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.
Practical Business Reasons to Resist the Allure of AI
There are many traps along the journey required to leverage AI/ML to generate value for your business. Success relies on aligning AI/ML initiatives with clear business objectives and understanding their true potential.
Anti-Patterns in Data Mesh
This article explores common anti-patterns in implementing Data Mesh, a decentralized data architecture emphasizing domain-oriented data ownership. While Data Mesh aims to enhance data accessibility and usability across organizations, its success relies on understanding core principles: domain-driven data ownership, data products, and federated governance.
Data Mess to Data Mesh
The standard strategy of centralizing data into a single repository often leads to chaotic "data swamps.” Due to poor data quality and governance issues, these swaps hinder efficient analysis and decision-making. An alternative approach, known as Data Mesh, proposes a decentralized architecture focused on treating data as a product.
Transformative Data Pipelines for Analytics Using AWS Glue
Practical considerations for building analytics-ready data pipelines and data products using AWS Glue with Jupyter notebooks, Python, and Terraform.
MLOps Automation
MLOps requires specialized knowledge that traditional DevOps teams lack. The challenges related to data quality, consistency, and accessibility demand a different set of skills and tools.
Model Release & Assessment Phase
This 3rd phase of our Data Science Process explores the release of ML models into production and the importance of ongoing monitoring and assessment.
Additionally, it provides a framework for defining "done" and achieving a high-quality model release.
Model Development
This blog post outlines the second phase of our Data Science Process: Model Development. Which involves building, training, and evaluating models based on data gathered during Question Formation. The process is iterative, experimenting with different algorithms, features, and parameters in a sandbox environment before scaling to larger datasets. Model performance is evaluated using metrics, validation for overfitting/underfitting, and checks for robustness and interpretability. Finally, models must be versioned, monitored for data drift, and continuously updated to ensure they remain effective and relevant over time.
Question Formation and Data Analysis in Data Science
This blog post focuses on the first phase of our Data Science Process: Question Formation and Data Analysis. In this phase, we iterate multiple times through question formation, data collection, and exploration. Initial questions are likely to be of low fidelity. Through the process of data exploration, the questions gain fidelity and drive toward business value.
Introducing a Data Science Process for AI/ML
This is an introduction to a series of blog posts describing the process of creating and operating data models in support of your AI/Machine Learning (ML) programs. It is structured to ensure that you can deliver actual business value.
Your Starter Guide to Data Governance
Data governance establishes standards for data collection, storage, and analysis, ensuring accuracy and mitigating risks associated with regulatory non-compliance. Moreover, governance promotes ethical data practices, safeguarding individual privacy rights and societal norms.