Quote for the day:
"Develop success from failures. Discouragement and failure are two of the surest stepping stones to success." -- Dale Carnegie
Technology skills gap plagues industries, and upskilling is a moving target
“The deepening threat landscape and rapidly evolving high-momentum technologies
like AI are forcing organizations to move with lightning speed to fill specific
gaps in their job architectures, and too often they are stumbling,” said David
Foote, chief analyst at consultancy Foote Partners. To keep up with the rapidly
changing landscape, Gartner suggests that organizations invest in agile learning
for tech teams. “In the context of today’s AI-fueled accelerated disruption,
many business leaders feel learning is too slow to respond to the volume,
variety and velocity of skills needs,” said Chantal Steen, a senior director in
Gartner’s HR practice. “Learning and development must become more agile to
respond to changes faster and deliver learning more rapidly and more cost
effectively.” Studies from staffing firm ManpowerGroup, hiring platform Indeed,
and Deloitte consulting show that tech hiring will focus on candidates with
flexible skills to meet evolving demands. “Employers know a skilled and
adaptable workforce is key to navigating transformation, and many are
prioritizing hiring and retaining people with in-demand flexible skills that can
flex to where demand sits,” said Jonas Prising, ManpowerGroup chair and CEO.
Mixture of Experts (MoE) Architecture: A Deep Dive & Comparison of Top Open-Source Offerings
The application of MoE to open-source LLMs offers several key advantages.
Firstly, it enables the creation of more powerful and sophisticated models
without incurring the prohibitive costs associated with training and deploying
massive, single-model architectures. Secondly, MoE facilitates the development
of more specialized and efficient LLMs, tailored to specific tasks and domains.
This specialization can lead to significant improvements in performance,
accuracy, and efficiency across a wide range of applications, from natural
language translation and code generation to personalized education and
healthcare. The open-source nature of MoE-based LLMs promotes collaboration and
innovation within the AI community. By making these models accessible to
researchers, developers, and businesses, MoE fosters a vibrant ecosystem of
experimentation, customization, and shared learning. ... Integrating MoE
architecture into open-source LLMs represents a significant step forward in the
evolution of artificial intelligence. By combining the power of specialization
with the benefits of open-source collaboration, MoE unlocks new possibilities
for creating more efficient, powerful, and accessible AI models that can
revolutionize various aspects of our lives.
The DeepSeek Disruption and What It Means for CIOs
The emergence of DeepSeek has also revived a long-standing debate about
open-source AI versus proprietary AI. Open-source AI is not a silver bullet.
CIOs need to address critical risks as open-source AI models, if not secured
properly, can be exposed to grave cyberthreats and adversarial attacks. While
DeepSeek currently shows extraordinary efficiency, it requires an internal
infrastructure, unlike GPT-4, which can seamlessly scale on OpenAI's cloud.
Open-source AI models lack support and skills, thereby mandating users to build
their own expertise, which could be demanding. "What happened with DeepSeek is
actually super bullish. I look at this transition as an opportunity rather than
a threat," said Steve Cohen, founder of Point72. ... The regulatory
non-compliance adds another challenge as many governments restrict and disallow
sensitive enterprise data from being processed by Chinese technologies. A
possibility of potential backdoor can't be ruled out and this could open the
enterprises to additional risks. CIOs need to conduct extensive security audits
before deploying DeepSeek. rganizations can implement safeguards such as
on-premises deployment to avoid data exposure. Integrating strict encryption
protocols can help the AI interactions remain confidential, and performing
rigorous security audits ensure the model's safety before deploying it into
business workflows.
Why GreenOps will succeed where FinOps is failing
The cost-control focus fails to engage architects and engineers in rethinking
how systems are designed, built and operated for greater efficiency. This lack
of engagement results in inertia and minimal progress. For example, the
database team we worked with in an organization new to the cloud launched all
the AWS RDS database servers from dev through production, incurring a $600K a
month cloud bill nine months before the scheduled production launch. The
overburdened team was not thinking about optimizing costs, but rather
optimizing their own time and getting out of the way of the migration team as
quickly as possible. ... GreenOps — formed by merging FinOps, sustainability
and DevOps — addresses the limitations of FinOps while integrating
sustainability as a core principle. Green computing contributes to GreenOps by
emphasizing energy-efficient design, resource optimization and the use of
sustainable technologies and platforms. This foundational focus ensures that
every system built under GreenOps principles is not only cost-effective but
also minimizes its environmental footprint, aligning technological innovation
with ecological responsibility. Moreover, we’ve found that providing emissions
feedback to architects and engineers is a bigger motivator than cost to
inspire them to design more efficient systems and build automation to shut
down underutilized resources.
Best Practices for API Rate Limits and Quotas
Unlike short-term rate limits, the goal of quotas is to enforce business terms
such as monetizing your APIs and protecting your business from high-cost
overruns by customers. They measure customer utilization of your API over
longer durations, such as per hour, per day, or per month. Quotas are not
designed to prevent a spike from overwhelming your API. Rather, quotas
regulate your API’s resources by ensuring a customer stays within their agreed
contract terms. ... Even a protection mechanism like rate limiting could have
errors. For example, a bad network connection with Redis could cause reading
rate limit counters to fail. In such scenarios, it’s important not to
artificially reject all requests or lock out users even though your Redis
cluster is inaccessible. Your rate-limiting implementation should fail open
rather than fail closed, meaning all requests are allowed even though the rate
limit implementation is faulting. This also means rate limiting is not a
workaround to poor capacity planning, as you should still have sufficient
capacity to handle these requests or even design your system to scale
accordingly to handle a large influx of new requests. This can be done through
auto-scale, timeouts, and automatic trips that enable your API to still
function.
