The document provides an overview of a talk on production machine learning. The talk will: 1) Discuss the changing data landscape and new roles in machine learning. 2) Provide a brief introduction to AWS services like EC2. 3) Lead a workshop on containerizing a RESTful machine learning service using Docker. 4) Include demos of AWS machine learning services like SageMaker, Athena, and Quicksight, as well as containerized machine learning APIs.

1. Production ML
@osamakhn

2. who am I?
Osama Khan
Big Data Engineer @ACLServices
Grad Student @GTComputing
AWS Big Data Specialist+
! Vancouver, BC
" : Java " : C# (via J#) # : Python
$ : Golang, NodeJS % : Scala
Previously: Robot Soccer, Credit Rating, AML, O&G
Portfolio, NLP/Governance, Doctor Triage, Energy
Monitoring, Consulting, Private Equity
Recently: Data/ML Pipeline, Tools & Platforms

3. what are we going to
talk about?
The goal of this talk is to provide a high level overview
of the data landscape, introduce AWS and run
through an exercise of containerizing an ML service
1) Data Landscape (v2018): Changing ecosystem
and new roles
2) Just Enough AWS: AWS Intro, EC2, et. al
3) Workshop: RESTful ML Service
4) Demos: Athena, Sagemaker, Quicksight, ModelDB,
Heroku ML API, Docker ML API

4. classic model

5. classic model

6. classic model

7. data science silo
Data Source Data & Feature
Engineering
Adaptation of slide by Ben Lorica
Model
Building
Deploy
Monitor

8. maturity spectrum

9. what’s changing(-ed)?
1. Cloud (faas, serverless data pipelines, ml-as-a-service)
2. Consumer demand for ML features/products/applications
3. Targeted Models (we need to manage 20MM models for 10MM users maybe)
4. Localization (ASEAN facial recognition)
5. Security (Adv. ML, Side-channel attacks)
6. Transparency (Bias is a BUG)
7. Many toy sophisticated solutions but conventional, simpler techniques (regression)
still deliver more business value!
8. Monitoring to ensure deployed models are making high quality predictions
9. Need practices to maintain (update or rebuild) models over time
10. and ….

10. feature engineering, wat?
By @MLpuppy

11. data science (v2017+)

12. model: monitoring & maintenance
- What models are being deployed? [Model Inventory]
- Are we seeing deviations from expected performance? [Model Output monitoring]
- Reasons for performance degradation? [Data monitoring]
- Take action on out of ordinary situations

13. rise of machine learning engineers

14. intro to aws
https://acloud.guru

15. relevant technologies & references
https://acloud.guru
Storage Compute ETL Viz
CS349D: Cloud Computing Technology

16. DEMO: Sagemaker, Athena, Quicksight, RESTful ML

17. www.productionml.org