On the IT side, CIOs sent workers home with laptops and video conferencing software last year. But it's time to reexamine whether those simple tools are adequate. Do workers need bigger displays? Do they need more than one monitor? What about webcams and better microphones, particularly if they are representing the corporate brand in virtual meetings with external partners and customers. Other technologies that are getting more attention include anything to do with security in this age of distributed work such as edge security, and VPNs. Companies are also reevaluating their unified collaboration and communications technologies as they look to enhance collaboration in a virtual setting. Employees are spending more time using software such as Microsoft Teams, Cisco Webex, and Zoom. How can those tools be improved? "CIOs have moved from infrastructure officers to innovation officers," Banting said. "CIOs are finding out what technology can do for the business, how it meets their needs, and how it makes them more agile by promoting distributed working. Technology can be used as an asset rather than a liability on the books. That's quite a fundamental shift in the IT department and the roles that CIOs play."
Composable commerce is a microservices and modularised architecture that provides organisations with agility through quick, application programming interface (API) driven integrations, from catalogues and product searches, to order submissions, inventory, and recommendations. It provides seamless communication between various applications, giving customers new ways to interact and connect with brands on a personal level. Development teams can focus their efforts on speed and innovation, while operations can make time for back-end updates, compliance releases, and testing. All this can be done without affecting front or back-end operations. It provides collaboration between departments so development, operations, marketing, ecommerce, data, finance, and other areas can align and become an agile platform. Everything can work together cohesively and with siloes no longer existing, products can be brought to market quickly and efficiently without manual intervention.
Taking an agile approach enables workforces – especially project management teams – to adapt quickly and easily, promoting creative, out-of-the-box thinking throughout the business. Businesses that have embraced business agility have found that teams work better together, and their decision-making processes often become much quicker than would have been possible otherwise. To enable adaptability, employers need to find ways to drive employee engagement and efficiency regardless of where people are. ... The uptake of innovative technologies that drive true workplace collaboration spans broader work management platforms offered by a range of global providers, communication apps such as Microsoft Teams and Slack, and toolchains for developing and deploying software such as Azure DevOps. Their use has been made easier because they can often be integrated, allowing teams to use the tools they want for various purposes while still keeping collaborative efforts connected. These types of intuitive solutions enable enterprises to rapidly adjust tactics, resources and personnel to keep operations on course when business conditions shift dramatically – providing organizations with a competitive edge through the current health and economic crisis and in a post-Covid world.
The pact was reportedly forged to avoid legal battles and complaints to regulators. It meant we haven’t seen Microsoft and Google complaining publicly about each other since the days of Scroogled, a campaign that attacked Google’s privacy policies. Now the gloves appear to be off once again, and we’ve seen some evidence of that recently. Google slammed Microsoft for trying to “break the way the open web works” earlier this year, after Microsoft publicly supported a law in Australia that forced Google to pay news publishers for their content. Microsoft also criticized Google’s control of the ad market, claiming publishers are forced to use Google’s tools that feed Google’s revenues. The rivalry between the two has been unusually quiet over the past five years, thanks to this legal truce. Microsoft was notably silent during the US government’s antitrust suit against Google last year, despite being the number two search engine at the time. The Financial Times reports that the agreement between Microsoft and Google was also supposed to improve cooperation between the two firms, and Microsoft was hoping to find a way to run Android apps on Windows.
One thing to note is we have continuous integration (CI)/continuous deployment (CD) for models and services, as shown above in Figure 1. We arrived at this solution after several iterations to address some of MLOps challenges, as the number of models trained and deployed grew rapidly. The first challenge was to support a large volume of model deployments on a daily basis, while keeping the Real-time Prediction Service highly available. We will discuss our solution in the Model Deployment section. The memory footprint associated with a Real-time Prediction Service instance grows as newly retrained models get deployed, which presented our second challenge. A large number of models also increases the amount of time required for model downloading and loading during instance (re)start. We observed a great portion of older models received no traffic as newer models were deployed. We will discuss our solution in the Model Auto-Retirement section. The third challenge is associated with model rollout strategies. Machine learning engineers may choose to roll out models through different stages, such as shadow, testing, or experimentation.
