Stream-aligned ML Teams are teams that develop and/or manage ML solutions for end-users, i.e., domain experts, or customers in an organization. For example, in a retail company, such a team can be a markdown/discount-pricing team that delivers prices during seasons throughout the year. The scope of the team can vary but should be determined by the cognitive load of the team. For example, if the data sources and regression mechanism of the solution does not vary too much for the in- or sale-seasons then the cognitive load to support both do not double and, hence, a slightly bigger team can develop, operate, and manage the solutions for its stakeholders. On the other hand, for the same industry how online and store channel operates can vary a lot. Therefore, the markdown solution for the online channel may be operated by one team, whereas the same type of solution for the store channel may be operated by a different team. Should such a team develop its own platform or data/infrastructure subsystems?
Put simply, a more diverse cybersecurity team is a better cybersecurity team. In a multidisciplinary field like this, different perspectives are critical. When threats and tactics change around us daily, the diverse viewpoints on my team help counter complacency by bringing new thinking to situations. Our adversaries, after all, are continuously trying new tactics, finding new ways to bypass controls and identify vulnerabilities. My team’s different perspectives bring a more disruptive “hacker mindset” to our work in countering attacks. Our industry’s overreliance on specialists with the “right” qualifications and educational backgrounds might actually be a weakness — a point of view reinforced for me by David Epstein’s 2019 book, “Range: Why Generalists Triumph in a Specialized World.” Epstein argues that generalists with wide-ranging interests are more creative, more agile and able to make connections that their more specialized peers can’t see, especially in complex and unpredictable fields — a description that is a good fit for cybersecurity.
2022 will see more Gen Z in employment than even before. Organizational culture will have to create space to address the needs of Gen Z. Multiple research shows that this generation has higher conviction in their own strengths and profound belief in dialogue. That means organizations will have to create accessible and better platforms for frequent and candid dialogue and train their Gen X and Gen Y leaders to be open to diverse views. McKinsey’s survey reveals this generation’s quest for truth. As per McKinsey, Gen Z is “True Gen” in contrast to Gen Y - the millennials, sometimes called the “Me Generation”. To attract and retain Gen Z, HR leaders will have to catalyze genuine culture of greatness at the workplace rather than just the labels and brands. They will have to ensure providing this experience of Truth to even interns, as this New Generation is more likely to rely on the experience of their peers rather than labels and brands. When it comes to technology and business models, we're in the midst of a revolution that can't be separated.
Many of today’s problems are so massive that no single entity can solve them on its own. These problems can be tackled only by networks of companies and institutions that work together toward a common purpose. For example, think about people’s need for mobility—which requires dealing with public, shared, and privately owned methods of transportation; infrastructure; public 5G networks; energy supply; financing; regulation; and many more factors. The only way for companies to thrive in this disruptive age is to work with ecosystems and harness the capabilities that others have built in order to deliver their own value propositions—and do so at speed, at scale, and flexibly. When a labor shortage loomed in Japan’s construction industry in 2013, Komatsu tried to address the problem by introducing ICT (information and communications technology) construction machinery that used GPS, digital mapping, sensors, and internet-of-things connections to enhance efficiency. But leaders quickly saw that the new machines were not resulting in the expected increase in productivity. The reason? Bottlenecks in processes at the construction site.
Under budget pressure to deliver more with less, CISOs want to consolidate their tech stacks and save the budget for new technologies. Unified Endpoint Management (UEM) proves its value by unifying identity, security, and remote access within Zero Trust Security or ZTNA frameworks now considered essential for securing an anywhere workforce. Like ZTNA, there’s been rapid innovation occurring in UEM over the last twelve months, with reduced security and compliance risks being the goal. UEM’s benefits include streamlining continuous OS updates across multiple mobile devices and platforms, enabling device management, and having an architecture capable of supporting a wide range of devices and operating systems. Another benefit enterprises mention is automating internet-based patching, policy, and configuration management. Unified Endpoint Management (UEM) leaders include Ivanti, whose platform reflects industry leadership with advanced unified endpoint management capabilities.
