Auto AI : AI used to create AI applications
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Building AI applications is a very complex process involving steps and workflows which are becoming more complex every other day. Its a circle since the AI application is nothing but a feedback loop between various steps involving data. Consider the below picture a data scientist or ML engineer has to work through. Now my mission as an evangelist of the AI technology who sees a lot of promise in this technology would like to make it simple so we can empower more professionals in the business to become what we call "citizen data scientists". A citizen data scientist is a business person empowered so well that he can combine his domain knowledge with tools an expert data scientist uses in a simplified way. We have seen this impacting customer experience in 5x and revenue increase in the range of 15-20%.
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Auto AI : AI used to create AI applications
- 1. AI for AI Karan Sachdeva Director Cloud Pak for Data, Asia Pacific IBM Data & AI karan@sg.ibm.com
- 2. Use Cases “86% of CEOs consider advanced analytics(Artificial Intelligence & Data Science) to be the priority № 1 for their companies” Source PwC General Directors Survey Companies
- 3. Typical model building & Deployment path Data and AI Forum / © 2019 IBM Corporation 3 …Task: Go build me a model!... …How do we get started?… …Two months go by… …what is she doing?… …It’s built! Now to deployment!… …Finally… …Does this thing even work?... …Oops… …This is a lot of work!… …This is way more than I signed up for… Data and AI Forum / © 2019 IBM Corporation
- 4. Typical model building & Deployment path Data and AI Forum / © 2019 IBM Corporation 4 …Task: Go build me a model!... …How do we get started?… …Two months go by… …what is she doing?… …It’s built! Now to deployment!… …Finally… …Does this thing even work?... …Oops… …This is a lot of work!… …This is way more than I signed up for… Data and AI Forum / © 2019 IBM Corporation
- 5. Typical model building & Deployment path Data and AI Forum / © 2019 IBM Corporation 5 …Task: Go build me a model!... …How do we get started?… …Two months go by… …what is she doing?… …It’s built! Now to deployment!… …Finally… …Does this thing even work?... …Oops… …This is a lot of work!… …This is way more than I signed up for… Data and AI Forum / © 2019 IBM Corporation
- 6. Typical model building & Deployment path Data and AI Forum / © 2019 IBM Corporation 6 …Task: Go build me a model!... …How do we get started?… …Two months go by… …what is she doing?… …It’s built! Now to deployment!… …Finally… …Does this thing even work?... …Oops… …This is a lot of work!… …This is way more than I signed up for… Data and AI Forum / © 2019 IBM Corporation
- 7. Typical model building & Deployment path Data and AI Forum / © 2019 IBM Corporation 7 …Task: Go build me a model!... …How do we get started?… …Two months go by… …what is she doing?… …It’s built! Now to deployment!… …Finally… …Does this thing even work?... …Oops… …This is a lot of work!… …This is way more than I signed up for… Data and AI Forum / © 2019 IBM Corporation
- 8. AutoAI: What we are hearing from our clients Healthcare insurer AutoAI is my first step now with any new use case, I can test it out and see immediately if there is signal in the data Airline Leader Demonstrated with live data that AutoAI outperforms the existing loyalty campaigns designed by their in-house marketing teams. International Banking Leader The AutoAI function has played an important role in the feature engineering process. We are super excited and want to get deeper dive on the potential of AutoAI
- 9. Why care about Auto AI? “Through 2020, the number of citizen data scientists will grow five times faster than the number of expert data scientists.” Source: Gartner: Top 10 strategic technology trends for 2019, October 2018 Empower Citizen Data Scientists Speed AI lifecycle management “The moment you put a model in production, it starts degrading.” Source: Forbes, Why Machine Learning Models Crash And Burn In Production, April. 2019 “Typical project consumes 1-6 expert data scientists for 2-12 months.” Source: IBM Research estimate, March 2019 Enhance Performance & Reproducibility
- 10. Domain Knowledge Auto AI Automated Machine Learning and Data Engineering Data Science Math and Statistics Computer Science and Coding Machine Learning Data Science Application Software Industry Applications Domain Knowledge The Case for Auto AI
- 11. AI for AI: Automation of AI Development with Cloud Pak for Data AI Designing AI AI Optimizing AI AI Governing AI Create AI and Neural network Models AI Model optimization Monitoring AI outcome for explainability
- 12. 12 AI Designing AI: AI Workflow’s Bigger & More Complex Majority spent on data wrangling! Use Case Business Req Data Cleansing Feature Engineering Model Selection Parameter Optimization Ensemble Model Validation Model Deployment Runtime Monitoring Model Improvement Source: https://www.kaggle.com/paultimothymooney/2018-kaggle-machine-learning-data-science-survey AI Lifecycle Management
- 13. AI Optimizing AI Raw Labeled Data Set Prep Model selection HPO Feature Engineering HPO Finds best preprocessing imputation / encoding and scaling strategies Finds top-K estimators HPO on selected estimator HPO on estimator after Feature Engineering Finds best data transformation sequence
- 14. “AI Governing AI”: Trust, and Transparency with Watson OpenScale via Cloud Pak for Data Understand where data originated (data provenance) Understand how models are making decisions based on that data Highlight when models go wrong (anomaly detection, bias, etc.)
- 15. Data Science Model Build, Run, Deploy and Manage Automate AI lifecycle Trust and Transparency in AI Models Cloud Pak for Data Advanced Analytics = Big Data + AI Real Time Data Data Discovery Data Governance Data Scientist & ML Engineers Data Management team Enterprise Architect Business User Data Science Use Cases Dev-ops & Hybrid Cloud
- 16. Benefits Accelerate data science projects by “as much as” 80% with AutoAI Improve your insight by bringing AI/ML models to Hadoop and big data eco system Empower citizen data scientists and analysts to gain as high as 400%+ returns on investment Predict and optimize business outcomes using natural language interface AutoAI via IBM Cloud Pak for Data Open by Design Power of IBM Research Operationalize data science AutoAI Hadoop Execution Engine SPSS Modeler Decision OptimizationAccelerate data prep and model development with automated AI Push AI to Cloudera or Hortonworks Build schedules and plans based on prediction Prepare and visualize data. Build Models Multiple capabilities | Consumption based | Trade-up from SPSS Available ++ + AutoAI: Leaderboard Base
- 17. User Benefits of using AutoAI Build models faster Automate data preparation and model development Jump the skills gap No coding? No problem – get started with a couple clicks Discover more use cases Supercharge collaboration with AI everywhere to disrupt and transform Find signal from noise Auto-feature engineering makes it easy to extract more predictive power from your data Rank and explore models Quickly compare candidate pipelines to find the best model for the job Ready, set, deploy Pipelines generated with AutoAI can be deployed to REST APIs with one click
- 18. 18 Cloud Pak for Data Banking AI and Data Science Use cases
- 19. Thank you! — Karan Sachdeva Director Cloud Pak for Data, Asia Pacific IBM Data & AI karan@sg.ibm.com