Using AI and DevOps to streamline communications

Using AI and DevOps to streamline communications

The way in which organisations deploy enterprise technologies has undergone a shift in recent years. Today, there is a cry for more agile ways of working. But to achieve this agility, teams need to establish a communication stream that works for both the techies and non-techies, the influencers and implementers, the stakeholders and the individual. In short, the more integrated and familiar your employees are with one another, the less painful (and costly) your communication has to be.

People, then tech

Whilst digital transformation is often perceived to be technology focused, you’d be mistaken to put the onus of change wholly on your DevOps team. According to PMI’s 2018 Success in Disruptive TimesReport, 29% of failed projects mention inadequate/poor communication as the primary cause of those failures.

Part of this problem is how different departments approach work, their interest in the change and the different language they use. Then there’s the fact that many departments are so busy working towards their own goals that they lose sight of the overall needs of the business – they can’t see the forest for the trees, as it were.

Rather than throwing work over the wall for unengaged individuals to pick up, creating communication streams that encourage collaboration and demonstrate value are fundamental to delivering a successful transformation.

Take automation. If the basic challenge behind DevOps is to keep moving parts in sync to enable a fail fast, fail often approach, having a collaborative team will reduce the number of moving parts that need to be synced – simplifying the process and accelerating deployment.

The same applies for feedback loops. Software developers use a DevOps approach to quickly release apps and gather feedback on new features – and not just when applications are in production. This enables teams to have full visibility over the development of products, testing as they build and releasing more rapidly with more confidence.

How is Artificial Intelligence (AI) strengthening DevOps Programs?

One of AI’s greatest strengths is that it can flex its intelligent, data-grabbing fingers a whole lot quicker than the average Joe. Not only does this help automate the extraction of knowledge from vast amounts of data at pace, it consolidates data from multiple sources, centralising data and granting teams a way of searching data pragmatically.

It also offers a greater degree of flexibility. Take Cloud tools as an example. There are so many different pathways of how to approach Cloud / implement the appropriate tools that whilst you might feel you know the best way to approach something, there is every chance a better alternative exists. And this is where AI comes into its own. Intuitive by design, AI can collate hundreds of thousands of examples, spot anomalies in this data and then recommend best practice based on what others have done. This intelligence offers a more holistic view and gives insights far beyond your companies’ four walls.

“It’s one thing to understand what’s happening, and it’s another to decide what to do. We see people turning to AI to help optimise their decision-making as the intelligence AI provides enables businesses to have a more holistic view over the data whilst remaining specific to the problem the business is trying to solve”

 Babak Takand, ML Specialist & DevOps Consultant at ECS Digital 

How is AI helping to streamline communications?

As touched on above, communication and feedback are two of the biggest challenges when it comes to moving to a DevOps methodology. Ideally, you need to be setting up channels that can revise workflows on the fly. Automated technology, chatbots and other systems enhanced with intelligence and learning abilities, are capable of doing just that, enabling communication streams to be simplified and more proactive.

As the communication streams begin to become slicker, businesses can begin to apply more pressure on their DevOps process with the confidence that the agility and tools in place will make it go faster than humans could go on their own.

Ultimately, tools are there to help you identify problems and to add flexibility to your system. Teams trained in these tools – like ECS Digital – are then on hand to train individuals on how to use these systems and adapt them to how things operate.

For those of us knee deep in sci-fi media, the utopia would be to invert this internally, so the system adapts to how you want your tech to work automatically. In other words, if you are wanting to use a specific DevOps tools, you could voice / code what it is you want to achieve, and the AI tool will have a good enough understanding that it will identify your needs and set it up for. Failing that, it will generate a set of steps you need to take to instead.

Leading by example

At ECS Digital, we are putting our tools where our mouth is.

For the past year, as part of our R&D initiative in AI and machine learning, we have been looking at what we can extract from our own internal communications, and utilise that knowledge to enhance our internal processes by looking at popular topics, reoccurring sentiments, and monitoring issues being flagged by individuals / teams. Using various tools – from nature language processing, visualisation, sentiment analysis and traditional analytics – we have the ability to capture the data we need totake a more proactive stance when it comes to problem solving.

Whilst the data is anonymised, the picture it paints is specific to the business and most importantly, it’s honest, meaning ECS Digital has greater visibility over the business communications to help it improve.

We have also begun trialling an automatic assistant for one our clients, introducing a fully automated tool that monitors the reaction of people and maps pathways in conversation. These insights are already helping to improve the customer journey. By flagging pain points and enabling the team to rework the available conversational pathways, our client is truly leveraging the power of AI to align their offerings with what the customer expects.

