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.
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.
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.