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    Data Engineering

  • Jul 20 2022 - DEM5 : Data Pipelines Best Practices - Complexities & Considerations

    Data pipelines or otherwise also known as ETL pipelines are simply a sequence of actions that deliver data from source to desitnation both effciently and reliably to make it suitable for driving analytics and valuable insights. In this blog, i discuss the difficulties in building these pipelines and the core capabilities that ought to be embraced.

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  • Jun 01 2022 - DEM4 : TBA

    YTD

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  • May 18 2022 - DEM3 : TBA

    YTD.

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  • Feb 20 2022 - DEM2 : Considerations when Designing For a Data Ecosystem

    Data pipeline can be viewed as the sum of all actions it takes to move data from source to destination, which is typically a data lake through software engineering best practices. What goes into the design, implementation and scalability so that it brings business value to the table?

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  • Feb 17 2022 - DEM1 : Data Processing Patterns ~ ETL to ELT

    ETL processes are no longer enough to manage the diversity of data types and ingestion styles of the modern data landscape. Cloud driven ELT patterns enable for greater scalability and elasticity. In this blog, i investigate the key considerations for a shift to ELT ingestion patterns and the accompanying data management strategy for putting in place.

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  • Machine Learning Operations

  • May 14 2023 - MLE5 : ML Operations Demystified ~ What Is It & Why It Matters

    MLOps is an extended concept borrowed from DevOps in software engineering.

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  • Feb 21 2022 - MLE4 : Evaluating Data Science Platforms ~ Real Data, Practical Tooling

    These days, there is no shortage of propreitary managed machine learning platforms. How do you find one that is transformative and a good fit for your enterprise? What are the key factors to consider when re-engineering decision making for gaining a competitive advantage?

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  • Jan 16 2022 - MLE3 : Handling Model Drift ~ Cues for Retraining and Incremental Learning

    Monitoring machine learning models in production is a necessary but tedious task. When the data has changed and the model has drifted, it will impact the model performance.

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  • Nov 20 2021 - MLE2 : Operationalizing Models 2 ~ Build and Deploy Machine Learning Model As API Endpoints

    This blog explains my personal project undertaking in serving out my classifier model into a Rest API endpoint

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  • Oct 12 2021 - MLE1 : Operationalizing Models 1 ~ Build and Deploy Machine Learning Model As Web Application

    This blog explains my personal project undertaking in developing a ml workflow that includes preprocessing transformation and training a regression model to make predictions in real time and building the back-end as a web application using the Flask framework and deploying the app on Heroku.

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  • Data Strategy, Analytics & Architecture

  • Jun 15 2023 - ABI4 : Rebuilding Foundations ~ Is Data Governance Key For Visibility & Transparency?

    Recognizing value creation in data governance can be challenging. It calls for a mindset shift from thinking of policies and frameworks to one where business leadership strategically links them to digital transformation efforts. In this blog, i examine why businesses need a fresh think on data governance ideas.

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  • Dec 24 2021 - ABI3 : Balancing Act ~ Understanding Imbalanced Datasets

    Real world datasets have varying degrees of class imbalance and this inevitably results in a bias towards prediction of the majority class.

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  • Dec 01 2021 - ABI2 :Data Strategy - An Approach Towards Purpose-Led, Future-Fit Business Model

    Designing for a modern and resilient analytics stack is not only about building for an intelligent data architecture via cloud native technologies or ensuring that an advanced analytics platform has a comprehensive set of data connectors with interactive data visualization tools.

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  • Dec 01 2021 - ABI1 : High-Dimensional Data Analysis ~ Dimension Reduction Techniques

    The prevalence of large, complex datasets require more than statistical analysis to yield human answers. The representation of data in the high dimensional space thus raises the challenge - curse of dimensionality.

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  • Machine Learning & Data Science

  • Apr 28 2022 - MLT3 : Machine Learning Security ~ Zero Trust & Model Governance?

