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Build a production AI in the cloud in 20 minutes!
- 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 http://aiclub.world
- 4. Realities of Production Use https://www.oreilly.com/library/view/the-new-artificial/9781492048978/ https://emerj.com/ai-sector-overviews/valuing-the-artificial-intelligence-market-graphs-and-predictions/ Despite the advanced services available, AI usage still minimal http://aiclub.world
- 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 http://aiclub.world
- 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
- 10. Dataset id duration codec width height bitrate framerate i p b frames i_size p_size b_size size o_codec o_bitra te o_fram erate o_widt h o_heig ht umem utime 0Yxo- eU6AjI 326.583 vp8 640 480 1055982 25.039877 102 8061 0 8163 1868804 41239444 0 43108248 flv 50000 00 24 480 360 215124 1.648 42hr- 6A1jYc 311.1 mpeg 4 176 144 56416 12 113 3620 0 3733 127552 2066350 0 2193902 vp8 50000 00 29.97 480 360 221160 2.012 3Wdg- dsGihA 340.424 flv 320 240 244491 11 69 4012 0 4081 1168939 9234909 0 10403848 h264 50000 00 29.97 640 480 221152 23.949 1YDt- FkeZ4E 98.6833 34 mpeg 4 176 144 55552 12 20 1164 0 1184 48267 636993 0 685260 mpeg4 56000 12 480 360 217852 1.104 22uE- OUUBH 8 43.827 flv 320 240 272273 25 23 1081 0 1104 137210 1354404 0 1491614 mpeg4 10900 0 25 480 360 218932 3.328 2NuI- Bp5brA 80.536 h264 480 360 690041 29 43 2374 0 2417 618732 6327913 0 6946645 mpeg4 82000 0 29.97 1920 1080 219480 18.381 4keb- __zqyQ 452.689 vp8 320 240 66680 5.7057524 42 2537 0 2579 207037 3566163 0 3773200 mpeg4 30000 00 29.97 1280 720 247900 12.609 2AfTeW TxhIg 645.84 vp8 320 240 95903 25.032558 138 16008 0 16146 1081321 6660941 0 7742262 vp8 30000 00 12 176 144 219200 1.564 2ApZ- KZ-pEk 157.891 h264 480 360 406908 29 87 4646 0 4733 1776729 6254165 0 8030894 h264 10900 0 29.97 1280 720 327668 21.173 3N6c- uPdNas 69.933 flv 320 240 272898 15 37 1013 0 1050 349781 2035796 0 2385577 vp8 82000 0 12 320 240 219656 3.048 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