Here are the slides from my talk at the Phoenix Data Conference

1. ct@askatlas.ai1
Crystal Taggart, MBA
Founder Atlasai
AI Pipelines
From Machine to API

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ABOUT
• Founder and CEO at Atlas AI
• Launch Monday!
• Over 20 Years in Technology
• Past 2 Years focused on AI Technologies
• Author:
– The AI Revolution, The Future of
Profit
– Marketing AI: 90 Days to AI-Driven
Marketing (Next Month!)

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HOWMANYUSING AI?
No Experience Experimenting Expert

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IDE
STARTINGPOINT
Environment
Manager
Language

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DataAnalysis,
Cleansing,
Manipulation
MachineLearning Deep Learning
COMMONLIBRARIES

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DEVELOPMENTLIFECYCLE
Data
Prep
Engineer
Features

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FEATUREENGINEERINGEXAMPLES
• Scaling: Transforms a number to remove anomalies in the data
• Binning: Grouping similar data ranges together
• Encoding: Many models require categorical data to be transformed into numbers
• Cleansing: Removing Null values and replacing with zeros, averages, or medians
• Choosing which features are predictive

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DEVELOPMENTLIFECYCLE
Data
Prep
Save
Model
Engineer
Features
Hyper
params
Train
Model
Eval
DeployMonitor

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PROBLEMSWITHIMPLEMENTINGAI
Ad-hoc
Model
Development
Knowledge
Sharing
Brittle
Deployment
Processes
Performance
Decay

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DESIREDSOLUTION
• Model Hosted in the Cloud
• Persistence/Availability
• APIAvailable for Consumption
• Traceable
• Independent
API
Storage
Deployment
LogsPredictive
Result

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DEPLOYMENTPARADIGMS
Ad-hoc
Deployment
Flexible
Not
Repeatable
Dev-Ops
Repeatable
Not
Independent
Joint
Management
Semi-
Flexible
Semi-
Independent
Deployment
Platforms
Semi-
Independent
Some
Not Flexible

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WWFD

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FACEBOOKCASESTUDY
• Training can takes weeks on TB of data
• Hundreds of ML engineers
• Job Crashes cause weeks of delays
• Optimizing Compute Cost
Unique Challenges

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FACEBOOKCASESTUDY
Data Features Training Evaluation Deployment
FB Learner
Feature
Store
FB Learner
Flow
FB Learner
Predictor

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PRIORITIES
Reliability
Scalability
Efficiency

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RELIABILITY:CHECKPOINTS

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SCALABILITY:DISTRIBUTEDTRAINING

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EFFICIENCY:OFF-PEAKTRAINING

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FBRESULTS

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OTHEROPTIONS

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GOOGLEAIHUB
Plug-And-Play AI Components

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AZUREMLSTUDIO
Drag-and-Drop AI

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H20AIAUTOML

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APACHESPARK
Big Data ML

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DATAROBOT
Auto ML

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SAGEMAKER(AWS)

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SAGEMAKEROVERVIEW

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CREATINGENDPOINTS

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SAGEMAKERSUMMARY
• Notebooks have support for all the major ML & DL frameworks
• Versions of data/models are stored in S3
• You’ll need to create your own checkpointing
• Can be hard to debug
• Don’t forget to turn off instances!

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CONFIGURATIONMANAGEMENT?
Data
Prep
Save
Model
Engineer
Features
Hyper
params
Train
Model
Eval
DeployMonitor

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TAKEAWAYQUESTIONS
• How does your ML pipeline handle checkpointing?
– Google Big Query you have to check a checkbox for long-running transactions
• How do you engineer features for your organization
– On your local machine?
– Does your API engineer the features from source data?
– Creating a database of shared features and ‘continuous feature engineering’

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TAKEAWAYQUESTIONS(CONT.)
• How do you store history on the four components of a model version?
– Data, Code, Features, Hyper Parameters, and Training Dataset
• How do you monitor model decay?
– Does your solution monitor for false positive/negative results?

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RESOURCES
Machine Learning A-Z
Great resource for learning ML
https://askatlas.ai/
partner
https://askatlas.ai/meetup https://askatlas.ai/ct
Rasa.Com
Open Source Chatbot

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THANKYOU!
CT@ASKATLAS.AI