Russia’s Largest Bank Conducts $12 Billion Transaction Using Smart Contracts

Shunning blockchain and smart-contracts for years, financial institutions are finally waking up to the potential that decentralized technologies hold, with a flurry of government and corporate companies making moves into the sector.

Mammoth Transaction, Minimal Traction

According to an official release on May 16, 2018, Russia’s largest bank, Sberbank, recently utilized a Hyperledger Fabric-based smart-contract system to issued rouble-backed bonds for a transaction.

The development saw Sberbank CIB, the investment division of Sberbank, the National Settlement Depository (NSD), and MTS, a Russian telecom operator, come together to conduct a RUB 750 billion bond issue, equivalent to $12.15 billion.

Historically, the commercial bond market has been limited to exclusive players, with most bond exchanges occurring Over-The-Counter (OTC) instead of digitally. With this successful transaction, Russian authorities are undeniably appraising the use of a robust blockchain-based system, which can dispose of the traditional methods and arguably provide a faster, safer exchange system.

As per the report, the parties also tested a blockchain-based delivery versus payment settlement model, that allowed for the simultaneous transfer of money and securities.

Authorities Fully Impressed with Blockchain’s Potential

Using the latest version of the Hyperledger Fabric, the participants were able to fully track every detail of the transaction, from placement to the issuer’s performance of its obligations to investors, and finally, its settlement in roubles.

Hyperledger Sawtooth: Blockchain for the enterprise

— Hyperledger (@Hyperledger) May 17, 2018


All parties, the investor, issuer, and depository were able to access the decentralized platform during the transaction. Additionally, the operation was conducted in full confidence and knowledge of Russia’s legal authorities, in line with current standards.

The parties noted the impressive features of using a blockchain system, particularly its wholly digital nature. In addition, the participants were pleased to track their transaction, exchange any required documents, and have a significantly faster bond exchange process.

Speaking about the development was Igor Bulantse, Senior Vice President of Sberbank CIB:

“This MTS bond issue not only allowed us to confirm the reliability, efficiency and secure nature of the blockchain platform and carry out complex structured transactions involving securities, but also demonstrated the potential that this technology has to develop Russia’s digital economy.”

Echoing his thoughts was Andrey Kamensky, Vice President of MTS, who praised the system’s speed, ease-of-use, and transparency. Additionally, Kamensky placed full-faith in smart contracts, and said that “MTS will continue using blockchain.”

Finally, the Chairman of Russia’s NSD, Eddie Astanin, added his views on the process:

“The deal with Sberbank and MTS was the first of its kind, and shows that blockchain is a mass-use technology that provides confidentiality and speed when working with securities.”

Astanin believes the transaction is a “vital step” towards the creation of blockchain-based systems for the financial markets.

With 145 million customers in 20 countries, Sberbank is undisputedly a major financial player, and its successful usage of a smart-contract system may prove to be the litmus test for many global developments to come.


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Re: EMC Xtremio Performance


We are using the EMC Xtremio Performance queries to gather statistics. Currently we are using below REST API to get the statistics.


However this gives us huge amount of data causing. So to filter this further, I am trying to put time limit instead of getting data for an hour. However below REST API is coming back with “Response code 400, msg : Bad Request”

https://xio01/api/json/v2/types/performance?cluster-name=xio176&entity=Volume&granularity=raw&from-time=2018-05-11 13:26:19&to-time=2018-05-11 13:41:19

https://xio01/api/json/v2/types/performance?cluster-name=xio176&entity=Initiator&granularity=raw&from-time=2018-05-11 13:26:19&to-time=2018-05-11 13:41:19

https://xio01/api/json/v2/types/performance?cluster-name=xio176&entity=Target&granularity=raw&from-time=2018-05-11 13:26:19&to-time=2018-05-11 13:41:19

Also same command with object list does not return any data either for initiator or volume. It works for Target.