How can you leverage AI to streamline your communications?

You can’t have intelligence without data, and you can’t have data without formalising how you collect that information from various input streams.

Data collection is a fundamental part of DevOps and requires creating structure around your data collection pipeline.By creating structure, you are enabling the process to be repeated again and again and again, creating the perfect environment for an AI or Machine Learning tool to read your data and generate insights.

In the words of Babak: “As part of your DevOps experience, you will have information that is being submitted left, right, and centre. How you collect this data, how you store it, how you keep it, how you look it, that is important – make your data collection process uniform”.

ECS Digital can help you formalise that structure.

With over 15 years’ experience delivering successful digital transformations, ECS Digital can help you deliver better products faster through the adoption of DevOps, agile ways of working and modern software delivery tools. Talk to the team today to find out how we can help you leverage AI to streamline your communication streams.

Want to read more? Check out our ‘Why you need to embrace AI in your software testing’ blog here.

Babak TakandUsing AI and DevOps to streamline communications
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Five ways Artificial Intelligence is already impacting DevOps

Five ways Artificial Intelligence is already impacting DevOps

Artificial Intelligence and Machine Learning have gained a lot of media attention over the past few years. Many commentators have pointed out how these new technologies are going to create new and interesting developments in a variety of fields – from law to medicine, transportation to education. At ECS Digital, we see AI and ML having a direct and lasting impact on DevOps, and here’s why.

DevOps is a business-driven approach to delivering software, creating an intense collaboration between developer and operations. Whilst human input remains an important cog within the system, DevOps focuses on encouraging businesses to automate repeatable processes to encourage efficiency, reduce variability and improve quality at every stage of the pipeline.

Artificial intelligence vs humans – posted on Targetprocess

 

Emerging AI tools stand to generate even bigger gains. Set to transform how teams develop, deliver, deploy and manage applications, AI and ML perform tasks which would have traditionally required human intelligence. Most notably, these technologies are capable of processing vast amounts of information – picking up the menial tasks and freeing up IT staff to do more targeted work. They can learn patterns, anticipate complications and recommend solutions, all of which fit perfectly within a DevOps culture.

Essentially, AI makes up the technology that integrates into the DevOps systems – affecting both the tools DevOps teams use, and the people who use them.

Here are five ways that AI can work with DevOps to improve software and delivery for the better:

  1. Feedback on Performance

DevOps uses continuous feedback loops at every stage of the process. This involves gathering huge amounts of data in the form of performance metrics, log files and other reports to provide feedback on the operational performance of running applications.

The more advanced monitoring platforms are already applying machine learning to proactively identify problems early in the process and make recommendations. ML in turn is enhancing the continuous feedback loops critical to DevOps by feeding these recommendations straight back to the relevant teams so they can ensure the application service remains viable.

This means you have the 20 highest priority tasks to hand and your AI system can analyse and help pinpoint certain root causes for you to immediately remediate.

  1. Increased Communication

Communication and feedback within teams is one of the biggest challenges when an organisation moves to a DevOps methodology. The sheer amount of information within a company’s systems forces companies to reconsider how teams are interacting with one another, with most businesses setting up a wider variety of channels to set and revise workflows as quickly as possible.

Many of our own team have experienced being blocked by administrative tasks whilst helping clients adopt new technology and ways of working. These tasks often take several weeks to complete, delaying progress in projects and momentum of change. “In these cases, it is advantageous to have access to self-service portals or ChatBots that will help me to orientate in customers’ infrastructure” – Marian Knotek, DevOps Consultant at ECS Digital.

AI systems such as ChatBots are essential to supporting the automated technology that DevOps offers, helping these communication channels become more streamlined and proactive.

  1. Smooth monitoring

To operate efficiently, DevOps teams need to simplify tasks. This is becoming increasingly more difficult as environments become more complex. The sheer volume of data in today’s dynamic and dispersed application environments has made it tricky for DevOps teams to effectively gather and apply information that can help resolve customer issues.

Start with monitoring tools for example, teams tend to use multiple tools that monitor an application’s health and performance in different ways. Extensive amounts of data produced by various platforms and tools are usually aggregated by tools like Splunk’s Artificial Intelligence for IT operations solution harnesses log, application, cloud, network, metric data and more. By automating routine practices, accuracy and speed of issue recognition are increased and operations become streamlined.

