Nordcloud Germany to sponsor Hamburg’s JeffConf

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Nordcloud Germany is going to be a proud sponsor of this year’s JeffConf in Hamburg, taking place next month on 16th February.

This will be an event focussed on everything Serverless, Machine Learning, AI, and Data Analysis, but we hope that a special highlight will be Nordcloud Senior Developer and Architect Arto giving a talk about Serverless WordPress.

Arto will be showcasing migrating a (semi-) high traffic WordPress site from a costly server to AWS Lightsail & server-side rendering React app on Serverless framework. On the new framework it will be faster and more secure, however, the customer won’t have to compromise on their favourite website/blog editor.

We are looking forward to meeting everyone and to talk to others in the Cloud industry about how you are using Serverless solutions right now, or, if not, then how they would benefit you and your business. We promise to share all of our best practice knowledge, so come and say hello to us at the Nordcloud stand!

Tickets are going fast, so sign up here to secure yours, or get in touch with our team here.

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State of AI for digital business in 2018

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One of the influential people in AI who I follow is Andrew Ng. In the past, he has headed AI functions at both Google and Baidu and co-founded Coursera. Last December he was back on the stage of MIT Technology Review conference EmTech discussing the present state of AI. I found his presentation very inspiring and picked the following insights for those who didn’t have the half an hour to listen to him.

What is AI now good at?

Andrew has for some time defined the capacity of current AI as follows:

Anything that a typical person can do in less than one second AI can learn. This is an imperfect rule, but holds pretty well.

Jobs and manual procedures which can be decomposed into these simple, constituent jobs can probably be automated in the near future.

Nowadays there are good examples of using AI to do market automation, loan decisions, speech recognition, and even to steer an autonomous vehicle. The technology behind the majority of these opportunities is “standard” AI, otherwise known as supervised learning.

99% of value created by AI comes out of supervised learning – mapping from A to B [identification, categorization].

The deep learning is the fancy new variant of AI repeatedly discussed in the media. Deep learning is finally improving and it provides superior performance in comparison to “old” AI technologies (SVM etc.) when the number of available data increases. Old learning solutions could not benefit from larger data sets, whereas neural networks can benefit from increasing datasets. The bigger the network, the more data can be poured in with a performance increment.

Andrew lists different techniques based on their current business impact

  1. Supervised learning
  2. Transfer learning
  3. Unsupervised learning
  4. Reinforcement learning

“Reinforcement learning PR excitement is largely disproportionate with its impact”

The most valuable thing for AI-based businesses is an exclusive data asset

Leading AI company don’t only have great data scientists, but unique data assets. Andrew says that data assets make AI-based businesses defendable in a competitive landscape. Although he has worked with leading search engines and knows intimately how they work, he would be unable to create a competitive product without similar sets of user data. To build a defensible business, a company must build a positive feedback loop that allows to accumulate more data from users.

Data assets allow leading web search companies to provide more relevant results.

What is an internet company and what is an AI company?

Andrew introduces the notion of an AI company, a digital business set apart by their unique power derived from utilisation of AI. But what defines this type of a company? Let us compare it to the picture of an internet company.

An Internet company is not just about selling stuff over the internet. Based on Andrew, the advantage of internet companies is to have distributed decision making which can’t depend upon centralized decision making (or the Hippo, cf. Lean). They do testing (AB) and have short cycle times and are able to ship product improvements frequently.

In comparison, an AI company is not just a company which uses neural networks on top of traditional technology products. AI companies do strategic data acquisition, which allows them to build defensible data-based business. They have unified data warehouses which allow fluid flow of data from application to application, across any superficial silos. They are good at spotting pervasive automation opportunities, including those under the one-second threshold.

New requirements for product management

To run an AI company or manage an AI-heavy product, visual representation of the new product is not enough. To deal with AI capabilities, product managers must meet AI developers in their terms, for instance, present annotated datasets which describe how the product should behave, in terms of matching A’s to B’s. Traditional specifications such as wireframes do not suffice when trying to crack this equation.

How to incorporate AI into a corporate structure

The final theme Andrew touches is upon is the integration of AI know-how in large organisations. First, he recognises that AI is not a mature capability. As such AI capabilities are currently best integrated as centralised AI teams which help the whole organisation to integrate AI functions (in a matrix fashion). Later on, when the practices and methods of AI work mature, individual business units may hire their own talent as has happened with UX and mobile developers, for instance.

“Common teams, common standard, company-wide platforms of AI”

Find out about our data-driven solutions delivering business intelligence here.

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AWS opens new availability zone in London

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AWS announced last week that they would be opening their 50th availability zone in London, giving an ever-growing customer base even more flexibility to develop ‘highly-scalable and ‘fault-tolerant’ applications.

