Artificial intelligence in cloud computing

Artificial intelligence in cloud computing

Artificial intelligence is now used almost everywhere it is necessary and possible - so AI is also an interesting solution in the cloud.

Cloud computing enables users to store and manage data efficiently while offering benefits such as data security, encryption, regular backups and hosting of cloud applications. Combine these structures with artificial intelligence (AI) and you have the ideal conditions for machine learning (ML): large data sets that can be applied to algorithms. The more data fed into a model, the better the predictions and the higher the accuracy.

When merging AI and cloud computing, it is important that it not only enables the user to store data, but also to analyze it and draw conclusions. Chatbots are a now common example: their AI-based software simulates conversations with. Cloud-based services store enormous amounts of data, which the chatbots can use to learn and grow.

Those aiming for such a fusion of AI and cloud computing should note that advanced computational methods require a particularly powerful combination of CPU and GPU. Cloud service providers such as Centron enable their customers to perform such processes by providing suitable virtual machines as part of specific IaaS (Infrastructure-as-a-Service) offerings. Such services can also help with the processing of predictive analyses, among other things.


  • Data storage. All data must be stored and securely encrypted. There are certain rules that specify that cloud service may not be used.
  • AI Security. Encryption, firewalls and security protocols of software, hardware and data must be considered.
  • Integration. It must be clarified whether (and how) AI applications can be integrated into existing applications or systems.


  • Increased data security. There are already several AI-based network security products on the market to mitigate potential data breaches, close security gaps, prevent data theft, and prevent accidental loss or corruption of stored data.
  • Savings. Companies can use AI to leave the traditional data center and move to the cloud - where storage is only purchased when it is needed.
  • Reliability. Cloud computing services are always available. Even in case of damage or problem with the system, it is easily accessible from other servers.
  • Agile development. The flexibility of cloud computing enables shorter development cycles.
  • Redesign of the IT infrastructure. The demand for an optimized working environment has never been greater. Cloud computing allows companies maximum flexibility and scalability.


  • Data privacy. Companies should urgently create privacy policies and protect all data when using AI in cloud computing.
  • Connectivity concerns. The systems require a permanent Internet connection. If this is too slow, the advantages of cloud-based algorithms for ML quickly become invalid.
  • Error probability. Working with AI currently still holds enormous potential for error. Trust and control must be built up.
  • Conclusion

    AI helps IT teams work deeper and change IT infrastructure quickly by providing automation and other capabilities. However, there is a lot to consider when implementing the algorithms as well as managing the systems. To avoid problems here, companies should either build up appropriate expertise or buy it in in the form of specialists or experienced service providers.

    Source: Cloud Infrastructure Services Ltd

How to become a Data-Driven Company?

How to become a Data-Driven Company?

Data-driven companies use their data to unlock new opportunities and possibilities for their business. We show you how you can develop your company into a Data-Driven Company.

Big Data is multifaceted and can mean big benefits for businesses. Better information leads to better decisions and optimal results. The value companies derive from Big Data depends on their ability to build a solid digital foundation and ultimately become data-driven organizations.

Glen Hopper, CFO of Sandline Global and author of Deep Finance, has spent the last two decades helping startups prepare for funding or acquisition. From this experience, he has identified six steps to help companies with their digital transformation.

6 steps to a Data-Driven Company

Step 1️⃣

Implement systems for data collection and processing. Hire appropriately qualified employees or train your existing talent. (Investment in systems and human resources)

Step 2️⃣

Make sure you collect and summarize all available data from your company and about your company. Give sufficient priority to the employees who are to build this knowledge base.

Step 3️⃣

Take stock of your descriptive statistics. Where do the statistical categories correlate? What trends do you see and how can you share them with the entire workforce? Clarify what you already know.

Step 4️⃣

What does your knowledge gained in step 3 mean? Move on to prescriptive analysis and use your collected data to make predictions and recommendations for the future. Only in this step does information become knowledge.

Step 5️⃣

Different team members may see the same data in different ways. A transparent approach prevents unhealthy competition and promotes collaboration and accountability. By "crowdsourcing" decision-making power, your organization can benefit from better decisions overall.