 

Artificial Intelligence for IT Operations (AIOps) platform by Splunk

 

In a nutshell, Artificial Intelligence and Machine Learning applications are capable of absorbing multiple data streams to find correlations, possible dependencies and issues in the system, giving the team a more holistic view of the application’s overall health.

  1. Prioritise alerts

Alert systems are fundamental to the DevOps culture of ‘fail fast, fail often’. But when a system has been set to flag inconsistencies and flaws in real-time, these can hit the team thick and fast with no differentiation between the severity of the problem – making it difficult for teams to react.

Machine Learning applications can help teams prioritise their responses. Pulling on data such as past behaviour, the magnitude of the alert and the source, DevOps teams can set up rules which enable machines to manage the influx and assort the data when it begins to overwhelm the system.

  1. Improved customer service

Improving the customer journey and providing a positive customer experience (CX) was ranked as the top strategic priority in a survey of global banking organisations for the 2017 Retail Banking Trends and Predictions Digital Banking Report. For many, understanding how users are interacting with their business and tweaking their software in response to these findings is a significant part of creating an all-round better CX. Businesses are also looking for ways to effectively support a 24/7 always on, internet-based, mobile-accessible consumer environment.

Artificial Intelligence and Machine Learning lend themselves perfectly to this landscape. Not only can they collect and analyse data, they can pre-empt questions that may come up during the customer journey and manage the bulk of enquiries to help ease human resource. ITSM tools such as ServiceNow are capable of fashioning a pattern of events before each previous failure is noted. This results in the creation of a support ticket before the event takes place, moving businesses from a reactive to a predictive approach.

This ability to solve a problem before it arises is a huge benefit, significantly lowering customer abandonment rates in the purchasing cycle. It has also been proven to reduce customer complaints and improve consumer satisfaction.

The Future of AI and DevOps

AI, Machine Learning and DevOps – none of these concepts are leaving the conversation any time soon. All are contributing huge amounts to innovation in the tech space and, whilst they are able to operate effectively on their own, there is an interesting dynamic between the maturity of one and the evolution of the others.

The IT industry right now is already in a very different place than where it was five years ago. Whilst DevOps has repeatedly proven its place, this fast development of IT requires reshaping the cultures and mindsets around how we can get the most out of an already successful tool. Most notably, these new approaches towards automated IT enable shouldn’t be ignored. Enterprises that do not make this adjustment and fail to adapt their DevOps efforts to work with Artificial Intelligence and Machine Learning are going to find themselves left behind.

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ECS Digital is an experienced digital transformation consultancy that helps clients deliver better products faster through the adoption of modern software delivery methods. We help our clients transform at scale through the use of Enablement Pods – combining outcome focused teams and value-add sprints.

Our Pods deliver DevOps, CT, Cloud and engineering capabilities in one team. This means you get process, enablement and nearly two decades of experience on top of the first-rate engineering, tooling and testing you would expect.

It also means you have a team on board that can help implement the technology you need to embrace Artificial Intelligence and Machine Learning and enable your team in modern tools, technology and ways of working.

Want to know how we can benefit your business? Get in touch.

Andy CuretonFive ways Artificial Intelligence is already impacting DevOps
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DOES 2018 –  bigger, better, brighter

DOES 2018 –  bigger, better, brighter

We’re back, sponsoring the DevOps Enterprise summit (DOES) 2018 in London – the 5th event in the series we’ve sponsored.

DOES primarily takes a look at how large enterprises are adopting DevOps and the associated challenges which comes at scale. This year was no different. Over the past couple of days, we heard talks from the likes of Jaguar Land Rover, Adidas, Nomura and Lloyds bank – not to mention the usual suspects of Barclays, Hiscox and Disney. Each provided valuable insights into their transformation journeys.

It came as no surprise that the common theme amongst these speakers was that whilst adoption is growing, scaling across different areas of the business is proving the greater challenge. One of the key takeaways for adoption is that businesses need to stop looking at IT as “projects” or “programmes”. IT should instead become “long lived products” where the focus is on business outcomes.

