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.
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.
Model Release & Assessment Phase
This 3rd phase of the 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.
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.
A Coalition of the Motivated
Communities of Collaboration are an effective way to solve problems that impact business value without adding unnecessary complexity and stagnation gates to your business processes.
Question Formation and Data Analysis in Data Science
This blog post focuses on the first phase of the 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 a Machine Learning (ML) model to deliver true 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.
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.
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.