    Zero Knowledge Proof protocols are an age old cryptographic method of allowing a prover to produce a short proof "p" that can convince a verifier

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  • Mar 28 2022 - MLT2 : Explanability In AI ~ Frameworks for XAI

    There is always a tension between accuracy and interpretability in using deep learning models. Explanaibility frameworks impose and enforce the process of explanability while assessing model results with respect to biasedness and fairness.

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  • Dec 30 2021 - MLT5 : Probabilistic Modelling ~ Bayesian Approach & Inference

    Bayes theorem is an approach to data aanlysis and parameter estimation.

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  • Dec 07 2021 - MLT4 : Missing Link In Machine Learning ~ Representing Cause and Effect

    Increasingly, machine learning models are used to automate decision-making in a plethora of domains. In this blog, i will delve into how causal reasoning can shed new light into the challenges of machine learning.

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  • Deep Learning

  • Apr 14 2022 - DL4 : Image To Image Translation ~ Conditional Generative Adversarial Networks

    Image to image translation involves translating an image from one domain to a corresponding image in another domain while preserving the structure in the content such as objects.

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  • Dec 01 2021 - DL3 : Causal Relationships ~ Inferring Causality with Deep Learning


  • Nov 15 2021 - DL2 : Optimization in Deep Learning ~ Gradient Descent Strategies

    Gradient Descent is a generic method that can be used to optimize any differtiable loss function and find its minimum. This blog post aims at giving readers a practical and yet an intuitive guide on the different strategies for optimizing gradient descent.

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  • Nov 10 2021 - DL1 : Deep Learning Algorithms ~ Analyzing Neural Networks

    In this blog, i will attempt to explain why the AI community gravitates away from traditional machine learning methods and instead focus on neural networks, algorithms that form the basis for most of our pre-trianed models in deep learning.

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  • Affective Computing

  • Jan 02 2024 - AC3 : Is our Mind A Probabilistic Machine?

    We have very little to no insights into the inner workings of artifical intelligence tools and this severly impacts how we formalize expectations around generating trust in a black box model.

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  • Jul 15 2022 - AC2 : Understanding Artifical Intelligence And Human Emotions ~ Where Are We?

    Emotions are an inevitable aspect of intelligent agents to function effectively in the real world and both personality and mood play an important role in modulating emotions. AI today has earned itself a reputation of being cold and “robotic”, unable to understand human empathy, intentions and other mental states. Just look around the social robots, chatbots that interact with us massively in our daily lives.

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  • Jan 07 2022 - AC1 : Taking Human out of the Loop ~ Can I trust you, AI

    We have very little to no insights into the inner workings of artifical intelligence tools and this severly impacts how we formalize expectations around generating trust in a black box model.

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  • Digital Transformation & Innovation

  • Apr 13 2022 - SI3 : Platform Economics ~ Pivoting to the Subscription Economy

    There is a new trend in the business environment today, a total transformation of how business is conducted, highly driven by the internet, smart devices and artifical intelligence.

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  • Nov 02 2021 - SI4 : Promise of AI ~ Hire Machine Learning Engineers not Data Scientists

    The key goals of setting up any MLOps ecosystem revolve round reproducability, accountability, colloboration and continuous development.

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  • Sep 20 2021 - SI2 : Monetizing Data ~ How To Drive Value From Analytics

    Its important to set a mental framework for value assessment in mapping AI techniques to business problem types. I go beyond rigorous use case selection to examine what other strategies should organizations adopt against the backdrop of business application complexity.

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  • Aug 01 2021 - SI1 : Profitability in Artificial Intelligence ~ Cents & Sensibility

    This is an an article i submitted for publishing to Medium

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  • Blockchain Systems / Distributed Ledger Technology

  • Dec 30 2021 - BS1 : Securing Services In An Untrusted Environment.

    Traceable supply chain is about determining and proving provenance of a product and securely tracing it in the supply chain process.

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  • Blockchain Innovation and Decentralized Finance

  • Dec 17 2021 - BDF2 : Tokenization For Wellness Community ~ Transforming the Industry


  • Dec 01 2021 - BDF1 : Blockchain ~ Hype or Hero?

    How do we seperate the noise and hype of blockchain technology from its true commercial promise?

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