The below images, drawn at DOES 2018, gives an overview of the different talks that took place over the two-day event:

Two of the most impressive talks were given by Verizon and Disney. We’ve summarised the key takeaways from each below:

Verizon

‘DevOps is not a hobby but a new avenue to revenue’

Delivered by John Scott, Oliver Cantor, & Sanjeev Jain

This presentation focused heavily on howVerizon has enabled different systems with new ways of working, as well as the adoption of new technology. Their talk touched on:

  • The creation of Immersion Centres, where teams would focus on current challenges and look to improve these during a 6-week period
  • Creation of MVP products
  • New ways of working and the coaching required
  • Using gamification to gather more momentum with the “DevOps Cup”

Disney

‘Creating Digital Magic’

Delivered by Jason Cox & Jim Vanns

An incredibly powerful talk, with a spectacular cinematic view of some of Disney’s blockbusters. Fundamentally, all areas of the business are powered by technology and Jim Vanns explained within Industrial Light and Magic (ILM) how they have used technology to change how they operate. The below were highlights from the talk:

  • Technology stack used include Docker, Ansible, Elasticsearch
  • Strong focus on Microservices
  • Main challenges include scale, speed and stability
  • DevOps transformation focuses on leadership, technology and community

DOES 2018 had some amazing presentations, as well as memorable insights from some of the industry’s trailblazers. It is an event for bringing together innovative thinking, and as Gene Kim mentioned in one of the opening remarks: “business leaders who are driving organisations forward in the next 5-10 years will be in this room”.

We don’t believe any other conference brings this type of thought leadership and access to such an open community. We look forward to DOES 2019 where next year it will be spread across three days!

Jason ManDOES 2018 –  bigger, better, brighter
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Why you need to embrace AI in your software testing

Why you need to embrace AI in your software testing

Testing is changing.

Automated software testing has boosted efficiency in major businesses, reduced time to market, and positively impacted the quality of delivered product for companies who’ve embraced it. But automation was just the beginning. Now there’s another step to take: Artificial Intelligence (AI).

Integrating the power and flexibility of AI into automated testing accelerates the process, further improving delivered product. Choosing not to implement AI – in a market where competitors are almost certainly finding an edge in doing so – means a real risk of falling behind or even becoming obsolete.

Software systems are escalating in complexity. Data volumes are increasing exponentially.  Software needs to be developed in a way which cleverly accommodates future demands. These things all mean that AI will one day not be an enhancement, but a necessity in automated testing.

What is Artificial Intelligence?

AI covers a very broad range of concepts. It reaches all the way from simple reactionary systems – possible in a few lines of code – to full-fledged and hugely complex examples like driverless cars. As a general definition, an AI system will exhibit any number of behaviours that we consider intelligent. These typically include the capacity to learn, adapt to new situations, and make optimal decisions.

While there are futuristic views of AI, which present it as a self-aware entity that will render the human element obsolete, these are rather far from fruition. The pragmatic direction in which AI is developed, is as yet another tool which increases the ability, speed, accuracy and overall efficiency of the human process – A new generation of intelligent tools complements human intelligence, and makes our technology more flexible.

Many business sectors have already applied AI to their major processes to great effect. A good example is Amazon, which has completely rebuilt its business around AI systems. Some of these are the product in themselves – the Alexa assistant, for instance – while others power a back end which sells more, reduces errors, and works more efficiently. Market intelligence firm Tractica predicts that AI-influenced trading revenue will rise from $643.7 million in 2016 to $36.8 billion in 2025. AI is here, and it’s making a difference.

Machine Learning (ML) is a very promising discipline of AI which has been tried and tested in various applications within the industry, as it can be used to make predictions, detect trends and irregularities, by using statistical methods to extrapolate new information out of data, which is then used in various decision making processes.

The advancements in processing power, as well as the availability and exponential increase in the size of data, have resulted in an unprecedented increase in popularity of ML. Already a large number of data-driven companies have integrated machine learning into their business processes which can be found at the heart of retail, financial as well as social media companies.

Why test with AI?

There may be no better application for AI than in enhancing automated testing. AI-led testing can bring to light issues earlier as it analyses data as it goes – helping companies find solutions faster and reducing the burden on human testers.

Testing is never a one-time process: A set of test scenarios has to be executed at each development iteration throughout the lifecycle of any software, with the number of test scenarios increasing with each new added functionality.

Automated testing has greatly increased the effectiveness and speed of software testing, by removing the need for a human tester to repeat the exact same tests, with the added benefit of having test scenarios expressed in a consistent and formal manner. The limitations of automated testing arise from two key factors:

  • The clockwork nature of automation does not always allow sufficient flexibility to accommodate software with dynamic content and features
  • Test development often relies on the intuition and skill of the developer, and requires a good understanding of the System Under Test (SUT).

The introduction of artificial intelligence can greatly reduce the effort and complexity in analysis and implementation associated with software testing, as well as the quality of the tests by leveraging the ability of a tester to analyse a SUT.