Protecting Ultra-Sensitive Health Data: The Challenges
Protecting ultra-sensitive information "is an incredibly confusing and
complicated and evolving part of the law," said regulatory attorney Kirk Nahra
of the law firm WilmerHale. "HIPAA generally does not distinguish between
categories of health information," he said. "There are exceptions - including
the recent Dobbs rule - but these are not fundamental in their application, he
said. Privacy protections related to abortion procedures are perhaps the most
hotly debated type of patient information. For instance, last June - in response
to the June 2022 Supreme Court's Dobbs ruling, which overturned the national
right to abortion - the Biden administration's U.S. Department of Health and
Human Services modified the HIPAA Privacy Rule to add additional safeguards for
the access, use and disclosure of reproductive health information. The rule is
aimed at protecting women from the use or disclosure of their reproductive
health information when it is sought to investigate or impose liability on
individuals, healthcare providers or others who seek, obtain, provide or
facilitate reproductive healthcare that is lawful under the circumstances in
which such healthcare is provided. But that rule is being challenged in federal
court by 15 state attorneys general seeking to revoke the regulations.
Evolving threat landscape, rethinking cyber defense, and AI: Opportunties and risk
Businesses are firmly in attackers’ crosshairs. Financially motivated
cybercriminals conduct ransomware attacks with record-breaking ransoms being
paid by companies seeking to avoid business interruption. Others, including
nation-state hackers, infiltrate companies to steal intellectual property and
trade secrets to gain commercial advantage over competitors. Further, we
regularly see critical infrastructure being targeted by nation-state
cyberattacks designed to act as sleeper cells that can be activated in times of
heightened tension. Companies are on the back foot. ... As zero trust disrupts
obsolete firewall and VPN-based security, legacy vendors are deploying firewalls
and VPNs as virtual machines in the cloud and calling it zero trust
architecture. This is akin to DVD hardware vendors deploying DVD players in a
data center and calling it Netflix! It gives a false sense of security to
customers. Organizations need to make sure they are really embracing zero trust
architecture, which treats everyone as untrusted and ensures users connect to
specific applications or services, rather than a corporate network. ...
Unfortunately, the business world’s harnessing of AI for cyber defense has been
slow compared to the speed of threat actors harnessing it for attacks.
Six essential tactics data centers can follow to achieve more sustainable operations
By adjusting energy consumption based on real-time demand, data centers can
significantly enhance their operational efficiency. For example, during periods
of low activity, power can be conserved by reducing energy use, thus minimizing
waste without compromising performance. This includes dynamic power management
technologies in switch and router systems, such as shutting down unused line
cards or ports and controlling fan speeds to optimize energy use based on
current needs. Conversely, during peak demand, operations can be scaled up to
meet increased requirements, ensuring consistent and reliable service levels.
Doing so not only reduces unnecessary energy expenditure, but also contributes
to sustainability efforts by lowering the environmental impact associated with
energy-intensive operations. ... Heat generated from data center operations can
be captured and repurposed to provide heating for nearby facilities and homes,
transforming waste into a valuable resource. This approach promotes a circular
energy model, where excess heat is redirected instead of discarded, reducing the
environmental impact. Integrating data centers into local energy systems
enhances sustainability and offers tangible benefits to surrounding areas and
communities whilst addressing broader energy efficiency goals.
The Engineer’s Guide to Controlling Configuration Drift
“Preventing configuration drift is the bedrock for scalable, resilient
infrastructure,” comments Mayank Bhola, CTO of LambdaTest, a cloud-based testing
platform that provides instant infrastructure. “At scale, even small
inconsistencies can snowball into major operational inefficiencies. We
encountered these challenges [user-facing impact] as our infrastructure scaled
to meet growing demands. Tackling this challenge head-on is not just about
maintaining order; it’s about ensuring the very foundation of your technology is
reliable. And so, by treating infrastructure as code and automating compliance,
we at LambdaTest ensure every server, service, and setting aligns with our
growth objectives, no matter how fast we scale. Adopting drift detection and
remediation strategies is imperative for maintaining a resilient infrastructure.
... The policies you set at the infrastructure level, such as those for SSH
access, add another layer of security to your infrastructure. Ansible allows you
to define policies like removing root access, changing the default SSH port, and
setting user command permissions. “It’s easy to see who has access and what they
can execute,” Kampa remarks. “This ensures resilient infrastructure, keeping
things secure and allowing you to track who did what if something goes
wrong.”
Strategies for mitigating bias in AI models
The need to address bias in AI models stems from the fundamental principle of
fairness. AI systems should treat all individuals equitably, regardless of their
background. However, if the training data reflects existing societal biases, the
model will likely reproduce and even exaggerate those biases in its outputs. For
instance, if a facial recognition system is primarily trained on images of one
demographic, it may exhibit lower accuracy rates for other groups, potentially
leading to discriminatory outcomes. Similarly, a natural language processing
model trained on predominantly Western text may struggle to understand or
accurately represent nuances in other languages and cultures. ... Incorporating
contextual data is essential for AI systems to provide relevant and culturally
appropriate responses. Beyond basic language representation, models should be
trained on datasets that capture the history, geography, and social issues of
the populations they serve. For instance, an AI system designed for India should
include data on local traditions, historical events, legal frameworks, and
social challenges specific to the region. This ensures that AI-generated
responses are not only accurate but also culturally sensitive and context-aware.
Additionally, incorporating diverse media formats such as text, images, and
audio from multiple sources enhances the model’s ability to recognise and adapt
to varying communication styles.
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