Important lessons were learned during the pandemic, not least that banking and other financial institutions’ business models, created pre-COVID, were not suitable for weathering a major crisis. As noted by McKinsey, this could be due to the fact that most business models rely on historical data, “without access to high-frequency data that would enable recalibration”, as well as infrastructure that lacks the agility for effective risk management. In the current landscape of economic recovery, plus the need to navigate the effects of the UK leaving the EU, evolving regulations, intensified competition and more, it’s crucial for financial organisations to rethink their models and data strategies now to strengthen future resilience. Therefore, attention will turn to implementing DataOps practices to make themselves nimble enough to identify and react to sudden micro and macro issues, integrate robust risk assessment and mitigation, and capitalise on newly emerging market opportunities. Further, the digital economy in which we live necessitates an elevated approach to engaging with consumers, who have become accustomed to instantaneous, always-on digital and omnichannel communication and personalisation of products and services on convenient platforms
If you have existing documentation and people know about it, you’re doing great! The last hurdle to overcome is making sure that your documentation stays up to date. As time passes, processes change and wikis naturally get out of date. Stale documentation with misleading info is the worst, so finding a good way to keep track of existing documentation and showing ownership in updating it when things change is the problem to solve here. New hires in this instance are again one of the best resources you have. If a new hire is setting up their app locally and runs into issues when following the setup documentation, they should take the time to update the documentation with the correct steps. If your company is actively hiring, this ensures that fresh eyes will be following and improving the documentation every month. The same goes for every other current employee. Any time someone finds information in a wiki that is incorrect, they should do their due diligence and update the documentation. Ignoring the bad information won’t make things any easier for the next person who stumbles across the same page.
This demonstration features ALGLIB, one of the better available numerical analysis libraries for C# programmers that offers several easy-to-use machine learning methods. (Later in this series, we will examine MS CNKT for C# routines.) ALGLIB for C# is available and licensed appropriately as a free, single-threaded edition for individual experimentation and use or as a commercial, multi-threaded edition for purchase. For this demonstration, I will explain at some length how to download and build the free edition as a class library that will need to be included as a reference for the demonstration program to work. For reference, you can go to the ALGLIB Wikipedia page for a description and history, the ALGLIB website for download information and an excellent on-line User’s Guide, and to the download itself for a detailed User’s Manual in .html format. ... Our small demonstration data set was built by selecting pre-classified student histories at random, while insuring a balanced set of demo data representing each status group.
The ability to code is crucial to good data scientists, that’s why almost every data science role has a technical round. But what’s equally important but sometimes overlooked is the ability to understand the business. Without the business acumen, data scientists will always be the passive implementer of tasks instead of the active thought partners that they should be. Moreover, only when you truly understand the asks and how they fit into the larger business, you are able to problem solve in creative ways without counting on others to prescribe a solution. ... On top of the technical challenge, construct a business case which candidates have to work through. The business case should closely align with the job description. If the role will be conducting a lot of metrics analyses, then the case could be a metrics decomposition type of question; if the role will be mainly building models, then the case could be a realistic business situation that candidates can brainstorm modeling solutions for.
For many organisations, multi-cloud is inevitable. After all, it’s unlikely there is a single cloud out there that can support all your requirements. Organisations typically use several, to dozens, to hundreds of SaaS products, as well as a handful of IaaS hosting services, and development PaaS. Some applications will work better on certain platforms – cloud native apps should be happy on AWS, Microsoft Azure or Google Cloud, but traditional apps might prefer Oracle Cloud or IBM Cloud. So, a multi-cloud approach enables you to create this best-of-breed environment. But there are also benefits to being able to run workloads across multiple hyperscale cloud environments – something that is being made easier through containerisation. The caveat is that success for a multi-cloud environment lies in bringing all the pieces together in harmony. It’s ensuring the right workload is distributed to the most appropriate cloud and making sure all the cloud services can communicate with one another. Organisations need to establish and understand the core connectivity between, and governance around, these disparate environments.
Quote for the day:
"Distinguished leaders impress, inspire and invest in other leaders." -- Anyaele Sam Chiyson