Fuzzy logic is a technology that has found application in situations where the effectiveness of conventional types of logic is limited, and can be found at the core of many AI technologies. Its strong potential in testing is due to the ability of fuzzy logic to produce valuable results in problems riddled with uncertainty and ambiguity.

AI augmented software testing can result in improved test quality, faster delivery, and an end to clockwork testing. Most importantly this analytic and data-driven approach has the potential to change the nature of the automated software testing process altogether.

Things to consider

Like any process improvement, the benefits of AI have an allure that makes it tempting to jump in immediately. Not embracing AI means the potential of your business taking a back seat whilst you watch your competitors soar.  There’s no doubt that it’s an essential move, but it’s one that needs to be handled with care. The way AI is applied to testing processes must be systematic and intelligent in itself.

AI cannot solve every problem. Instead, it is important to discover and analyse those problems it can solve, and to understand the requirements and impact that introducing AI into your systems will have.

Rush into implementation without proper research and consideration, and you could end up investing time and money into a solution which is not appropriate for your business. Fail to invest in the proper training and documentation, and you risk your testers losing touch or using the new technology incorrectly. For AI to improve automated testing, it needs to be fully understood.

ECS Digital’s QA team has long been working with and developing AI processes to augment automated testing. Its internal AI group have also been educating staff and promoting good development practices throughout the company.

As both AI users and consultants, ECS Digital are uniquely placed to inform our clients on the right way to implement AI in their testing processes. AI doesn’t come off the shelf – it needs to be tailor made to suit you.

ECS Digital have the expertise and experience to advise exactly how integrating AI and automation can help your business and can recommend the solution that best fits your needs.

Get in touch to discuss how you can revolutionise your testing process.

Babak TakandWhy you need to embrace AI in your software testing
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Alexa: Building Skills for the World of Tomorrow

Alexa: Building Skills for the World of Tomorrow

We have all seen the TV Ads with someone asking Alexa (Amazons personal assistant AI) to dim the lights or start playing ‘The Grand Tour’ on Prime Video, and this technology is growing larger and faster every day.

Most commercial technologies like computers and internet started their lives in the hands of big businesses and large institutes that could afford the large initial RnD costs. In light of this, the Amazon team have taken a reverse approach and employed a small scale, iterative expansion of the product.

By providing developers access to the Alexa development kit and opening the voice service to the public, Amazon have made Alexa development a straightforward, painless and rewarding process.

Amazon incentivises its cult following of open source developers by rewarding those who create great skills that others want to use. Amazon announced:

“Publish a new skill this month and get an Alexa water bottle to help you stay hydrated during your coding sessions. If more than 75 customers use your skill in its first 30 days in the Alexa Skills Store, you can also qualify to receive an Echo Dot to help you make Alexa even smarter. The skill with the most unique users within its first 30 days after publishing in February will also earn an Echo Spot.”

Vocal Skills Revolution

We should all remember the mobile app revolution along with the tremendous increase in the number of smartphone users  experienced in global mobile app markets . A massive increase in the user base drove innovation, producing better mobile phones. An organised marketplace for app download, timely updates, advanced app development platforms became the norm. Most significantly, the development of some very useful and revolutionary apps have become part of our everyday lives. With the number of users almost doubling over the last 5 years, mobile app developers can reach more consumers than ever.

At ECS Digital, we believe Voice will experience the same type of growth as mobile applications did.

While consumers command more of their day to day life using voice-controlled technologies, from smart TVs to Alexa enabled electric cars, we can be safe in the knowledge that the voice revolution is coming and will change the way future generations interact with technology.

Alexa for Business

What is Alexa for Business?

Alexa for Business makes it easy for you to use Alexa in your organisation. Alexa for Business provides tools to manage Alexa devices, enrol users and configure skills across those devices. You can build your own context-aware voice skills using the Alexa Skills Kit (ASK) and conferencing device APIs, and you can make them available as private skills for your organisation.

What is an Alexa Skill?

Alexa is Amazon’s voice service and the brain behind tens of millions of devices like the Amazon Echo, Echo Dot, and Echo Show. It provides capabilities, or skills, that enable customers to create a more personalised experience. There are now tens of thousands of skills from companies like Starbucks, Uber, and Capital One as well as other innovative designers and developers.

Alexa Voice Service

The Alexa Voice Service (AVS) enables you to integrate Alexa directly into your products. We provide you with access to a suite of resources to quickly and easily build Alexa-enabled products, including APIs, hardware and software development tools, and documentation. With AVS, you can add a new intelligent interface to your products and offer your customers access to a growing number of Alexa features, smart home integrations, and skills.

