Supercharging Machine Learning with Cloud Computing
Case study of how using secure cloud tools in a DoD environment enabled 10X data processing & prediction speeds. Starting with 1 Billion+ data points stored on the cloud, an automated script trains ML models & makes predictions, on a predetermined schedule (unattended). Overview of leading cloud tools and approaches to maximize efficiency in your domain.
Speaker:
Conner Lawston holds a BS in Engineering & MS in Data Science. He currently works on the DoD’s ADVANA project, creating ML models using Python, SQL, & AWS. He previously spent 3 years Cost Estimating DoD Space Systems (LA AFB) and has taught Power BI classes at various USAF bases across the country.
Supercharging Machine Learning with Cloud Computing
Case study of how using secure cloud tools in a DoD environment enabled 10X data processing & prediction speeds. Starting with 1 Billion+ data points stored on the cloud, an automated script trains ML models & makes predictions, on a predetermined schedule (unattended). Overview of leading cloud tools and approaches to maximize efficiency in your domain.
Speaker:
Conner Lawston holds a BS in Engineering & MS in Data Science. He currently works on the DoD’s ADVANA project, creating ML models using Python, SQL, & AWS. He previously spent 3 years Cost Estimating DoD Space Systems (LA AFB) and has taught Power BI classes at various USAF bases across the country.
Past Meetings:
Machine Learning in Space
The ICEAA Southern California Chapter and the ICEAA Machine Learning Working Group have teamed up to offer three presentations on Machine Learning in Space:
• Acquisition Complexity (ACS) Using Principal Component Analysis (PCA)
Mike Finnegan, The Boeing Company
• Proposing a Space-Based Missile Defense Program: Examining Engineering Complexities and Cost Feasibility
Sirina Nabhan and Matthew Ramirez, NASA Jet Propulsion Laboratory
• 2021 Plans for the Machine Learning Working Group
ML Group Steering Committee: Bryan Anderson, Dan Harper, Adam James, Karen Mourikas, Christian Smart
Attending this webinar counts as 1 hour of training or 0.1 points towards your CCEA recertification.
July 21, 2020 Meeting
Data Scraping Challenge: Collecting Cost Data for Machine Learning / Advanced Analytics. We will share information on data sources and automated techniques to collect data and introduce a problem for the community to solve.
The intent of the ICEAA Machine Learning Working group is to:
- share with one another different machine learning methods and applications
- help our community learn and grow from one another and
- advance the field of machine learning for cost analysis.
April 22, 2020 Meeting
Our April meeting provided an overview of “Common Machine Learning Tools and Methods for Cost Analysis”. We will hear about scripting languages, like R and Python, and commercial tools, like RapidMiner and JMP. Speakers include Adam James from Technomics, Bryan Anderson from Cobec, and Tom Donnelly from JMP.
Machine Learning Tools Pt. 1 presented by Adam James (PDF)
RapidMiner vs. Python by Bryan Anderson (PDF)
A Brief Introduction to Machine Learning with JMP® presented by Tom Donnelly (PDF)
January 2020 Machine Learning Group Meeting Slides
January 2020 Intro to Machine Learning Slides
October 23, 2019 Machine Learning Group Meeting Slides
Are you interested in joining or want to help shape the future of the Machine Learning Working Group? Complete our survey to add your name to our contact list and provide your feedback.