While a multi-cloud approach can deliver serious value in terms of resiliency, flexibility and cost savings, making sure you’re choosing the right providers requires a comprehensive assessment. Luckily, all main cloud vendors offer free trial services so you can establish which ones best fit your needs and see how they work with each other. It will pay to conduct proofs-of-concept using the free trials and run your data and code on each provider. You also need to make sure that you’re able to move your data and code around easily during the trials. It’s also important to remember that each cloud provider has different strengths—one company’s best option is not necessarily the best choice for you. For example, if your startup is heavily reliant on running artificial intelligence (AI) and machine learning (ML) applications, you might opt for Google Cloud’s AI open source platform. Or perhaps you require an international network of data centers, minimal latency and data privacy compliance for certain geographies for your globally used app. Here’s where AWS could step in. On the other hand, you might need your cloud applications to seamlessly integrate with the various Microsoft tools that you already use. This would make the case for Microsoft Azure.
Remote working exposed another potential weakness holding back teams from realising their potential – employee expertise that isn’t being shared. Under the lockdown, many companies realised that knowledge and experience within their workforce were highly concentrated within specific offices, regions, teams, or employees. How can these valuable insights be shared seamlessly across internal networks? A truly collaborative company culture must go beyond limited solutions, such as excessive video calls, which run the risk of burning people out. Collaboration tools that support culture have to be chosen based on their effectiveness at improving interactions, bridging gaps and simplifying knowledge sharing. ... While revamping strategies in recent months, many companies have started to prioritise customer retention and expansion over new customer acquisition, given the state of the economy. Data and technology can help employees adapt to this transition. Investing in tools that empower employees gives them the confidence, knowledge and skills they need to deliver maximum customer value. This in turn boosts customer satisfaction as staff deliver an engaging and consistent experience each time they connect.
“The benefits of IT and business automation extend far beyond cost savings. Organizations need this capability – to drive revenue, stay connected with customers, and keep employees productive – or they face extinction,” said Bernd Greifeneder, CTO at Dynatrace. “Increased automation enables digital teams to take full advantage of the ever-growing volume and variety of observability data from their increasingly complex, multicloud, containerized environments. With the right observability platform, teams can turn this data into actionable answers, driving a cultural change across the organization and freeing up their scarce engineering resources to focus on what matters most – customers and the business.” ... 93% of CIOs said AI-assistance will be critical to IT’s ability to cope with increasing workloads and deliver maximum value to the business. CIOs expect automation in cloud and IT operations will reduce the amount of time spent ‘keeping the lights on’ by 38%, saving organizations $2 million per year, on average. Despite this advantage, just 19% of all repeatable operations processes for digital experience management and observability have been automated. “History has shown successful organizations use disruptive moments to their advantage,” added Greifeneder.
The ultimate goal should be the implementation of a process for formal review of cybersecurity risk and readout to the governance, risk, and compliance (GRC) and audit committee. Each of these steps must be undertaken on an ongoing basis, instead of being viewed as a point-in-time exercise. Today's cybersecurity landscape, with new technologies and evolving adversary trade craft, demands a continuous review of risk by boards, as well as the constant re-evaluation of the security budget allocation against rising risk areas. to ensure that every dollar spent on cybersecurity directly buys down those areas of greatest risk. We are beginning to see some positive trends in this direction. Nearly every large public company board of directors today has made cyber-risk an element either of the audit committee, risk committee, or safety and security committee. The CISO is also getting visibility at the board level, in many cases presenting at least once if not multiple times a year. Meanwhile, shareholders are beginning to ask the tough questions during annual meetings about what cybersecurity measures are being implemented. In today's landscape, each of these conversations about cyber-risk at the board level must include a discussion about the Enterprise of Things given the materiality of risk.
FreedomFi offers a couple of options to get started with open-source private cellular through their website. All proceeds will be reinvested towards building up the Magma's project open-source software code. Sponsors contributing $300 towards the project will receive a beta FreedomFi gateway and limited, free access to the Citizens Broadband Radio Service (CBRS) shared spectrum in the 3.5 GHz "innovation band." Despite the name "good-buddy," CBRS has nothing to do with the CB radio service used by amateurs and truckers for two-way voice communications. CB lives on in the United States in the 27MHz band. Those contributing at $1,000 dollars will get support with a "network up" guarantee, offering FreedomFi guidance over a series of Zoom sessions. The company guarantees they won't give up until you get a connection. FreedomFi will be demonstrating an end-to-end private cellular network deployment during their upcoming keynote at the Open Infrastructure Summit and publishing step-by-step instructions on the company blog. This isn't just a hopeful idea being launched on a wing and a prayer. WiConnect Wireless is already working with it. "We operate hundreds of towers, providing fixed wireless access in rural areas of Wisconsin," said Dave Bagett, WiConnect's president.