The new London region has been given PSN (Public Services Network) assurance, which provides Public sector customers in the UK with an assured infrastructure to build their services. The PSN allows UK Public Service customers to move their UK-OFFICIAL data to the EU (London) Region, controlling the risks that accompany it.

For all customers, the ability to spread your applications over the third AZ allows you to decrease the risk profile of running your application on AWS, (spreading your application into the third AZ means that only 33% or your instance will fail if an AZ does, whereas previously this would have been 50%). The third AZ will also allow AWS to begin rolling out additional services which require three AZ’s to operate in the London region.

You can find a complete list of AWS Regions, services and prices on the AWS Global Infrastructure page.

If you would like to know more about AWS’s availability zones, please contact us. 

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Nordcloud partners with Microsoft to unleash the power of AI for Azure

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Nordcloud, Europe’s fastest growing public cloud provider and the Nordic market-leader has teamed up with Microsoft to boost the spread of AI for Azure across the continent.

Nordcloud helps customers to complete AI projects faster and at lower costs with the use of Microsoft Azure

AI projects with complex deep learning problems consume vast amounts of computing capacity and traditional hosting solutions are not sufficiently agile to meet this demand. Nordcloud has wide experience of working with the Azure platform and developing innovative services with and for its clients that use the power of AI. The company’s past projects have included helping customers with AI-related challenges such as recommendation engines, classification and natural language processing.

Microsoft’s Azure cloud computing service offers a fast-growing range of Platform Services for AI, machine learning and IoT development that can substantially boost businesses’ adoption of these technologies, including the potential for creating innovative, market-disputing business models, and for the revenue-boosting, cost-cutting benefits they bring.

The entire Azure AI stack in use

Nordcloud allows customers to make use of the entire Azure AI stack, creating scalable intelligent services and adding cutting-edge smart features to existing solutions in a fast, agile manner with the total cost of ownership benefits only cloud services can deliver.

AI is set to be the main engine for the next generation of digital services that will deliver more data-driven decision making, better customer experiences and new problem-solving paradigms for challenges that are beyond the scope of traditional programming.

Many of the tools available come ‘pre-taught’, in other words, they’ve already been fed vast quantities of data so their machine learning is already advanced. This cuts the time it takes to fine-tune tools for specific applications.

AI creates competitive advantage to early adopters

“Nordcloud has already delivered AI projects for its clients,” says Microsoft’s Cloud (for Finland) and Enterprise Business Lead Antti Alila. “This is important to stress: AI is here and now and helping businesses innovate and gain a competitive edge.” “Nordcloud has partnered with Microsoft for jointly deploying Azure AI-based solutions for our enterprise customers.”

A recent report by the global consultancy McKinsey highlighted the competitive advantage created by early adopters of AI and the growing gap between them and companies that had failed to grasp the opportunities.

“Of the more than 3000 customers McKinsey surveyed only one in five is using AI” says Nordcloud’s CEO Jan Kritz. “Fewer than one in ten is using machine learning, which is the area seeing the greatest investment into AI. Moreover, it’s the bigger companies that are investing most readily.”

By leveraging Azure’s world-class AI infrastructure and services, Nordcloud aims to help a much wider range of businesses to embrace the potential of artificial intelligence. Nordcloud and Microsoft will be working together with Nordcloud’s clients to spot opportunities to build the next generation of cost-effective, scalable, intelligent AI-powered digital solutions.

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GDPR: The drought for your data lake

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Data lake – we’ve seen it mentioned in the IT news headlines

The new hope of IT organisations to enable their business units with actual content and valuable insights, rather than just offering servers and empty storage. Almost all companies of size and renown have embarked on this new journey and are building data lakes to sail upon them. Or maybe not?

Recent concerns raised around this data lake use case, especially since the dawn of GDPR has made people rethink the share-everything-with-everyone mindset behind these lakes.  Also, it has raised the matter of data ownership, retention, deletion and correction. Most data lake scenarios are viewed from a primarily technical perspective because that is where the idea comes from. Inevitably, and luckily not AFTER the actual release of many of these lakes into production, the legal and compliance departments have woken up.

As we are involved in quite a number of these projects, we wanted to share the main aspects to keep in mind when building your data lake with GDPR in mind. So here we go:

Employee Data

You can argue of course that once you work for a company, your data belongs to them. But it’s not that easy. First of all, this is a concept that may or may not apply in some countries. Secondly, the concept of storing employee data is one thing, the idea to use it for analytical purposes may require the employee’s consent. And that is where you run into challenges. For example in Germany, companies all have one thing in common: they have extensive employee data and are rarely allowed to use it to their advantage because of the current legislation. Through the GDPR introduction, this type of scrutiny will be imposed on all EU countries and hence become a challenge for many more businesses.