E.g below works

https://xio01/api/json/v2/types/performance?cluster-name=xio176&entity=Target&granularity=raw&from-time=2018-05-11 13:26:19&to-time=2018-05-11 13:41:19&obj-list=X1-SC1-target1&obj-list=X1-SC1-target2&obj-list=X1-SC1-target3&obj-list=X1-SC1-target4

But below does not

https://xio01/api/json/v2/types/performance?cluster-name=xio176&entity=Initiator&granularity=raw&from-time=2018-05-11 13:26:19&to-time=2018-05-11 13:41:19&obj-list=mdm1dbq1_0&obj-list=mdm1dbq1_0s&obj-list=mdm2dbq1_1&obj-list=mdm1dbq1_1s

Also the commands seem to be very slow too, e.g below command is taking 2 hrs and comes back with “Response code 400, msg : Bad Request”

https://xio03/api/json/v2/types/performance?cluster-name=xio066&entity=Initiator&granularity=raw&from-time=2018-05-11 15:30:14&to-time=2018-05-11 15:45:14&obj-list=vplex3_e2_b1_fc03&obj-list=vplex3_e3_a1_fc01&obj-list=vplex3_e3_a1_fc03&obj-list=vplex3_e3_b1_fc01

Please can you help why time limit is not working in certain cases? And how can I limit the data.


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PAC file query

I need a solution


We have explicit deployment , so it is not possibel to bypass any website from proxy.

So we are planning to deploy PAC in our environment. In our network, Internal DNS is unable to resolve the external website will bypassing  in PAC file will work ?



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Problem in Symantec DLP

I need a solution


I install Symantec dlp for my organization,
Oracle and enforce is the same virtual machine and my Detection Server is sperate virtual machine

I create a policy that can detect English Character in pdf and can prevent it print

but I unable to create a policy can detect none-English character and prevent it from print

what can I do?

my language is Arabic

best regard



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7 Machine Learning Best Practices

Netflix’s famous algorithm challenge awarded a million dollars to the best algorithm for predicting user ratings for films. But did you know that the winning algorithm was never implemented into a functional model?

Netflix reported that the results of the algorithm just didn’t seem to justify the engineering effort needed to bring them to a production environment. That’s one of the big problems with machine learning.

At your company, you can create the most elegant machine learning model anyone has ever seen. It just won’t matter if you never deploy and operationalize it. That’s no easy feat, which is why we’re presenting you with seven machine learning best practices.

Download your free ebook, “Demystifying Machine Learning

At the most recent Data and Analytics Summit, we caught up with Charlie Berger, Senior Director of Product Management for Data Mining and Advanced Analytics to find out more. This is article is based on what he had to say.

Putting your model into practice might longer than you think. A TDWI report found that 28% of respondents took three to five months to put their model into operational use. And almost 15% needed longer than nine months.

Graph on Machine Learning Operational Use

So what can you do to start deploying your machine learning faster?

We’ve laid out our tips here:

1. Don’t Forget to Actually Get Started

In the following points, we’re going to give you a list of different ways to ensure your machine learning models are used in the best way. But we’re starting out with the most important point of all.

The truth is that at this point in machine learning, many people never get started at all. This happens for many reasons. The technology is complicated, the buy-in perhaps isn’t there, or people are just trying too hard to get everything e-x-a-c-t-l-y right. So here’s Charlie’s recommendation:

Get started, even if you know that you’ll have to rebuild the model once a month. The learning you gain from this will be invaluable.

2. Start with a Business Problem Statement and Establish the Right Success Metrics

Starting with a business problem is a common machine learning best practice. But it’s common precisely because it’s so essential and yet many people de-prioritize it.

Think about this quote, “If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.”

Now be sure that you’re applying it to your machine learning scenarios. Below, we have a list of poorly defined problem statements and examples of ways to define them in a more specific way.

Machine Learning Problem Statements

Think about what your definition of profitability is. For example, we recently talked to a nation-wide chain of fast-casual restaurants that wanted to look at increasing their soft drinks sales. In that case, we had to consider carefully the implications of defining the basket. Is the transaction a single meal, or six meals for a family? This matters because it affects how you will display the results. You’ll have to think about how to approach the problem and ultimately operationalize it.

Beyond establishing success metrics, you need to establish the right ones. Metrics will help you establish progress, but does improving the metric actually improve the end user experience? For example, your traditional accuracy measures might encompass precision and square error. But if you’re trying to create a model that measures price optimization for airlines, that doesn’t matter if your cost per purchase and overall purchases isn’t going up.

3. Don’t Move Your Data – Move the Algorithms

The Achilles heel in predictive modeling is that it’s a 2-step process. First you build the model, generally on sample data that can run in numbers ranging from the hundreds to the millions. And then, once the predictive model is built, data scientists have to apply it. However, much of that data resides in a database somewhere.

Let’s say you want data on all of the people in the US. There are 360 million people in the US—where does that data reside? Probably in a database somewhere.

Where does your predictive model reside?

What usually happens is that people will take all of their data out of database so they can run their equations with their model. Then they’ll have to import the results back into the database to make those predictions. And that process takes hours and hours and days and days, thus reducing the efficacy of the models you’ve built.