In thinking through what a practical model of digital intelligence might look like, we thought it would be useful to identify three elements that make up best practices for operating in a digital environment. One is the analytical and cognitive component — in essence, how to make sense of the welter of information and data that the digital world offers. The second is the need to collaborate with others in new ways and new mediums. The third is the practical mastery and application we need to demonstrate. This third element is akin to how Robert J. Sternberg, James C. Kaufman and Elena L. Grigorenko describe “practical intelligence”; that is to say, how we manage real world situations or, in our case, navigate the digital world successfully. This is an ability, we would argue, that entails a different or least greatly modified set of skills from that we use in face-to-face environments. ... We aren’t proposing that digital intelligence be treated as true intelligence, but rather as a loose framework to help us identify the knowledge, skills, attitudes and behaviors that make up the “digital sensibility” needed to operate and succeed in increasingly digital organizations and marketplaces.
CTOs, product managers, software executives, process specialists are looking for newer ways to enhance the trustworthiness of their software systems without any compromise on the speed and quality. SRE and DevOps are two such software methodologies that are popular today, in the world of software development. What does SRE stand for? SRE stands for Site Reliability Engineering. Both these procedures are supposed to be sharing a similar line of principles and goals that makes them compete. They look like two sides of the same coin, targeting to lessen the gap between the development and operation teams. Yet, they have their own distinct characteristics that make them contrast. Rather than being two competing procedures for software operations, SRE and DevOps are more like pals that work together to solve organizational hurdles and deliver software in a fast manner. It is interesting to understand what these concepts individually mean, what they have in common, how they differ from each other, and how they fit each other like missing pieces of any puzzle.
Like everyone else, security people want to see the company succeed, and see cool stuff happen. Developers also care about more than just delivery of code; plus they know that if something bad happens, there are significant implications that they want to avoid. While open lines of communication and mutual understanding are key it is equally important that DevSecOps teams have a toolset that is similarly integrated and capable of tracking and addressing the changes that might be happening in your organization. Whether we’re talking about changes in cloud providers, the deployment stack, or something else, there is a clear need to have a platform that will work where you are—in the cloud or on-premises. ... While tools are an essential element of enabling DevSecOps, there remain other challenges to be resolved. These include the “unknown unknowns” that organizations encounter as they speed up their digital transformation. For example, organizations across the board rushed to scale up their cloud environments in response to the pandemic last year. However, when rushing to do so many did not scale up their security and governance processes at the same time and rate.
Do I want to be a data engineer or a data scientist? Do I want to work with marketing & sales data, or do the geospatial analysis? You may have noticed that I have been using the term DS so far in this article as a general term for a lot of data-related career paths (e.g. data engineer, data scientist, data analyst, etc.); that’s because the lines are so blurred between these titles in the data world these days, especially in smaller companies. I have observed a lot of data scientists see themselves as ONLY data scientists building models and don’t pay attention to any business aspects, or data engineers who only focus on data pipelining and don’t want to know anything about the modeling that’s going on in the company. The best data talents are the ones who can wear multiple hats or are at least able to understand the processes of other data roles. This comes in especially handy if you want to work in an early stage or growth stage startup, where functions might not be as specialized yet and you are expected to be flexible and cover a variety of data-related responsibilities.
Thanks to the Digital Revolution, many things that seemed impossible just a few years ago are now commonplace. No one can deny that our productivity – and indeed, enjoyment – has been dramatically improved by technologies ranging from AI to Big Data, 5G and the IoT. While new applications for these technologies are being found seemingly every day, it’s increasingly important to ask how we can utilise technology in a responsible way, to change and improve people’s lives in critical areas like education, healthcare and the environment. The good news is that work is already underway to apply technology in meaningful ways. Take, for instance, the support being provided for young African women programmers in marginalised communities. They are benefitting from free online training and free access to cloud computing resources. The aim of this project is to create one million female coders by 2030 with the objective of improving their life outcomes by helping them along a career path in engineering and other practical subjects. The iamtheCODE initiative provides them with tailored courses on a range of technical topics including cloud computing, data analysis, machine learning and security.
Quote for the day:
"Leaders know the importance of having someone in their lives who will unfailingly and fearlessly tell them the truth." -- Warren G. Bennis