What is the Alexa Skills Kit?

The Alexa Skills Kit (ASK) is a collection of self-service APIs, tools, documentation, and code samples that makes it fast and easy for you to add skills. ASK enables designers, developers, and brands to build engaging skills and reach customers through tens of millions of Alexa-enabled devices. With ASK, you can leverage Amazon’s knowledge and pioneering work in the field of voice design.

ECS Digital and Amazon Alexa

With Alexa for business being released in the US and coming to the rest of the world soon, we at ECS Digital have been using her to increase productivity and enable innovation within the office. We have been working on a few different initiatives coining the term OfficeOps.

Here are some of them:

Booking a meeting room

Working in a large consultancy,  it can be difficult to know if a meeting room is free. Moreover, booking said room can be a complicated and confusing process. The answer: create an internal/Dev skill to track the availability of a room, who has it and for how long. This skill also allows users to book a room on the spot, allowing our colleagues to interact with the booking process by literally asking the room for a booking slot .

Interactive Training

As a fast-moving DevOps consultancy, ECS Digital are always looking for innovative ways to improve our skills. For a long time now, we have been using Alexa to learn new skills and brush up on existing ones by using her as a pop quiz master. Colleagues located in our London Bridge office can ask Alexa to test their knowledge about a technology, helping them to maintain a high level of competency.

Summary

All evidence suggests that voice is here to stay, and will drive the next wave of technical innovation, both in business and at home, making those laborious, everyday tasks a little easier and futuristic. However, our assessment comes with a note: work still needs to be done in order make voice the standard, but we are confident that changes will be made swiftly.

Visit our services to explore how we enable organisations to transform their internal cultures, to make it easier for teams to collaborate, and adopt practices such as Continuous Integration, Continuous Delivery, and Continuous Testing. 

Morgan AtkinsAlexa: Building Skills for the World of Tomorrow
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The rise of Artificial Intelligence at the AIBE Summit 2018

The rise of Artificial Intelligence at the AIBE Summit 2018

A couple of weeks ago, we attended the annual Artificial Intelligence in Business & Entrepreneurship Summit (AIBE). The summit boasted more than 700 delegates and took place in QEII Centre in Westminster. The organisation behind AIBE wanted to create an event that would attract all education levels. Although, a relatively new event, AIBE has managed to establish itself over the past two years as a great way to engage diverse audiences and share ideas to further progress AI initiatives.

The selection of speakers varied significantly, from your highly technical IBM engineer, to academics who focused more on theoretical aspects and the future of technology. Several topics were debated around what exactly an AI driven world would look like; examples included how AI will influence society in terms of social interaction, career choices, job interaction, and the potential harm that AI can inherently cause. One speaker suggested that we should be pro-active in developing regulatory systems around how this technology should be used.

Democratisation of AI

Danilo Poccia, Amazon’s Evangelist led a discussion on the benefits of democratisation. We were introduced to Amazon’s products and services that are already widely available in their AWS ecosystem. The potential for AI to grow within this industry is huge and the more widely available AI technology becomes to the masses, the greater the opportunity for anyone and everyone to build the AI systems they need.

How is blockchain and AI influencing Fintech?

In a panel discussion, there was an interesting debate regarding how blockchain is influencing Fintech companies and how, in turn this is disrupting business. The ultimate question from this was: what is the future of digital currencies based on blockchain and AI?

Only time can tell as there was no consensus on whether cryptocurrencies would replace legacy currencies or how exactly AI would influence monetary systems. Several advantages were highlighted especially the fact that rules are set from the outset, potentially making these currencies less volatile. Cryptocurrencies are still in their infancy and they will need to go through several iterations of improvements towards faster and more secure platforms.

Expo

Another area of the AIBE Summit was an Expo where you can lead discussions with companies about their particular AI driven software. People were happy to confer the technical details and it was a good chance to gauge the diverse opinions of the attendees.

So what has AI got to do with DevOps and Continuous Delivery?

It indeed contributes a great deal when you’re in the business of automation, testing and performance optimisation. As a matter of fact, we’re currently developing several tools used for our clients to make automated testing and performance optimisation more autonomous. We will be sharing

If you would like some more information about Artificial Intelligence and DevOps, or if you have any questions, please get in touch.

Marian KnotekThe rise of Artificial Intelligence at the AIBE Summit 2018
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