Adeel Aslam

Experience: 16 Years

Enterprise Applications Developer using Java EE 6, using JPA, JSF 2.0 and enclosing Oracle Java EE technologies. Java Enterprise Developer, Oracle Certified Associate Database 10g. Mobile Application Development in Android, Gerrit, Jenkins, JUnit, Selenium, Puppet, GlassFish, Tomcat, Maven, KVM, MongoDB, JNI and IBM FileNet. Brainbench Java Enterprise and EJB 3.0 Job Roles Certified. Machine Learning and AI Enthusiast since 2018 and currently a part of ML Division.

September 3, 2026
Data Augmentation Techniques for Improving Model Robustness
Learn how data augmentation techniques improve model robustness, which methods fit each data type, and how to measure whether your model actually got stronger.
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June 5, 2026
From Feasibility to Functionality: Running a Successful Generative AI Readiness Assessment
Learn how to run a practical generative AI readiness assessment across workflow fit, data quality, governance, platform readiness, risk, and operating model design.
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June 5, 2026
Architecting AI-First Business Processes: Technical Considerations in Generative AI Strategy
Learn how to design AI-first business processes with governed retrieval, tool use, identity controls, evaluation, observability, and safe execution.
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April 29, 2026
The Art of Quantum: Bridging Quantum Computing with Creative Expression
Explore how quantum computing concepts like superposition, interference, entanglement, and measurement can inspire creative workflows, generative art, product design, and better team decision making without needing a physics degree.
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April 29, 2026
Technology Selection and Integration: A Technical Expert's Framework for Evaluating the Right Tech Stack
A practical guide to technology stack selection comparing Java, Python, AI platforms, prompt workflows, and machine learning approaches. Learn how to choose the right stack based on system needs, team structure, and product maturity rather than trends or language popularity.
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April 29, 2026
Deep Java Library (DJL): A Practical Deep Dive for Java, Python, and Hybrid Teams
Deep Java Library (DJL) is an open-source, engine-agnostic deep learning framework for Java that enables developers to run and deploy AI models directly in JVM-based applications. This guide explains what DJL is, how it works, and how Java and Python developers can quickly get started with inference, training, and production-ready AI integration without rewriting their existing stack.
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October 29, 2025
Why I Ditched Electron for Qt (And You Probably Should Too)
A candid look at a developer’s journey from sluggish Electron apps to the power and elegance of Qt. This personal story explores how ditching web wrappers for native performance reignited a love for desktop development.
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October 27, 2025
Building Killer Custom UI Components in Flutter: Why Everyone's Ditching Pre-Made Libraries
Discover why building custom UI components in Flutter beats relying on pre-built packages. Learn how custom components improve performance, design consistency, and developer control, plus a step-by-step guide to creating your own professional-grade component library.
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October 23, 2025
Why Scala's Functional Programming Will Change How You Code
Discover how Scala’s functional programming can transform the way you write code. This guide helps Python and Java developers understand key concepts like immutability, pure functions, and function composition, showing how Scala makes concurrent, scalable, and bug-resistant software easier to build.
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September 22, 2025
How to Get Real ROI from an AI-Powered IDS in Production - IDS Series (Part 3)
Practical guide to AI-powered intrusion detection: real-world best practices, explainability, continuous learning, ROI insights, and lessons from the field to build resilient, business-ready cybersecurity systems.
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September 15, 2025
How to Implement an AI-Powered Intrusion Detection System (IDS Series Part 2)
Learn how to design and deploy an AI-powered Intrusion Detection System (IDS) that detects threats faster, reduces false positives, and strengthens your cybersecurity posture.
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September 5, 2025
Why Traditional IDS Fall Short: The Need for Smarter, AI-Powered Security (IDS Series - Part 1)
Discover why traditional IDS fails against modern threats and how AI-powered detection helps CISOs, SOCs, and engineers cut noise, spot real risks, and stay secure.
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Adeel Aslam

Data Augmentation Techniques for Improving Model Robustness

From Feasibility to Functionality: Running a Successful Generative AI Readiness Assessment

Architecting AI-First Business Processes: Technical Considerations in Generative AI Strategy

The Art of Quantum: Bridging Quantum Computing with Creative Expression

Technology Selection and Integration: A Technical Expert's Framework for Evaluating the Right Tech Stack

Deep Java Library (DJL): A Practical Deep Dive for Java, Python, and Hybrid Teams

Why I Ditched Electron for Qt (And You Probably Should Too)

Building Killer Custom UI Components in Flutter: Why Everyone's Ditching Pre-Made Libraries

Why Scala's Functional Programming Will Change How You Code

How to Get Real ROI from an AI-Powered IDS in Production - IDS Series (Part 3)

How to Implement an AI-Powered Intrusion Detection System (IDS Series Part 2)

Why Traditional IDS Fall Short: The Need for Smarter, AI-Powered Security (IDS Series - Part 1)

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