Parallaxis

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.

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.

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.

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.

Practical considerations for building analytics-ready data pipelines and data products using AWS Glue with Jupyter notebooks, Python, and Terraform.

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.

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.

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.

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.

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.