Step 6️⃣

Effektive Analysen sind nur dann möglich, wenn jeder im Unternehmen den datengesteuerten Ansatz akzeptiert hat. Every team member must therefore be able to trust that the data is correct and provides a complete picture of the company's situation. For this, verification mechanisms are essential to continuously ensure that the information collected is "real".


So, on the way to becoming a Data-Driven Company, it is crucial to build processes and teams that collect relevant data. They must further make this data available to all stakeholders and enable the relevant colleagues to draw conclusions from it, which in the best case scenario will lead to productive and fruitful business decisions.

Source: Glen Hopper via Forbes


More exciting blog posts from the "Big Data" area:

Data Lake vs. Data Warehouse

Data Lake vs. Data Warehouse

When is a data warehouse worthwhile and when would a data lake make more sense? We show what companies should look for when making their decision.

We have already explained in previous blog posts exactly what is hidden behind the terms data lake and data warehouse. Both are capable of storing large amounts of information and making it available for analysis. However, data lake (DL) and data warehouse (DWH) differ fundamentally in their concepts and the way they store data.

Deciding when to use one or the other depends on what you intend to do with the data. Below, we contrast DL and DWH to help you make your decision.

Gegenüberstellung von Data Lake und Data Warehouse

Data warehouses bring together data from different sources and convert them into formats and structures that enable direct analysis. Data warehouses can process large amounts of data from different sources. As a rule, key figures or transaction data are stored in the DWH. Unstructured data (e.g. images or audio data) cannot be stored and processed. Using a DWH is recommended when companies need analytics that draw on historical data from various sources across the enterprise.

Data Lakes ingest data from different sources in its original format and also store it in an unstructured manner. It doesn't matter if the data is relevant to later analyses. The data lake does not need to know the type of analyses to be performed later in order to store the data. Searching, structuring or reformatting only takes place when the data is actually needed. Thus, a data lake is more flexible and can therefore be used well for changing or not yet clearly defined requirements.

Sources: Oracle & BigData-Insider


More exciting blog posts from the "Big Data" area:

What is a "data warehouse"?

What is a "data warehouse"?

A data warehouse stores current and historical data for the entire company. We show you what it can do beyond that and what its advantages and disadvantages are.

The term "data warehouse" refers to a type of data management system that is used to enable and support business intelligence activities (especially the execution of queries and analyses). Data warehouses often contain large amounts of historical data.

A data warehouse centralizes and consolidates large amounts of data from various sources, such as application log files and transactional applications. Here, for example, you can see all the databases that centron ccloud³ supports. Its analytics capabilities help companies derive valuable business insights from their data to improve decision making. Over time, a historical data set is created that can be of tremendous value.

Elements of a typical data warehouse
  • relational database (for storage and management of data)
  • Extraction, loading and transformation solution (to prepare the data for analysis).
  • Statistical analysis, reporting and data mining capabilities
  • Client analysis tools (for visualization and presentation of data)
  • More sophisticated analytic applications that generate actionable information (through algorithms or artificial intelligence) or enable further data analysis (through graphical or spatial functions)
Pros and cons of a data warehouse

➕ Delivers advanced business intelligence
➕ ensures data quality and consistency
➕ saves time and money
➕ enables tracking of historical data
➕ provides higher return on investment (ROI)

➖ requires additional reporting
➖ May limit flexibility in handling data
➖ May lead to data privacy concerns
➖ May cause high implementation costs

Sources: Oracle & CIO


More exciting blog posts from the "Big Data" area:

10 adjusting screws for a successful cloud migration

10 adjusting screws for a successful cloud migration

Cloudification occupies medium-sized companies like hardly any other IT topic. We show you which parameters need to be taken into account during planning and implementation.

In collaboration with various experts, our colleagues at gridscale have worked out the 10 levers for successful cloud migration in SMEs. We can only endorse the resulting white paper and would therefore like to give you an overview of the key points below. You can request the complete study free of charge here.

The 10 adjusting screws for a successful cloud migration

There is no one right way when it comes to the cloudification of midmarket IT. The following merely highlights various parameters that need to be taken into account when planning and implementing cloudification - including the associated challenges and options for action.

Strategic adjusting screws

1. Need for cloudification
Cloudification of internal IT makes sense in principle in order to be able to guarantee a sufficient level of fail-safety, scaling, speed and flexibility. However, cloudification should not be pursued at any price. The decisive factor is the extent to which the company in question succeeds in finding an adequate solution for its current challenges. Cloudification should therefore be driven forward pragmatically and with a sense of proportion.

