Azure trial: you will be given 1 month and $200 that you can use to try any Azure service, including AzureML. It will require for you to register a new Azure account, and enter your credit card information.

AzureML Pay-per-use

After your one month trial expires, you can check the current prices here.

Different options for different goals

If you just want play and try some small experiments: Use the Free Tier. Most small experiments will be run just fine.

If you are ready to take your experiments to the next level, and release to production: Start with the Azure one month trial. After one month, you will be billed at the regular rates.

Dell XPS 13 2015 Review

I’ve just got a new Dell XPS 13 2015, and all I can say it good things about it. I’ve been a faithful Mac convert since 2004, but after 10 years, I feel its the right time to come back to the PC and Windows.

Even through all these years, I always kept working on Windows, MacOS seemed a more stable and uniform environment, but with Windows 8.1 and the coming Windows 10, I think Microsoft is really coming back. Besides, the quality of Ultrabooks in general now matches (if not surpasses) the ones from Apple.

Image taken from The Verge’s Dell XPS 13 review

Are there other Ultrabook options to consider?

I bought a Yoga 3 Pro earlier this year and ended up returning it after less than a week, because of its lousy performance. Don’t get me wrong, I loved the chassis and design in general, also the 2-in-1 factor seemed cool at the beginning. But honestly, couldn’t justify the machine being slow after just opening two or three tabs on IE, unacceptable.

Now, straight to the Dell XPS 13: this is the Ultrabook to have in 2015. I’ve been following the XPS 13 for a couple of weeks now, and it was nowhere to be found: neither Dell, Microsoft, BestBuy or any other online retailer had it in stock.

Screen: Touch screen or matte?

The first option you have to deal with is the screen: if you want a touch screen is around $100 more, but also the resolution is awesome: 3200×1800 (even higher than a MacBook Pro Retina Display). The only drawback is the glossiness… I love matte screens, I’m sure I will find a matte screen protector for this.

The brightness at its maximum is really good, also has an auto-brightness setting that works pretty well and saves you battery.

Final comment: Just go for the non-touch if you must, the real deal is the 3200×1800 QHD touch screen. The resolution is excellent.

i3, i5 or i7?

The processor is the second big decision to make: i3 is not an option for me (having discarded the Yoga 3 Pro for having a Core M, which is even better than the i3). The only real options are i5 vs i7. This was a tough call, as I found the i5 reduced $100, so the gap between these two was $300. Too much of a price difference just for a couple more GHz and cache. Honestly, don’t think the i7 is worth it, unless you plan to keep your computer for a long time.

SSD Space: 128GB, 256GB or 512GB

128GB is out of the question: you either get 256 or 512. If I would have found the 512 n stock I’d buy it, but 256GB was the only thing I could get. Besides, the good news is that (apparently) you can upgrade the storage. If not, you can add more storage via an SD card.

Where to buy? At the Microsoft Store of course!

There are lots of retailers that can sell you this, but your best bet is still the Microsoft Store. Their service is superb, comparable experience to what you get at an Apple Store. When I was at the store an still undecided between the i5 and i7, they didn’t try to upsell me straight to the i7, but walked me through the considerations they would have, and ended up recommending the i5. That’s really honest!

Other advantages are:

Signature Edition PCs: Your Windows is pre-installed by Microsoft and with no manufacturer adware, malware or bloatware. This is excellent, now that we’ve heard what just happened to Lenovo and its infamous Superfish.

Microsoft Complete for PCs: Kind of an extended-warranty, but at $129 it definitely makes sense! Apple charges around $300 for the same on their Macs. Whatever problem you have, you can go to the Microsoft store and they’ll fix it for you. It covers up to two damage incidents during the two year warranty, and they will give you a new PC for just $49. Really hope I don’t have to use it, but you never know..

Overall comments

I’m very happy with the Dell XPS 13, the non-bezel display is gorgeous, the keyboard is very comfortable and the performance of the i5 model is excellent. The portability is very similar to a MacBook Air 11 (and that is not a typo).

Overall, a very minimalistic machine with excellent performance and at a reasonable price.

Azure Machine Learning: Data Mining 2.0

Azure Machine Learning (aka AzureML) is one of the new products/services in this new bold world of ‘cloud first, mobile first’ that Microsoft is endeavouring. It helps you create predictive analytics from your data in a very quick and simple way, and easily integrate this with allyour applications. And you can do that armed just with your browser!

But I think I’ve heard about this before… Haven’t I?

