Team Topology for Machine Learning
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?
Connecting the dots on diversity in cybersecurity recruitment
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.
Workplace Trends 2022: The age of employees is here
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.
Seven imperatives for moving beyond digital
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.
Top 5 trends for endpoint security In 2022
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.
DataOps will play a pivotal role in financial services growth
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
Death to Tribal Knowledge
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.
Bezier Curve Machine Learning Demonstration
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.
Top Qualities Hiring Managers Look For In Data Scientist Candidates
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.
Why businesses should embrace multi-cloud
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
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