How to easily build production AI in the cloud. This presentation covers the common tools available from different cloud vendors and an easy way to link them together to build a full end to end AI in 20 minutes or less. We also cover REST microservice deployment.
1. Build and Integrate AI into
Applications Using the Cloud
Nisha Talagala
Sindhu Ghanta
Pyxeda
http://aiclub.world
2. Agenda
• The full AI workflow including applications
• Tools available for each stage from major cloud vendors
• How to do a full workflow with AWS in just 20 minutes
http://aiclub.world
3. Sophisticated AI
technologies available in
the cloud
Each logo is a (separate) service offered by GCP, AWS or Azure for part of an AI workflow
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5. The Full AI Workflow
• Identify problem
• Prepare data
• Develop models
• Train models
• Test models
• Deploy models
• Connect to app
• Monitor and optimize
• Repeat!
Data
Train
Model(s)
Develop
Model(s)
Test
Model(s)
Deploy
Model(s)
Connect
to
Business
app
Business
Need
Monitor
and
Optimize
http://aiclub.world
6. The Full AI Workflow
• Why the full workflow?
• Improve applications
• Understand whether the AI
truly benefits the application
(requires deployment and
iteration, not just training)
• Show ROI for AI models
• Why it is challenging
• Requires production
deployment
• Requires easy integration
Data
Train
Model(s)
Develop
Model(s)
Test
Model(s)
Deploy
Model(s)
Connect
to
Business
app
Business
Need
Monitor
and
Optimize
http://aiclub.world
7. Building and using an AI workflow in the cloud
Link various tools together to form a workflow
Labeling
Data Prep and Visualization
Modeling and Training
Manipulate raw data
Build, tune and train models
(built in and your own)
Infrastructure: Compute, Authentication, Data source, Logs etc.
Where your AI runs and what
monitors it
Deployment Deploy models as REST API
Application
Request Prediction
http://aiclub.world
8. Example: AWS, Azure and GCP Services
Category AWS GCP Azure
Data Preparation Sagemaker Ground Truth, Glue DataPrep Azure Data Bricks, Data Factory
Data Analysis /Visualization QuickSight Data Studio, Data Lab, Partners(Tableau) Azure Data Explorer, Azure Analysis service, Power BI embedded
Data Processing Glue Data flow Data Lake Analytics, Streams Analytics, Azure Data Bricks, HD Insight
Spark & Hadoop EMR DataProc Azure Data Bricks, HDInsight
Orchestrator Data Pipeline Composer Batch, Service Fabric
Performance Monitoring Cloud Watch, Cloud Trail Firebase Azure Monitor
Marketplace Marketplace AI Hub Azure Market Place
Machine Learning (TF, Scikit Learn, Keras, SG Boost) Sagemaker cloud machine learning
machine learning service, azure machine learning, machine learning studio,
Azure Batch AI
Serverless Endpoint API Gateway Cloud endpoints API Apps, Cloud Services
AutoML Recommendation, h20.ai automl, Cloud AutoML (), BigQueryML Azure ML, Machine Learning Studio
Conversation / Dialog Lex Dialogue Flow Speaker recognition, linguistic analysis
Text Textract Natural Language Text Analysis,
Speech-to-Text Transcribe Speech-to-Text Speech-to-Text
Text-to-speech Polly text-to-speech text-to-speech
Translation Translate translation speech translation, text translation
Vision Rekognition vision computer vision, custom vision, face
Video Rekognition video intellegence Video indexer
Anomaly Detection Quickshight (anomaly detection) Cloud Inferennce -
User Application Insight Pinpoint Firebase (churn, customize experience, campaign) -
IAM AWS IAM Cloud IAM, Cloud Identity Azure AD, Azure information protection, Azure Policy
Monitoring CloudWatch monitoring (GCP, AWS) Azure Monitor
Cost Management Billing cost management cost management
APIs (to access services) Yes Yes Yes
Async Task Execution Step Function Cloud Tasks (Beta), Cloud Scheduler, Cloud Composer (Airflow) Scheduler
SDK SDKs Cloud SDK SDKs
DataScience Virtual Machines Deep Learning AMIs, Apache MXNet, TensorFlow DSVM
Kubernetes Support Yes Yes Yes
Container Registry Yes Yes Yes
Serverless Lambda, Cloud Functions, App Engine Azure Functions
Genomics somewhat support Microsoft Genomics
Bot Sevice Support Yes Azure Bot Service, QnA, Language understanding
Cognitive Service - Cognitive Service
Content Moderation - Content Moderator
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9. Demo – Video Transcoding using Regression
Open source dataset from UCI:
http://archive.ics.uci.edu/ml/datasets/Online+Video+Characteristics+and+Transcoding+Time+Dataset?ref=datanews.io
Labeling
Data Prep and Visualization
Modeling and Training
AWS Lambda in Pyxeda
AWS Sagemaker
Infrastructure: Compute, Authentication, Data source, Logs etc.
AWS EC2 and S3
Deployment
AWS Sagemaker, Lambda, API
Gateway
Application
Request Prediction
Example: Python
Not Shown
http://aiclub.world
11. Demo – AWS tools, Linked with Pyxeda
Navigator automated linkage
Model develop and deploy
Application Integration
http://aiclub.world
12. Datasets and Sample Code:
• Datasets used in the demos:
• https://aiclub.world/projects
• Download from Project Video Transcode
• Dataset – original version
• https://archive.ics.uci.edu/ml/datasets/Online+Video+Characteristics+and+Transcoding+Time+D
ataset
http://aiclub.world
13. Shameless plug slide
If you are interested in a free account, please
sign up at http://aiclub.world
Have data, but need help demonstrating that
your AI value prop is viable? For help going
from data to AI prototype, contact our ML
Guru service at info@pyxeda.ai
http://aiclub.world