Customer Data

This should be the most traditional use case in data privacy and protection and is one of the key reasons why the GDPR debate is so viral and vibrant these days: it concerns almost all companies. There’s a lot to discuss around this particular point, but one specific aspect that is of some note is the “Right to Explanation”. If you use machine learning on user data, GDPR regulations state that “meaningful information about the logic” behind machine learning models must be made available to users.

Many machine learning models are black boxes, but the type of data used to train them should be made clear to users so that they can make an informed decision to opt out. Users should, at all times be offered the option not to have their data used as part of machine learning and artificial intelligence applications.

Device Data

With IoT and Connected-X, we all feel like we can’t really participate in modern society without sacrificing some of your privacy tied to devices and gadgets. From a legal perspective, the providers of services ask for your consent when you install mobile apps or sign up for a SaaS-type service. This is the easy part. Now, imagine you are a car manufacturer, who could gain plenty of insights and competitive advantage through collection of device/car data in that field, and has all the technology to make that happen but is not allowed to do it.

In actual fact, this is an issue. People used to buy cars without signing a data privacy agreement. Recently, privacy agreements have become an actual necessity in order to even operate the connected car services. As a business, you have to always keep in mind that just because it is device data, does not mean you can harvest and use the data for your advantage. There is a human being or an organisation behind that device who’s using it. You need their consent, otherwise, no data can be legally processed.

Prevent the Drought

So does that mean there is a chance your data lake could dry out very soon? Don’t worry, here are some relatively easy ways to address this challenge:

Anonymisation of data is one way to solve this. This means that the data is being stripped of all potential identifiers to human beings and actual end-user facing devices and collects statistical data for very specific use cases. If that isn’t possible in your given use case it’s a different story. But it must become an inherent part of all the data processing in the solution you design and isn’t bound to the data lake at all – it sits within your application.

Encryption of data can be a very easy and elegant way to address the challenge without even building much of a solution into your cloud platforms. Most of the public cloud platforms provide several mechanisms that allow encryption on various layers of the platform at no additional cost. The great thing is you can automate remediation actions based on alerts if any kind of data is being stored unencrypted into a cloud. Non-compliance to this standard is practically impossible.

Data Management Practice setup is a general requirement in order to make sure you have full visibility and (access) control over all the data your company holds, manages or has access to. Also, it is important to run a proper metadata scheme across all the data types as complete as possible so it is searchable and can be clustered.

There are many more use cases in the Big Data field that require your attention, but I hope we’ve made our point. Just because you have data (in your lake), does not necessarily mean you can actually use it. GDPR demands that you have customer and employee consent, before using any form of data collected. At Nordcloud, we combine strong expertise in Big Data, Machine Learning and IoT field with years of AWS and Azure project delivery, all wrapped up in a deep awareness of data protection and security.

Please feel free to reach out to us if you think the above sounds familiar but perhaps too complex to tackle on your own. We’re here to help.

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“SAAS over PAAS over IAAS over my own data centre” – some thoughts

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This is a headline from a quote which came from my friend, a C-level executive in one of the biggest corporations in Finland. You might wonder why, as a representative of a company that doesn’t particularly preach cloud*, and doesn’t provide a full set of services for managing key SaaS components like ERPs or CRMs, I would bring up the topic.

Naturally, I want to talk about the role of custom-built software running on PaaS. I agree that when SaaS does the trick, you should choose it over a custom PaaS-application.

Let’s start with the easy part: Why is PaaS superior to IaaS?

There are a number of reasons:

  • Capacity is dirt cheap
  • Labour is expensive
  • Capacity is getting cheaper rapidly
  • Labour costs are increasing.

You should focus on saving labour, and this is fundamentally what using PaaS means. Your software development projects will ultimately end up costing less. Finding the cheapest possible capacity should save you a couple of hundred euros/pounds/dollars per month. On the other hand, finding the most efficient development platform saves you months in development costs and crucially, in time to market.

You might find this irrelevant if you want to use SaaS for everything, but unfortunately, you can’t. There might be a service for each of your needs, but how are you going to win overall in the marketplace?

Every company today runs their business processes digitally. When you use SaaS, you’re essentially using the process the vendor has defined, which in a lot of cases are world-class. Using these processes ensures you’re competitive, but by definition, they are not unique and you can’t build competitive advantage with them. When you look at your strategy and understand how are you going to be different from your competitors, you’ll understand when and where you’re going to need PaaS.

For me, this makes PaaS somewhat superior to SaaS. The things we develop run at the core of our customers’ strategy. We make a real impact. Ultimately, a partner such as Nordcloud will help you win.

There’s enough market demand with the believers to spend time on educating the non-believers (!)

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