Making My Way as a Principle Data Architect at Evolve

On my way to Carter Notch in the White Mountains of New Hampshire

I am thrilled to say that I will be starting at Evolve next week as a Principle Data Architect.

I've had a great time working on my own project in on automating Machine Learning data pipelines and taking Machine Learning (MLOps) for Production specialization on Coursera.

There are many tools out there, and two that have captivated my attention are OpenAI and Sagemaker Autopilot. OpenAI is well-known, and I have adopted it as my personal assistant. Sagemaker Autopilot is part of the AWS ML suite of tools and is equally helpful for quickly building ML models from scratch.

As I dig into machine learning, I realize I have the perfect background as an architect for advanced machine learning applications. For many years, I was a numerical analyst and applied mathematician, and I have an entire section of my bookshelf devoted to optimization, linear algebra, and matrix analysis. Since then, I've spent a career as a developer, building and designing database applications. Recently, I've automated the S3 to Snowflake via dbt ingestion process in a simple set of scripts that could save many a struggling development pipeline.

References

1. T. Elskin et al. (2019) Neural Architecture Search: A Survey

2. A. Agnihotri, N. Batra (2020) Exploring Bayesian Optimization

3. B. Zoph, Q. Li (2017) Neural Architecture Search with Reinforcement Learning

4. C. Liu et al. (2018) Progressive Neural Architecture Search

5. T. Wei et al. (2016) Network Morphism

6. AWS (2023) Sagemaker Autopilot