2. Cloudification strategy
Companies should be aware that the range of cloud models is diverse - a thorough analysis of the application landscape is essential for a sound cloudification strategy. In addition, the cloudification strategy should be derived from the company's business and IT strategy. A deep understanding of the company's own applications and their importance for business operations is therefore also essential.

3. Dealing with public clouds
A complete shift of all workloads to a public cloud seems neither economically sensible nor feasible in the short term for most SMEs. However, private cloud solutions generally lag behind public cloud platforms in terms of performance, scaling and innovation.
If companies enter into a business relationship with a hyperscaler, one of the large cloud service providers, they have to reckon with strong lock-in effects and, as a consequence, with major dependencies. Therefore, it should be checked in advance exactly which workloads are to be moved to a public cloud and to what extent.
In terms of provider independence, it is worth taking a closer look at the public cloud provider market. In addition to the international hyperscalers, numerous providers have now established themselves that are trying to differentiate themselves by supporting muli or hybrid cloud scenarios.

Technical adjusting screws

4. Creating conditions
Cloudification of internal data center IT first requires a transformation of the application and server landscape and associated processes. Techniques must be aligned to the extent that Rz operations can be managed in a value-added, cost-optimized manner and as close as possible to cloud methods. Of course, this means immense effort, which is why short-term and cost-effective cloud transition solutions seem tempting. In the longer term, however, these can entail risks and thus additional expense.

5. Technical planning
Virtualization of computing capacities is a good starting point, but it is far from being a private cloud. In addition to the data center expansion, technical dependencies such as network or security requirements must also be taken into account. Integrated management is also essential to ensure high performance and scalability of the overall system - if possible, cross-platform so that hybrid or omni-cloud scenarios can be supported and external providers can be effectively controlled. If necessary, IoT strategies and the integration and management of edge components must also be taken into account in cloud planning.
The next logical step in the direction of cloudification could be understood as the implementation of a so-called hyper-converged infrastructure (also Hyper Converged Infrastructure or HCI for short). Such a software-defined architecture, which integrates server, storage and network components as well as virtualization and management software in one system, offers great advantages in terms of data management, scalability, availability, among other things. However, HCIs are associated with considerable investments and are often oversized for medium-sized companies. Hosted or managed private clouds provided externally represent a more cost-effective alternative.

6. Managed Cloud
As described, the effort required to set up and operate a private cloud is enormous. Outsourcing options in the private cloud environment should therefore be seriously considered and carefully examined. The selection of experienced service providers and suitable offers is essential here.
One option would be to procure Managed Private Cloud Services largely via an external service provider. Alternatively, individual components could either be procured directly in the as-a-service model or implemented internally and left to operate in the managed service model. The second approach offers more flexibility, control and possibly also cost advantages - however, it also requires significantly greater expertise on the part of the company than the first approach.

Cultural and organizational adjusting screws

7. Repositioning of the IT
Successful cloudification must not only be viewed from a strategic and technical perspective - it must also be lived by the employees. Flexible structures and agile methods can only develop their effect if they are accepted by people, properly classified and used in a targeted manner. Therefore, sufficient time and resources should be planned to actively promote and accompany the change in the minds of the workforce.

8. Realignment of processes
Cloud technologies force new methods and ways of working that meet existing organizational structures in the company, which also serve a purpose and cannot simply be replaced. Pragmatic solutions must be found here to reconcile both sides. Process reengineering is usually unavoidable - but this should by no means be seen as a one-off project. Rather, in view of the complexity of the topic and the dynamic cloud development, it is necessary to establish a continuous improvement process in the company.

9. Design of the external cooperation
As already explained, cloudification is usually accompanied by increased use of external services. The basis for this is a clarification of responsibilities; the goal should be a long-term partnership at eye level.
The more responsibility is transferred to service providers, the more they become strategic partners. To enable them to perform this role, it makes sense to involve them in internal communication and decision-making processes.
The more comprehensive the outsourcing, the greater the dependency of the company on the performance of the partner. This can only be prevented by the company itself assuming the role of consultant - which, however, requires a high level of competence in the cloud area. Additional resources are needed for cloud sourcing and outtasking as well as for active (multi-) provider management.