Remember a couple of years ago everything was 2.0? Web 2.0 was the paradigm everyone swore by, adding ‘social’ and ‘services’ around all we already knew by then.

That is how I feel about Azure Machine Learning: it is a great, improved 2.0 version of the old Data Mining concept we’ve known for years (SQL Server implemented this with its SSAS Data Mining feature). Don’t take me wrong, I’m not saying that because this already existed one will quickly discard it. I think Microsoft took a page of its own book, and put a lot of thinking on how to bring that into 2015. And that is great!

Out with the old…

If you remember, Analysis Services Data Mining always had a couple of algorithms you can use:

  • Classification algorithms predict one or more discrete variables, based on the other attributes in the dataset.
  • Regression algorithms predict one or more continuous variables, such as profit or loss, based on other attributes in the dataset.
  • Segmentation algorithms divide data into groups, or clusters, of items that have similar properties.
  • Association algorithms find correlations between different attributes in a dataset. The most common application of this kind of algorithm is for creating association rules, which can be used in a market basket analysis.
  • Sequence analysis algorithms summarize frequent sequences or episodes in data, such as a Web path flow.

To use them you would create a model in SSAS, load data (with help provided by SSIS) to train the model, and then you can use them through DMX (Data Mining eXtension) queries. Doing DMX queries involved connecting to SSAS using native windows-only proprietary drivers and then sending these queries to get back your results.

… and in with the new!

The principle behind AzureML is pretty much the same. Couple of notorious diferences here:

– You don’t need SSAS: In fact, you don’t even need SQL Server at all: no database, no SSIS, no SSAS. This is a pure online service, born into and for the cloud. There’s been talks about bringing it to on-premise, but honestly I don’t think that is going to happen any time soon (and nobody would blink an eye either).

– Data loading and manipulation inside the tool: As mentioned before, you don’t need SSIS. Your expermient designer in AzureML has a workflow view that resembles SSIS in the sense that you have components to scrub and manipulate data before loading into your model. One less thing to worry about.

– No DMX or weird query languages to use: As this is a cloud service, the output of your model is a web service. Anybody (with the correspondingAPI key) can call it and make use of your model. This makes your model available and online-ready in really no time.

– Integration with R: R is ‘THE’ language to create models. In the old world, you could still create your own models using the SSAS Data Mining SDK (using C++ or C#) but they would still have to be compiled into native windows code, deployed, managed and available only through SSAS. Being able to take any R algorithm available and use as a component makes this very much open for experimentation.

– One click deployment to Azure: To deploy your old data mining model used to require creating some kind of component (or service) to wrap the SSAS DMX call. Deploying to the cloud is literally done in one click, and you are ready to go. There’s even boilerplate code provided for you to call the production-ready web service from C#, Python and R.

– Really low entrance barrier: No infrastructure setup, no licensing costs, no development tools setup. The only thing you need to do is register to the AzureML service online and pay for the processing cost when you run your model. That’s it!


AzureML is one of those products (services?) that makes me excited about the future of Business Intelligence. So easy to setup, work with and deploy that is kind of a crime not using it!

Now, this is still a 1.0 version of a product. Features that are still not there or missing:

– Heavy data encryption: Training models often involve highly sensitive / private data. Everybody requires a trusted and heavily encrypted transport for this data. This is where most of the asks are going to come from: people coming from the Enterprise world concerned about their data travelling through public networks.

– Easy model retrain: Model re training is something it should be done frequently. Once you train your model, you need to keep it up to date to respond to environment changes and also potential decreasing accuracy. There is no easy way to automate this right now.

– More algorithms: This is mitigated by the fact that you can infinitely expand by using R, but still this is where most of the grow will come from. Also, Microsoft recently bought Revolution Analytics, so I would expect more algorithms and features added.

Your next steps

If you’re interested in using AzureML, just register a new account (there’s a 1 month, $200 trial) and just start using it. Some resources you can use to start learning it are:


Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes

By: Roger Barga; Valentine Fontama; Wee Hyong Tok
Publisher: Apress
Pub. Date: November 26, 2014
Print ISBN-13: 978-1-484-20445-0
Pages in Print Edition: 188


– If you only have 5 minutes or less, watch this: Azure ML Overview: this is a great 5 minutes overview of what AzureML is.

– If you have one hour, watch this: Intro to Azure Machine Learning: The full product tour, with demos, from TechEd 2014.

If you have more time, you can start watching this YouTube video playlist.