JourneyToAI

Operationalizing AI
Join the interactive chat with AI experts from IBM, Ingram, Accuras as they share best practices how to overcome the challenges of AI adoption and how to operationalize AI to scale in your organization
muratguvenc
We are coming to the end of the live part of the Crowdchat! To our hosts and everyone here: What are your recommendations on where to start to unlock the value of AI?
https://www.crowdchat.net/s/064bm

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https://www.crowdchat.net/s/064bm

Michael Gunzelmann
@mgunzelmann1 To learn more try https://www.coursera.org/professional-certificates...
https://www.coursera.org/professional-certificates/applied-artifical-intelligence-ibm-watson-ai?utm_source=IBM&utm_medium=institutions&utm_campaign=cognitiveclassblog
IBM Applied AI
IBM Applied AI
Offered by IBM. Artificial intelligence (AI) is transforming our world. Whether you’re a student, a developer, or a technology consultant - understanding AI and knowing how to create AI-powered applications can give you an edge in your career. This P...
Souvik Banerjee
We have experienced greater success in AI journey when we target low hanging fruits like Demand Forecasting
Shikhar Kwatra
I'd say in order to progress along the AI ladder and productionize your potential AI solutions, think about where you can bring in automation, especially with data insights. Data-Ops (Data Governance) and Model Ops infused together creates end-to-end lifecycle possible
Sourav Mazumder
Early deployment of Model and Continuous Monitoring & Refinement of the same is key to unlock the value of AI. It is better not to spend months of effort to build the model and then find that it is not aligned to the reality.
Shikhar Kwatra
Start looking at existing tools and technologies in AI space, inclusive of understanding your data pipeline as well. AI is as good as your data. https://www.ibm.com/blogs/journey-to-ai/2020/02/se...
https://www.ibm.com/blogs/journey-to-ai/2020/02/setting-an-ai-strategy-to-unlock-the-value-of-your-data
Setting an AI strategy to unlock the value of your data - Journey to AI Blog
Setting an AI strategy to unlock the value of your data - Journey to AI Blog
It’s been said that data is the most valuable resource on the planet. But most companies aren’t getting the maximum value out of their data. If you look at the top three things that are really needed in the marketplace, it’s really been around defini...
Michael Gunzelmann
@mgunzelmann1 Build your own sandbox to explore AI to see what is possible...https://www.ibm.com/cloud/free
https://www.ibm.com/cloud/free
Cloud Free Tier
Cloud Free Tier
Get started for free with your Lite account. No credit card required. No time limits.
Shikhar Kwatra
Understand the end to end lifecycle and play with Cloud Pak Experiences. Explore this flexible multicloud data platform with free access to a hosted environment. https://www.ibm.com/cloud/paks/experiences/cloud-p...
https://www.ibm.com/cloud/paks/experiences/cloud-pak-for-data
IBM Cloud Pak Experiences
IBM Cloud Pak Experiences
Discover the power of the IBM Cloud Paks through guided flows in a sandbox environment. Try it for free for 7 days and see how IBM can help accelerate your journey to the cloud.
Souvik Banerjee
Along with the data quality, ability to explore/ self service the models on a data lake or virtualized data layer for the citizen data scientists in the platform leads to early adoption right from the business users
Shikhar Kwatra
There is no one right answer but think about forecasting, optimization, prediction problems etc. and see if you can inject any intelligence and automation to it.
muratguvenc
Is it possible to bring trust and transparency to machine learning models and avoid bias?
https://www.crowdchat.net/s/264ai
https://www.crowdchat.net/s/264ai

muratguvenc
According to #IBM Institute of Business Value Study 60% of the companies see regulatory constraints as a barrier to implementing #AI..
Michael Gunzelmann
@mgunzelmann1 Trust but verify, a new idea for AI
Souvik Banerjee
Explainability leads to trust on AI models
Michael Gunzelmann
@mgunzelmann1 It has to be a transparent process and your platform must include this
Shikhar Kwatra
Enhancing Fairness and trust in AI models has been crucial for Operationalizing AI models and putting them into production. For imperatives involving AI adoption would typically include Drift, Fairness, Explainability and Resiliency.
muratguvenc
Four trust imperatives hindering AI adoption
Drift: How do we ensure data quality throughout AI lifecycle?
Fairness: How do we reject bias?
Explainability: How can we explain decisioning of how AI came to its conclusion?
Resiliency: How can we shield AI against cyber threats.
Souvik Banerjee
Metadata and Lineage often are two factors in explainability of models
Michael Gunzelmann
@mgunzelmann1 If users don't trust AI, it will never be used. Amke use you can explain your decisions.
Sourav Mazumder
Model's transparency can be achieved using IBM OpenScale (https://www.ibm.com/cloud/watson-openscale)
and AIX360 (https://aix360.mybluemix.net/) across the 4 key dimensions of Model's interpretability - Local Explanation, Global Explanation, Counter Factual and Similarity