10. Human Resources Development
In the course of cloudification, the areas of responsibility in IT are changing. The specific demands placed on employees depend on the type of cloud or outsourcing model the company has chosen.
If the majority of the work clouds are to be provided in-house via a private cloud, additional personnel resources are required who must have the appropriate specialist knowledge - and are therefore difficult to recruit.
If (managed) cloud services are to be procured predominantly from external partners, employees are needed who are able to think and act holistically: They must anticipate the impact of their work on the different areas.

Source: gridscale

What is a "data lake"?

What is a "data lake"?

strong>A data lake is a central repository in which all structured and unstructured data can be stored to any extent. We will show you what this means exactly and what the advantages and disadvantages are.

The term data lake describes a very large data store that holds data from a wide variety of sources. The special feature compared to normal databases is that a data lake stores the data in its original raw format. This can be both structured and unstructured data - it does not need to be validated or reformatted before storage. Data is not structured or reformatted, if necessary, until the data in question is needed. In this way, the data lake can be fed from a wide variety of sources and ideally used for flexible analyses in the Big Data environment.

The concept of the data lake is supported by many frameworks and file systems for Big Data applications as well as by the distributed storage of data. For example, data lakes can be implemented with the Apache Hadoop Distributed File System (HDFS). Alternatively, data lakes can also be implemented with cloud services such as Azure Data Lake and Amazon Web Services (AWS).

Requirements for a Data Lake

In turn, to meet the requirements of applications built on top of the information, a data lake must meet the following requirements:

  • It must be possible to store a wide variety of data or data formats in order to avoid distributed data silos.
  • Common frameworks and protocols of database systems and database applications from the Big Data environment are to be supported in order to enable the most flexible use of the data.
  • The following measures must be taken to ensure data protection and data security: role-based access control, data encryption, and mechanisms for backing up and restoring data.

Advantages and disadvantages of a Data Lake

➕ More meaningful and in-depth analyses thanks to the large amount of information provided
➕ Fast storage operations by storing data in its raw format (without prior structuring or reformatting).
➕ Low requirements in terms of computing power, even for storing large amounts of data
➕ No restriction of the analysis possibilities (due to the inclusion of all data)

➖ High requirements in terms of data protection and data security (the more data and the more interrelationships, the more in need of protection)

Source: BigData-Insider

Tech Trend #10: Sustainable Technology

Tech Trend #10: Sustainable Technology

Sustainable technology is a framework of digital solutions that creates opportunities in three critical business areas at once. Learn more about this tech trend and how you can address it in your business.

For the coming years, the U.S. research and consulting firm Gartner sees four priorities that companies can address with the help of various technology trends. On this basis, Gartner names and categorizes the 10 most important strategic technology trends for 2023. (centron reported)

As the last of these ten tech trends, we would like to present sustainable technologies to you in this article. Gartner assigns this trend to the priority "Pursuit of sustainable technology solutions".

Sustainable Technology

According to Gartner, technology delivery alone will no longer be sufficient in 2023. Sustainable technology is a framework of digital solutions that increases the efficiency of IT services. It also enables enterprise sustainability (through technologies such as traceability, analytics, emissions management software, and AI), and it helps customers achieve their own sustainability goals. So opportunities are created in three critical areas of the business at once: internal IT, corporate and customer operations.

Investing in sustainable technologies has the potential to improve operational stability and financial performance while opening up new growth opportunities, according to Gartner forecasts. Act now and prioritize technology investments according to your issues to create an effective, sustainable technology portfolio. Cloud services, greenhouse gas management software, AI, supply chain blockchain, or supplier sustainability applications, among others, could be of interest.

Source: Gartner

What is "Big Data" actually?

What is "Big Data" actually?

Big Data delivers new insights, which in turn open up new opportunities and business models. In the first part of our new blog series, you will learn how this can be achieved.

"Big Data" is on everyone's lips. In the first part of our new blog series, we first want to clarify what is actually meant by it, how Big Data fundamentally works and what can be done with it.

