Cloud & AI SummitExplore solutions to support transformation

The AI Lifecyle and ModelOpsRobin van TilburgAnalytics Architect

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AI has emerged as a strategic business opportunity

But what if AI goes wrong?

DATA TALENT TRUSTThe lifeblood of AI, but

complexity slows progress

AI skills are rareand in high demand

Skepticism of AI systems & processes

Find it difficult to fundor acquire AI skills

of time spent preparing data versus building AI models

say its very challenging tobuild trust in AI outcomes

Stuck in Experimentation A majority of enterprises face significant challenges in operationalizing AI value

How to operationalize AI?

Based on 2019 Forrester “Challenges That Hold Firms Back From Achieving AI Aspirations”

DevOps: The guiding principles…

Automating the repeatable

Challenge 3: ModelOps requires collaboration…

There are differences between applications and models…

Challenge 1: AI teams will use multiple tools to build AI solutions.

Challenge 2: ML models decay

…between with data science, development, IT, ops, and business teams.

ModelOps is the bridge from model development to model deployment…

ModelOps…

App Deployment

Operational Data

New Requirements & Engagement

DevelopmentTest

Monitoring Retraining

Search for Data

Acquiring Data/ Self Service

Model building

Hadoop

EDW NO SQL

Virtual Data Lake

ModelDeployment

Refining Data

Continuous Delivery of Applications

Continuous Delivery of Models

Infuse AI

Generate Analytical

Data

Collect Data

Organize Data

AnalyzeData

R

Start

DevOps and ModelOps together

AI Lifecycle: Creating and maintaining trusted models

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Data Exploration

Data Preparation

Model Development

Build

Build

Run

Run

Deployment

RetrainingModel Management

Rebuild models, improve performance and mitigate bias

Monitor and orchestrate models served with WML

Easily deploy models to WML for

online, batch, streaming

deployments

Business KPIs and production

metrics

Fairness & Explainability

Inputs for Continuous Evolution

Infuse

Infuse

Rebuild models, improve performance and mitigate bias

Easily deploy models to WML for

online, batch, streaming

deployments

Monitor and orchestrate models served with WML

AI Lifecycle

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Data Exploration

Data Preparation

Model Development

Build

Watson Studio

Watson Machine Learning

Run

Deployment

RetrainingModel Management

Rebuild models, improve performance and mitigate bias

Monitor and orchestrate models served with WML

Easily deploy models to WML for

online, batch, streaming

deployments

Business KPIs and production

metrics

Fairness & Explainability

Inputs for Continuous Evolution

Infuse

AI Open Scale

Rebuild models, improve performance and mitigate bias

Easily deploy models to WML for

online, batch, streaming

deployments

Monitor and orchestrate models served with WML

Analyze any data, no matter where it livesConnect to and analyze your data without moving a single byte through dozens of connectors and multiple deployment options

Empower your entire organization with notebooks, visual productivity, and automation toolsLeverage your entire organization with a variety of tools in a single integrated platform

One platform to rule them all from discovery to production Analyze data, build predictive models, and seamlessly integrate Watson Machine Learning to deploy at scale

IBM Watson StudioEnterprise Data Science platform that helps your team work together to build models to make better data driven decisions for your business

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AI Lifecycle

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Data Exploration

Data Preparation

Model Development

Build

Watson Studio

Watson Machine Learning

Run

Deployment

RetrainingModel Management

Rebuild models, improve performance and mitigate bias

Monitor and orchestrate models served with WML

Easily deploy models to WML for

online, batch, streaming

deployments

Business KPIs and production

metrics

Fairness & Explainability

Inputs for Continuous Evolution

Infuse

AI Open Scale

Rebuild models, improve performance and mitigate bias

Easily deploy models to WML for

online, batch, streaming

deployments

Monitor and orchestrate models served with WML

IBM Watson Machine LearningEmbed Machine Learning and Deep Learning in your Business

Deploy and Manage ModelsMove models to production, in an easy, secure, and compliant way

Intelligent Model OperationsEmbed intelligent training services, with feedback loops that constantly learn from new data, regardless where it resides

Accelerate Compute Intensive WorkloadsDistribute your deep learning training and Hadoop/Spark workloads with multi-tenant job scheduling

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AI Lifecycle

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Data Exploration

Data Preparation

Model Development

Build

Watson Studio

Watson Machine Learning

Run

Deployment

RetrainingModel Management

Rebuild models, improve performance and mitigate bias

Monitor and orchestrate models served with WML

Easily deploy models to WML for

online, batch, streaming

deployments

Business KPIs and production

metrics

Fairness & Explainability

Inputs for Continuous Evolution

Infuse

AI Open Scale

Rebuild models, improve performance and mitigate bias

Easily deploy models to WML for

online, batch, streaming

deployments

Monitor and orchestrate models served with WML

IBM Watson OpenScaleAutomate & Operate AI at Scale

Production monitoring for complianceDetect and mitigate model bias; audit and explain model decisions

Ensure models resiliency in changing situationsDetect drift in data and anomaly in model behavior; specify inputs and triggers to model lifecycle

Align model performance with business outcomesCorrelate model metrics and business KPIs to measure business impact; actionable metrics and alerts

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Organizations that implement AI get results

35% to 50% 1.5x to 2x 3x and 8x 15% to 30%

Reduced model monitoring effort

Increased models in production

Increased accuracy of models:

1.5x and 2x more AI and machine learning (ML)

models

Forrester New Technology:Total Economic Impact™ Of Explainable AI And Model Monitoring In IBM Cloud Pak For DataStudy Commissioned By IBM August 2020

More more more…. Today!

14:45 - 15:05 A brief History of A.I, from statistical models to Automated Machine Learning-Pt.1

14:20 - 14:40 Discover Cloud Pak for Data

13:25 - 13:45 Watson OpenScale: Manage production AI with trust and confidence in outcomes

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