However, growing your equations from inside the database has significant advantages. Running the equations through the kernel of the database takes a few seconds, versus the hours it would take to export your data. Then, the database can do all of your math too and build it inside the database. This means one world for the data scientist and the database administrator.

By keeping your data within your database and Hadoop or object storage, you can build models and score within the database, and use R packages with data-parallel invocations. This allows you to eliminate data duplications and separate analytical servers (by not moving data) and allows you to to score models, embed data prep, build models, and prepare data in just hours.

4. Assemble the Right Data

As James Taylor with Neil Raden wrote in Smart Enough Systems, cataloging everything you have and deciding what data is important is the wrong way to go about things. The right way is to work backward from the solution, define the problem explicitly, and map out the data needed to populate the investigation and models.

And then, it’s time for some collaboration with other teams.

Machine Learning Collaboration Teams

Here’s where you can potentially start to get bogged down. So we will refer to point number 1, which says, “Don’t forget to actually get started.” At the same time, assembling the right data is very important to your success.

For you to figure out the right data to use to populate your investigation and models, you will want to talk to people in the three major areas of business domain, information technology, and data analysts.

Business domain—these are the people who know the business.

  • Marketing and sales
  • Customer service
  • Operations

Information technology—the people who have access to data.

  • Database administrators

Data Analysts—people who know the business.

  • Statisticians
  • Data miners
  • Data scientists

You need the active participation. Without it, you’ll get comments like:

  • These leads are no good
  • That data is old
  • This model isn’t accurate enough
  • Why didn’t you use this data?

You’ve heard it all before.

5. Create New Derived Variables

You may think, I have all this data already at my fingertips. What more do I need?

But creating new derived variables can help you gain much more insightful information. For example, you might be trying to predict the amount of newspapers and magazines sold the next day. Here’s the information you already have:

  • Brick-and-mortar store or kiosk
  • Sell lottery tickets?
  • Amount of the current lottery prize

Sure, you can make a guess based off that information. But if you’re able to first compare the amount of the current lottery prize versus the typical prize amounts, and then compare that derived variable against the variables you already have, you’ll have a much more accurate answer.

6. Consider the Issues and Test Before Launch

Ideally, you should be able to A/B test with two or more models when you start out. Not only will you know how you’re doing it right, but you’ll also be able to feel more confident knowing that you’re doing it right.

But going further than thorough testing, you should also have a plan in place for when things go wrong. For example, your metrics start dropping. There are several things that will go into this. You’ll need an alert of some sort to ensure that this can be looked into ASAP. And when a VP comes into your office wanting to know what happened, you’re going to have to explain what happened to someone who likely doesn’t have an engineering background.

Then of course, there are the issues you need to plan for before launch. Complying with regulations is one of them. For example, let’s say you’re applying for an auto loan and are denied credit. Under the new regulations of GDPR, you have the right to know why. Of course, one of the problems with machine learning is that it can seem like a black box and even the engineers/data scientists can’t say why certain decisions have been made. However, certain companies will help you by ensuring your algorithms will give a prediction detail.

7. Deploy and Automate Enterprise-Wide

Once you deploy, it’s best to go beyond the data analyst or data scientist.

What we mean by that is, always, always think about how you can distribute predictions and actionable insights throughout the enterprise. It’s where the data is and when it’s available that makes it valuable; not the fact that it exists. You don’t want to be the one sitting in the ivory tower, occasionally sprinkling insights. You want to be everywhere, with everyone asking for more insights—in short, you want to make sure you’re indispensable and extremely valuable.

Given that we all only have so much time, it’s easiest if you can automate this. Create dashboards. Incorporate these insights into enterprise applications. See if you can become a part of customer touch points, like an ATM recognizing that a customer regularly withdraws $100 every Friday night and likes $500 after every payday.


Here are the core ingredients of good machine learning. You need good data, or you’re nowhere. You need to put it somewhere like a database or object storage. You need deep knowledge of the data and what to do with it, whether it’s creating new derived variables or the right algorithms to make use of them. Then you need to actually put them to work and get great insights and spread them across the information.

The hardest part of this is launching your machine learning project. We hope that by creating this article, we’ve helped you out with the steps to success. If you have any other questions or you’d like to see our machine learning software, feel free to contact us.

You can also refer back to some of the articles we’ve created on machine learning best practices and challenges concerning that. Or, download your free ebook, “Demystifying Machine Learning.”


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