Quote for the day:
“Become the kind of leader that people would follow voluntarily, even if you had no title or position.” -- Brian Tracy
MLOps vs. DevOps: Key Differences — and Why They Work Better Together
Arguably, the greatest difference between DevOps and MLOps is that DevOps is, by
most definitions, an abstract philosophy, whereas MLOps comes closer to
prescribing a distinct set of practices. Ultimately, the point of DevOps is to
encourage software developers to collaborate more closely with IT operations
teams, based on the idea that software delivery processes are smoother when both
groups work toward shared goals. In contrast, collaboration is not a major focus
for MLOps. You could argue that MLOps implies that some types of collaboration
between different stakeholders — such as data scientists, AI model developers,
and model testers — need to be part of MLOps workflows. ... Another key
difference is that DevOps centers solely on software development. MLOps is also
partly about software development to the extent that model development entails
writing software. However, MLOps also addresses other processes — like model
design and post-deployment management — that don't overlap closely with DevOps
as traditionally defined. ... Differing areas of focus lead to different skill
requirements for DevOps versus MLOps. To thrive at DevOps, you must master
DevOps tools and concepts like CI/CD and infrastructure-as-code (IaC).
Transforming quality engineering with AI
AI-enabled quality engineering promises to be a game changer, driving a level
of precision and efficiency that is beyond the reach of traditional testing.
AI algorithms can analyse historical data to identify patterns and predict
quality issues, enabling organisations to take early action; machine learning
tools detect anomalies with great accuracy, ensuring nothing is missed.
Self-healing test scripts update automatically, without manual intervention.
Machine Learning models automate test selection, picking the most relevant
ones, while reducing both manual effort and errors. In addition, AI can
prioritise test cases based on criticality, thus optimising resources and
improving testing outcomes. Further, it can integrate with CI/CD pipelines,
providing real-time feedback on code quality, and distributing updates
automatically to ensure software applications are always ready for deployment.
... AI brings immense value to quality engineering, but also presents a few
challenges. To function effectively, algorithms require high-quality datasets,
which may not always be available. Organisations will likely need to invest
significant resources in acquiring AI talent or building skills in-house.
There needs to be a clear plan for integrating AI with existing testing tools
and processes. Finally, there are concerns such as protecting data privacy and
confidentiality, and implementing Responsible AI.
The Role of AI in Global Governance
Aurora drew parallels with transformative technologies such as electricity and
the internet. "If AI reaches some communities late, it sets them far behind,"
he said. He pointed to Indian initiatives such as Bhashini for language
inclusion, e-Sanjeevani for telehealth, Karya for employment through AI
annotation and farmer.ai in Baramati, which boosted farmers' incomes by 30% to
40%. Schnorr offered a European perspective, stressing that AI's
transformative impact on economies and societies demands trustworthiness.
Reflecting on the EU's AI Act, he said its dual aim is fostering innovation
while protecting rights. "We're reviewing the Act to ensure it doesn't hinder
innovation," Schnorr said, advocating for global alignment through frameworks
such as the G7's Hiroshima Code of Conduct and bilateral dialogues with India.
He underscored the need for rules to make AI human-centric and accessible,
particularly for small and medium enterprises, which form the backbone of both
German and Indian economies. ... Singh elaborated on India's push for
indigenous AI models. "Funding compute is critical, as training models is
resource-intensive. We have the talent and datasets," he said, citing India's
second-place ranking in GitHub AI projects per the Stanford AI Index.
"Building a foundation model isn't rocket science - it's about providing the
right ingredients."
Cisco ThousandEyes: resilient networks start with global insight
To tackle the challenges that arise from (common or uncommon)
misconfigurations and other network problems, we need an end-to-end topology,
Vaccaro reiterates. ThousandEyes (and Cisco as a whole) have recently put a
lot of extra work into this. We saw a good example of this recently during
Mobile World Congress. There, ThousandEyes announced Connected Devices. This
is intended for service providers and extends their insight into the
performance of their customers’ networks in their home environments. The goal,
as Vaccaro describes it, is to help service providers see deeper so that they
can catch an outage or other disruption quickly, before it impacts customers
who might be streaming their favorite show or getting on a work call. ...
The Digital Operational Resilience Act (DORA) will be no news to readers who
are active in the financial world. You can see DORA as a kind of advanced
NIS2, only directly enforced by the EU. It is a collection of best practices
that many financial institutions must adhere to. Most of it is fairly obvious.
In fact, we would call it basic hygiene when it comes to resilience. However,
one component under DORA will have caused financial institutions some stress
and will continue to do so: they must now adhere to new expectations when it
comes to the services they provide and the resilience of their third-party ICT
dependencies.
A Five-Step Operational Maturity Model for Benchmarking Your Team
An operational maturity model is your blueprint for building digital excellence.
It gives you the power to benchmark where you are, spot the gaps holding you
back and build a roadmap to where you need to be. ... Achieving operational
maturity starts with knowing where you are and defining where you want to go.
From there, organizations should focus on four core areas: Stop letting silos
slow you down. Unify data across tools and teams to enable faster incident
resolution and improve collaboration. Integrated platforms and a shared data
view reduce context switching and support informed decision-making. Because in
today’s fast-moving landscape, fragmented visibility isn’t just inefficient —
it’s dangerous. ... Standardize what matters. Automate what repeats. Give your
teams clear operational frameworks so they can focus on innovation instead of
navigation. Eliminate alert noise and operational clutter that’s holding your
teams back. Less noise, more impact. ... Deploy automation and AI across the
incident lifecycle, from diagnostics to communication. Prioritize tools that
integrate well and reduce manual tasks, freeing teams for higher-value work. ...