Artificial intelligence (AI) has made astonishing progress in the last decade. AI can now drive cars, diagnose diseases from medical images, recommend movies, even whom you should date, make investment decisions, and create art that people have sold at auction. A lot of research today, however, focuses on teaching AI to do things the way we do them. For example, computer vision and natural language processing – two of the hottest research areas in the field – deal with building AI models that can see like humans and use language like humans. But instead of teaching computers to imitate human thought, the time has now come to let them evolve on their own, so instead of becoming like us, they have a chance to become better than us. Supervised learning has thus far been the most common approach to machine learning, where algorithms learn from datasets containing pairs of samples and labels. For example, consider a dataset of enquiries (not conversions) for an insurance website with information about a person’s age, occupation, city, income, etc. plus a label indicating whether the person eventually purchased the insurance.
The key takeaway is that fundamentally BERT and GPT3 have the same structure in terms of information flow. Although attention layers in transformers can distribute information in a way that a normal neural network layer cannot, it still retains the fundamental property that it passes forward information from input to output. The first problem with feed forward neural nets is that they are inefficient. When processing information, the processing chain can often be broken down into multiple small repetitive tasks. For example, addition is a cyclical process, where single digit adders, or in a binary system full adders, can be used together to compute the final result. In a linear information system, to add three numbers there would have to be three adders chained together; this is not efficient, especially for neural networks, which would have to learn each adder unit. This is inefficient when it is possible to learn one unit and reuse it. This is also not how back propagation tends to learn, the neural network would try to create a hierarchical decomposition of the process, which in this case would not ‘scale’ to more digits. Another issue with using feed forward neural networks to simulate “human level intelligence” is thinking. Thinking is an optimization process.
The attitude regarding Agile adoption that is represented by top management impacts the whole process. Disengaged management is the most common reason for my ranking. There were not too many examples in my career when the bottom-up Agile transformation was successful. Usually, top management is at some point aware of Agile activities in the company, Scrum adoption, although they leave it to the teams. One of the frequent reasons for this behavior is that top management is not acquainted with the understanding that Agility is beneficial for business, product, and the most important – customers/users. They consider Agile, Scrum and Lean to be things that might improve delivery and teams' productivity. Let's imagine the situation when a large number of Scrum Masters reported the same impediment. It becomes an organizational impediment. How would you resolve it when decision-makers are not interested in it? What would be the impact on teams' engagement and product delivery? Active management that fosters Agility and empirical way, and actively participates in the whole process is a secret ingredient that makes the transition more realistic. Another observation I have made is a strong focus on delivery, productivity, technology and effectiveness.
So what's going on here? Adding two new countries -- Japan and India -- the statement suggests that more governments are getting worried, but the tone is slightly different now. Perhaps governments are trying a less direct approach this time, and hoping to put pressure on tech companies in a different way. "I find it interesting that the rhetoric has softened slightly," says Professor Alan Woodward of the University of Surrey. "They are no longer saying 'do something or else'". What this note tries to do is put the ball firmly back in the tech companies' court, Woodward says, by implying that big tech is putting people at risk by not acceding to their demands -- a potentially effective tactic in building a public consensus against the tech companies. "It seems extraordinary that we're having this discussion yet again, but I think that the politicians feel they are gathering a head of steam with which to put pressure on the big tech companies," he says. Even if police and intelligence agencies can't always get encrypted messages from tech companies, they certainly aren't without other powers. The UK recently passed legislation giving law enforcement wide-ranging powers to hack into computer systems in search of data.
One of the most important elements in DevSecOps revolves around a project’s branching strategy. In addition to the main branch, every developer uses their own separate branch. They develop their code and then merge it back into that main branch. A key requirement for the main branch is to maintain zero unresolved warnings so that it passes all functional testing. Therefore, before a developer on an individual branch can submit their work, it also needs to pass all functional tests. And all static analysis tests need to pass. When a pull request or merge request has unresolved warnings, it is rejected, must be fixed and resubmitted. These include functional test case failures and static analysis warnings. Functional test failures must be fixed. However, the root cause of the failure may be hard to find. A functional test error might say, “Input A should generate output B,” but C comes out instead, but there is no indication as to which piece of code to change. Static analysis, on the other hand, will reveal exactly where there is a memory leak and will provide detailed explanations for each warning. This is one way in which static analysis can help DevSecOps deliver the best and most secure code. Finally, let’s review Lean and shift-left, and see how they are connected.
Quote for the day:
'The mediocre leader tells; The good leader explains; The superior leader demonstrates; and The great leader inspires." -- Buchholz and Roth