Big Data is understood to mean data that is more diverse and accumulates in ever greater quantities and at higher speeds. Big Data is therefore fundamentally based on these three Vs:

  • Volume: Large volumes of unstructured, low-density data are processed. This can be a wide variety of data from a wide variety of sources and of unknown value. For some companies, this could be hundreds of petabytes.
  • Velocity: Data flows directly into memory at the highest speed and is not written to disk. Some Internet-enabled smart products operate in (near) real time and also require real-time evaluation/response.
  • Variety: Traditional, structured data types are being joined by new, unstructured or semi-structured data types that require additional pre-processing to derive meaning and support metadata.

In recent years, two more Vs have emerged:

  • Value: In recent years, two more Vs have emerged: A significant portion of the value that the world's largest technology companies provide comes from their data, which they are constantly analyzing to become more efficient and develop new products. Data has intrinsic value, but is only useful when that value is discovered.
  • Veracity: How reliable are the available data?

What are the advantages of Big Data?

Big Data provides more complete answers than traditional data analysis because more information is available. More complete answers bring more confidence in the data - and thus a completely different approach to solving problems. So you could say that Big Data delivers new insights, which in turn open up new opportunities and business models.

How does Big Data work in the first place?

Step 1: Integration
First, data must be brought in and processed. It must be ensured that the data is formatted and available in a form that business analysts can continue to work with. Caution: Conventional data integration mechanisms are usually not up to this task. New strategies and technologies are required to analyze the huge data sets on a terabyte or even petabyte scale.

Step 2: Administration
Big Data needs storage. This storage solution can be in the cloud, on-premise or hybrid. In our opinion, the cloud is the obvious choice here because it supports current computing requirements and at the same time can be easily expanded if necessary.

Step 3: Analysis
A visual analysis of the diverse data sets can provide new clarity. Machine learning (ML) and artificial intelligence (AI) can support here.

What can Big Data help with?

Big Data can assist with numerous business activities. Some examples are:

  • Product development: Predictive models for new products/services can be built by classifying key attributes of previous and current products/services and relating them to the commercial success of the offerings.
  • Predictive maintenance: Factors that can predict mechanical failures may be buried deep in structured data (e.g., year of manufacture, sensor data) - by analyzing this data, companies can perform maintenance early and more cost-effectively.
  • Machine learning: Big Data - and the associated availability of large amounts of data - makes training machine learning models possible.
  • Fraud and compliance: Big Data helps identify noticeable patterns in data and aggregate large amounts of data to speed up reporting to regulators.

Challenges of Big Data

In order to take advantage of the opportunities that Big Data brings, a number of challenges must first be overcome.

1. Data storage
First, companies need to find ways to store their data effectively. Although new technologies have been developed for data storage, the volume of data doubles about every two years.

2. Data preparation
Clean data (i.e., data that is relevant and organized in a way that allows for meaningful analysis) requires a lot of work. Data scientists spend 50 to 80 percent of their time preparing and editing data.

3. Stay up to date
Keeping up with Big Data technology is a constant challenge. A few years ago, Apache Hadoop was the most popular technology for processing Big Data. Today, a combination of the two frameworks Apache Hadoop and Apache Spark seems to be the best approach.

Source: Oracle

Tech Trend #9: Metaverse

Tech Trend #9: Metaverse

Metaverse technologies are the future of interaction in the virtual and physical world. Learn more about this tech trend and how you can address it in your business here.

For the coming years, the U.S. research and consulting firm Gartner sees four priorities that companies can address with the help of various technology trends. On this basis, Gartner names and categorizes the 10 most important strategic technology trends for 2023. (centron reported)

As the ninth of these ten tech trends, we would like to introduce you to the so-called metaverse in this article. Gartner assigns this trend to the priority "pioneering customer engagement, accelerated responses or opportunities".


A metaverse is a virtual shared space created by the convergence of virtually augmented physical and digital reality. It is not device-independent, nor is it owned by any single provider. Rather, it is an independent virtual economy enabled by digital currencies and non-fungible tokens (NFTs). In very simple terms, metaverses can also be understood as the next generation of the Internet.

Metaverses are a combinatorial innovation - meaning they require multiple technologies and trends to work. Contributing to this are virtual reality (VR), augmented reality (AR), flexible work styles, head-mounted displays (HMDs), an AR cloud, the Internet of Things (IoT), 5G, AI, and spatial computing.

