strategictech

[strategictech]

Hyperscale cloud: Expectations versus reality

With all the marketing around hyperscale cloud, you’d assume that adopting it would be easy and simple. And the irony is that it once was. When first brought to market, hyperscalers like AWS, Azure and GCP wanted to ensure their services were straightforward. But as time has gone on, these solutions have become much more complex – to the point that, often, specialist training is needed right from the start. 

In recent years, this increasing complexity has started to influence businesses to reconsider their hyperscale cloud usage in favour of alternative infrastructure solutions like colocation and bare metal hosting. In fact, 94% of large US organisations claim to have worked on some sort of cloud repatriation project in the last three years.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

How to Create and Manage Python Virtual Environments

With over 600,000 projects on PyPI (and counting), managing Python dependencies can be tricky. One package may require pandas 1.3.0, while another demands pandas 2.0.0. Installing one version may break another, bringing your project to a grinding halt. Wouldn’t it be great if we could create a Python environment with only the packages and versions we need? Enter: virtual environments.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

Data Product vs. Data as a Product (DaaP)

Discover the difference between Data Products & Data-as-a-Product (DaaP) and how they impact data quality, accessibility, and value.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

Your Data is Lying to You: A Guide to Detecting Information Anomalies

Organizations today rely heavily on data to inform their decision-making processes at every level. However, the increasing complexity of data ecosystems poses a challenge: The data we rely on may not always tell the whole truth.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

MLCommons Releases MLPerf Inference v5.0 Benchmark Results

Today, MLCommons announced new results for its MLPerf Inference v5.0 benchmark suite, which delivers machine learning (ML) system performance benchmarking. The rorganization said the esults highlight that the AI community is focusing on generative AI, and that the combination of recent hardware and software advances optimized for generative AI have led to performance improvements over the past year.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

NVIDIA Open Sources Run:ai Scheduler

Today, NVIDIA posted a blog announcing the open-source release of the KAI Scheduler, a Kubernetes-native GPU scheduling solution, now available under the Apache 2.0 license. 

Originally developed within the Run:ai platform, KAI Scheduler is now available to the community while also continuing to be packaged and delivered as part of the NVIDIA Run:ai platform.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

Grafana’s Annual Report Uncovers Key Insights into the Future of Observability

The architecture of modern systems has reached a tipping point where observability is no longer optional, it’s become existential. With advancements in Kubernetes and OpenTelemetry reshaping operational strategies, organizations are turning to open-source solutions to tackle the challenges of complex and distributed systems.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

Quality of Low-Code/No-Code Development Platforms Through the Lens of ISO 25010:2023

This article assesses the quality of low-code/no-code development platforms through the lens of ISO 25010:2023. While these platforms enable usability and reduce development speed, there are challenges in complexity and customizable business needs limiting their effectiveness for high-performance and high-risk environments.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

Machine Learning Approaches to Code Similarity Measurement: A Systematic Review

Source code similarity measurement, which involves assessing the degree of difference between code segments, plays a crucial role in various aspects of the software development cycle. These include but are not limited to code quality assurance, code review processes, code plagiarism detection, security, and vulnerability analysis. Despite the increasing application of ML technique in this domain, a comprehensive synthesis of existing methodologies remains lacking. This paper presents a systematic review of Machine Learning techniques applied to code similarity measurement, aiming to illuminate current methodologies and contribute valuable insights to the research community. Following a rigorous systematic review protocol, we identified and analyzed 84 primary studies on a broad spectrum of dimensions covering application type, devised Machine Learning algorithms, used code representations, datasets, and performance metrics, as well as performance evaluations. A deep investigation reveals that 15 applications for code similarity measurement have utilized 51 different machine learning algorithms. Additionally, the most prevalent code representation is found to be the abstract syntax tree (AST). Furthermore, the most frequently employed dataset across various code similarity research applications is BigCloneBench. Through this comprehensive analysis, the paper not only synthesizes existing research but also identifies prevailing limitations and challenges, shedding light on potential avenues for future work.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

Multisensory Experiences: Formation, Realization, and Responsibilities

Imagine trying to create the sensory impression of an object, say, the vibrant beauty of a sunflower. It is not just about visualizing its colors but also about feeling the textures and perceiving the delicate fragrances, all encapsulated within an event.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

Systems Correctness Practices at AWS

AWS (Amazon Web Services) strives to deliver reliable services that customers can trust completely. This demands maintaining the highest standards of security, durability, integrity, and availability—with systems correctness serving as the cornerstone for achieving these priorities. An April 2015 paper published in Communications of the ACM, titled "How Amazon Web Services Uses Formal Methods," highlighted the approach for ensuring the correctness of critical services that have since become among the most widely used by AWS customers.

Central to this approach was TLA+, a formal specification language developed by Leslie Lamport. Our experience at AWS with TLA+ revealed two significant advantages of applying formal methods in practice. First, we could identify and eliminate subtle bugs early in development—bugs that would have eluded traditional approaches like testing. Second, we gained the deep understanding and confidence needed to implement aggressive performance optimizations while maintaining systems correctness. 

Moreover, 15 years ago, AWS's software testing practice relied primarily on build-time unit testing, often against mocks, and limited deployment-time integration testing. Since then, we have significantly evolved our correctness practices, integrating both formal and semi-formal approaches into the development process. As AWS has grown, formal methods have become increasingly valuable—not only for ensuring correctness but also for performance improvements, particularly in verifying the correctness of both low- and high-level optimizations. This systematic approach toward systems correctness has become a force multiplier at AWS's scale, enabling faster development cycles through improved developer velocity while delivering more cost-effective services to customers. 

This article surveys the portfolio of formal methods used across AWS to deliver complex services with high confidence in its correctness. We consider an umbrella definition of formal methods that encompasses these rigorous techniques—from traditional formal approaches such as theorem proving, deductive verification, and model checking to more lightweight semi-formal approaches such as property-based testing, fuzzing, and runtime monitoring.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

How AlexNet Transformed AI and Computer Vision Forever

In a historic move, the Computer History Museum, in partnership with Google, has released the original 2012 source code for AlexNet, the neural network that revolutionized AI.

Developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, AlexNet's source code release is a monumental moment for AI enthusiasts.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

Everyone in AI is talking about Manus. We put it to the test.

Everyone in AI is talking about Manus. We put it to the test. The new general AI agent from China had some system crashes and server overload—but it’s highly intuitive and shows real promise for the future of AI helpers.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

What Does it Take to Be an Impactful Data Leader?

Clearly, there is no prescribed set of skills, experiences, attitudes, or behaviors that when consistently exhibited will guarantee a data leader any measure of success. Ask a group of high-functioning data leaders what it takes to engender success in their role. 

Although they may appear to disagree on the surface, their insights — forged in battle, refined in loss, and verified in victory — gravitate around common themes.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

Predictive analytics: Transforming data into future insights

Predictive analytics and predictive AI can help your organization forecast outcomes based on historical data and analytics techniques.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

Major Enhancements Coming in Java 24

The latest release of Java Development Kit (JDK) 24, scheduled for release on March 18, introduces 24 new features, marking the most substantial update to the platform since 2018. Key changes include faster application startup, enhanced concurrency, security overhauls, and a move away from outdated APIs, reflecting a broader effort to modernize Java while maintaining its widespread enterprise adoption.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[strategictech]

The Agile Prompt Engineering Framework

In this article, you will learn about the Agile Prompt Engineering Framework, which is a structured approach explicitly designed for agile practitioners.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X