Use data and automation to minimize disruptions and deliver seamless
experiences. Communicate proactively during incidents and apply learnings to
prevent future issues.
The Future is Coded: How AI is Rewriting the Rules of Decision Theaters
At the heart of this shift is the blending of generative AI with strategic
foresight practices. In the past, planning for the future involved static models
and expert intuition. Now, AI models (including advanced neural networks) can
churn through reams of historical data and real-time information to project
trends and outcomes with uncanny accuracy. Crucially, these AI-powered
projections don’t operate in a vacuum – they’re designed to work with human
experts. By integrating AI’s pattern recognition and speed with human intuition
and domain expertise, organizations create a powerful feedback loop. ... The
fusion of generative AI and foresight isn’t confined to tech companies or
futurists’ labs – it’s already reshaping industries. For instance, in finance,
banks and investment firms are deploying AI to synthesize market signals and
predict economic trends with greater accuracy than traditional econometric
models. These AI systems can simulate how different strategies might play out
under various future market conditions, allowing policymakers in central banks
or finance ministries to test interventions before committing to them. The
result is a more data-driven, preemptive strategy – allowing decision-makers to
adjust course before a forecasted risk materializes.
More accurate coding: Researchers adapt Sequential Monte Carlo for AI-generated code
The researchers noted that AI-generated code can be powerful, but it can also
often lead to code that disregards the semantic rules of programming languages.
Other methods to prevent this can distort models or are too time-consuming.
Their method makes the LLM adhere to programming language rules by discarding
code outputs that may not work early in the process and “allocate efforts
towards outputs that more most likely to be valid and accurate.” ... The
researchers developed an architecture that brings SMC to code generation “under
diverse syntactic and semantic constraints.” “Unlike many previous frameworks
for constrained decoding, our algorithm can integrate constraints that cannot be
incrementally evaluated over the entire token vocabulary, as well as constraints
that can only be evaluated at irregular intervals during generation,” the
researchers said in the paper. Key features of adapting SMC sampling to model
generation include proposal distribution where the token-by-token sampling is
guided by cheap constraints, important weights that correct for biases and
resampling which reallocates compute effort towards partial generations. ... AI
models have made engineers and other coders work faster and more efficiently.
It’s also given rise to a whole new kind of software engineer: the vibe
coder.
You Can't Be in Recovery Mode All the Time — Superna CEO
The proactive approach, he explains, shifts their position in the security
lifecycle: "Now we're not responding with a very tiny blast radius and instantly
recovering. We are officially left-of-the-boom; we are now ‘the incident never
occurred.’" Next, Hesterberg reveals that the next wave of innovation focuses on
leveraging the unique visibility his company has in terms of how critical data
is accessed. “We have a keen understanding of where your critical data is and
what users, what servers, and what services access that data.” From a scanning,
patching, and upgrade standpoint, Hesterberg shares that large organizations
often face the daunting task of addressing hundreds or even thousands of systems
flagged for vulnerabilities daily. To help streamline this process, he says that
his team is working on a new capability that integrates with the tools these
enterprises already depend on. This upcoming feature will surface, in a
prioritized way, the specific servers or services that interact with an
organization's most critical data, highlighting the assets that matter most. By
narrowing down the list, Hesterberg notes, teams can focus on the most
potentially dangerous exposures first. Instead of trying to patch everything, he
says, “If you know the 15, 20, or 50 that are most dangerous, potentially most
dangerous, you're going to prioritize them.”
When confusion becomes a weapon: How cybercriminals exploit economic turmoil
Defending against these threats doesn’t start with buying more tools. It starts
with building a resilient mindset. In a crisis, security can’t be an
afterthought – it must be a guiding principle. Organizations relying on informal
workflows or inconsistent verification processes are unknowingly widening their
attack surface. To stay ahead, protocols must be defined before uncertainty
takes hold. Employees should be trained not just to spot technical anomalies,
but to recognize emotional triggers embedded in legitimate looking
messages. Resilience, at its core, is about readiness. Not just to respond,
but to also anticipate. Organizations that view economic disruption as a dual
threat, both financial and cyber, will position themselves to lead with control
rather than react in chaos. This means establishing behavioral baselines,
implementing layered authentication, and adopting systems that validate not just
facilitate. As we navigate continued economic uncertainty, we are reminded once
again that cybersecurity is no longer just about technology. It’s about
psychology, communication, and foresight. Defending effectively means thinking
tactically, staying adaptive, and treating clarity as a strategic asset.
The productivity revolution – enhancing efficiency in the workplace
In difficult economic times, when businesses are tightening the purse strings,
productivity improvements may often be overlooked in favour of cost reductions.
However, cutting costs is merely a short-term solution. By focusing on
sustainable productivity gains, businesses will reap dividends in the long term.
To achieve this, organisations must turn their focus to technology. Some
technology solutions, such as cloud computing, ERP systems, project management
and collaboration tools, produce significant flexibility or performance
advantages compared to legacy approaches and processes. Whilst an initial
expense, the long-term benefits are often multiples of the investment – cost
reductions, time savings, employee motivation, to name just a few. And all of
those technology categories are being enhanced with artificial intelligence –
for example adding virtual agents to help us do more, quickly. ... At a time
when businesses and labour markets are struggling with employee retention and
availability, it has become more critical than ever for organisations to focus
on effective training and wellbeing initiatives. Minimising staff turnover and
building up internal skill sets is vital for businesses looking to improve their
key outputs. Getting this right will enable organisations to build smarter and
more effective productivity strategies.