Metaverse technologies are the future of interaction in the virtual and physical worlds, providing innovative new opportunities and business models. Some of these opportunities are already emerging - both for businesses and individuals:

Gartner predicts that by 2026, 25 percent of people will spend at least an hour a day in the metaverse - for work, shopping, education, social media, or entertainment purposes.

Opportunities for companies

There is currently no such thing as "the" metaverse. A metaverse today still encompasses several new technologies. Gartner advises enterprises to be cautious with investments at this time, as it is too early to determine what will be profitable for the enterprise in the long term.

Ultimately, however, the Metaverse Gartner predicts will provide durable, decentralized, collaborative and interoperable capabilities and business models that will enable enterprises to grow their digital business.
For now, these measures can help you design a strategy that includes Metaverse technologies:

  • Möglichkeiten erkunden, mit Hilfe von Metaverse-Technologien das Digital Business zu optimieren oder neue Produkte und Services zu schaffen
  • Metaverse-Produkte und -Lösungen durch eine Pipeline von kombinatorischen Innovationen aufbauen
  • Vom Metaverse inspirierte Möglichkeiten identifizieren durch die Bewertung aktueller, hochwertiger Anwendungsfälle
  • Mit Bedacht in bestimmte aufkommende Metaverses investieren (Achtung: Schützen Sie Ihren Ruf, indem Sie proaktiv eine Data Governance-, Sicherheits- und Datenschutzrichtlinie etablieren!)

Source: Gartner

How can we prevent AI attacks?

How can we prevent AI attacks?

The more we rely on AI systems, the higher the risk of manipulation becomes. The race to develop appropriate protective measures has begun.

Artificial intelligence (AI) is becoming an increasingly integral part of our everyday lives. But what if the algorithms used to control driverless cars, critical infrastructure, healthcare and much more are manipulated?

Currently, such attacks are still a rarity - but experts believe that the frequency will increase significantly as AI systems become more widespread. If we are to continue to rely on such automated systems, we need to ensure that AI systems cannot be tricked into making poor or even dangerous decisions.

Manipulation of AI systems

The concern that AI could be manipulated is, of course, not new. However, there is now a growing understanding of how deep learning algorithms can be tricked by minor - but imperceptible - changes. This, in turn, leads to misclassification of what the algorithm is studying.

Several years ago, researchers already showed how they could create adversarial 3D objects that would trick a neural network into thinking a turtle was a rifle. Professor Dawn Song (University of California, Berkeley) also showed how stickers on certain parts of a stop sign can trick AI into interpreting it as a speed limit sign instead.

When a human is still involved, such errors can be noticed in time. But if automation takes over more and more, there may soon be no one left to check the AI's work.

Fight against the misuse of AI

Help could come from the U.S. Defense Advanced Research Projects Agency's (DARPA) multi-million dollar GARD project, which has three main goals around fighting AI abuse:

  1. Develop algorithms that already protect machine learning from vulnerabilities and glitches
  2. Develop theories on how to ensure that AI algorithms are still protected against attacks as the technology becomes more advanced and more freely available
  3. Develop and share tools that can protect against attacks on AI systems and assess whether AI is well protected

To provide platforms, libraries, datasets, and training materials to the GARD program, DARPA partners with a number of technology companies, including IBM and Google. This allows the robustness of AI models and their defenses against current and future attacks to be evaluated.

A key component of GARD is the Armory virtual platform, which is available on GitHub. It serves as a testing environment for researchers who need repeatable, scalable, and robust assessments of defenses developed by others.

In the fight against AI misuse, building platforms and tools to assess and protect against today's threats is already difficult enough. Figuring out what hackers will do against these systems tomorrow is even more difficult.

The risk of data poisoning

In addition to direct attacks on AI algorithms, so-called data poisoning also poses an enormous risk. This involves attackers altering the training data used to create the AI in order to influence the AI's decisions from the outset. This risk is particularly prevalent when an AI is trained on a dataset that originates in the public domain - precisely when the public knows that this is the case.

Microsoft's AI bot Tay represents an example of this. Microsoft sent him out on Twitter to interact with humans so he could learn to use natural language and speak like humans. Within a few hours, people had misled Tay into saying offensive things, so Microsoft eventually took him offline again.

Source: ZDNET