Welcome to the Parallaxis Blog
Explore our latest thoughts on all things related to AI, Machine Learning, and the importance of data .
Find by Tag
- AI 4
- AI4Dev 2
- BusinessValue 3
- CitizenCoder 1
- CitizenDeveloper 3
- Cloud 1
- Community 3
- Compliance 1
- DataEthics 2
- DataFitness 3
- DataGovernance 11
- DataManagement 7
- DataMesh 5
- DataMess 4
- DataScience 9
- DataSwamp 5
- DataWarehouse 3
- DevOps 1
- GDPR 1
- InfrastructureAsCode 4
- MLOps 2
- MachineLearning 6
- Metrics 2
- ModelDevelopment 2
- ModelEvaluation 2
- ModelTraining 2
- PII 1
- PlatformEngineering 1
Shadow AI
Employee use of unauthorized AI tools is inevitable. Rather than blocking it, organizations should investigate why it's happening and build governed internal AI platforms that meet those needs. Success requires permission-aware data access, automated security controls, and treating AI governance as product development, not just compliance. Transform the risk into a competitive advantage.
AI - The Rote Machine
AI's perceived intelligence is often overstated. It is only as “intelligent” as the data it's trained on. Poor quality and unfit data lead to “incorrect”, unsupported, and potentially reputational affecting decisions. Actual progress in AI requires a relentless focus on data fitness, quality, governance, and fairness.
5 Key Indicators That AI Isn't The Answer
This post outlines five key considerations for deciding whether to pursue AI: the existence of clear rules, data volume and variability, significant regulatory requirements, static environments, and unclear ROI. AI should be a multiplier for disciplined thinking and well-designed processes, not a replacement. Focusing on data quality and automation maturity should precede any AI adoption.
Ethics Is Not “One and Done”
Implementing ethics in Machine Learning is not a one-and-done effort. Organizations must build trustworthy systems that serve their customer’s best interests fairly. Ultimately, ethically designed machine learning models align with compliance and regulations while also avoiding harmful outcomes.