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https://www.ibm.com/cloud/watson-openscale
Watson OpenScale - IBM Watson OpenScale
Watson OpenScale - IBM Watson OpenScale
IBM Watson OpenScale is the open platform that helps businesses manage production AI wherever their data lives, with trust and confidence in outcomes.
Shikhar Kwatra
When we talk about Drift, we need to ensure data quality throughout AI lifecycle, which involves inspecting and continuously drift in data consistency as well.
Sourav Mazumder
Similarly Fairness/Bias of Models can be easily figured out using AIF360 (https://aif360.mybluemix.net/) and IBM OpenScale (https://www.ibm.com/cloud/watson-openscale)
https://aif360.mybluemix.net/
AI Fairness 360
AI Fairness 360
This extensible open source toolkit can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. We invite you to use and improve it.
Shikhar Kwatra
In terms of Explainability, we have local and global explainability. There are algorithms like LIME, SHAP etc. and providing Contrastive explanations to aid in providing reasonable insights behind ML Models. https://www.ibm.com/blogs/research/2018/05/contras...

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https://www.ibm.com/blogs/research/2018/05/contrastive-explanations/
Contrastive Explanations Help AI Explain Itself by ID'ing what's missing
Contrastive Explanations Help AI Explain Itself by ID'ing what's missing
An algorithm that can provide contrastive explanations for models such as deep neural networks to improve the explanations generated by AI technologies.
muratguvenc
Some think AI is a magical black box that will do incredible things. Yet, the vast majority of AI projects failed to deliver on their intended promises. What are the challenges that hold companies back from operationalizing AI?
https://www.crowdchat.net/s/464b9

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https://www.crowdchat.net/s/464b9

muratguvenc
A study done by BCG shows that only around 20% of #AI models get into production.
Souvik Banerjee
The cost of quality is the first for sure - as without cleaning the data the AI is handicapped
Sourav Mazumder
Trust on AI Models is key to success of AI Operationalization.
Michael Gunzelmann
@mgunzelmann1 Its really only mathematics giving the computer to "analyze" data the way humans respond.
muratguvenc
I agree. In order to turn AI aspirations into outcomes, organizations need to overcome three major AI challenges: data complexity, skills, and trust.
Michael Gunzelmann
@mgunzelmann1 Now the computer can read and answer requests, more than simple calculations
muratguvenc
According to MIT Sloan, data collection & preparation is the most time consuming and difficult part of AI.
Souvik Banerjee
Along comes speed of decision as well, because by the time the AI is implemented the changing business scenario can leave it unusable, thus the need of real-time AI
Michael Gunzelmann
@mgunzelmann1 New techniques provide automation tools to reduce the time consuming processes.
Shikhar Kwatra
It is necessary to reach a stage where we can trust our AI models and provide explainable insights. https://www.ibm.com/blogs/research/2019/08/ai-expl...
https://www.ibm.com/blogs/research/2019/08/ai-explainability-360/
Introducing AI Explainability 360 | IBM Research Blog
Introducing AI Explainability 360 | IBM Research Blog
IBM Research AI announced AI Explainability 360, an open-source toolkit of algorithms that support the explainability of machine learning models.
Michael Gunzelmann
@mgunzelmann1 Keep in mind, we need to able to assure that AI is making"fair" decissions
Michael Gunzelmann
@mgunzelmann1 AI is much more than models today.
Sourav Mazumder
Before deploying a Model in production one has to ensure all necessary due diligence - data quality, independent validation of model, model's interpretability and overall governance in Model Development Life Cycle
Shikhar Kwatra
In order to transition from black-box to glass-box, it becomes quintessential to provide reasoning behind model outcome. Data Complexity, Skills and trust play a major role in this aspect.
muratguvenc
Is there a prescriptive approach, a framework to accelerate AI adoption?
https://www.crowdchat.net/s/564b8

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https://www.crowdchat.net/s/564b8

Shikhar Kwatra
In order to accelerate the AI Adoption, it is important to streamline and understand the AI ladder- Collect, Organize, Analyze and Infuse. https://www.ibm.com/analytics/journey-to-ai
https://www.ibm.com/analytics/journey-to-ai
Accelerate your journey to AI
Accelerate your journey to AI
AI transforms businesses by generating more accurate predictions, automating countless decisions, and optimizing time. AI is a journey, and it begins with data.
Michael Gunzelmann
@mgunzelmann1 In order to get AI right, you need a platform to address the entire process, Collecting trusted data, organizing and analyzing it, putting it into production and most importantly monitoring it over time!
Michael Gunzelmann
@mgunzelmann1 It is a continuous feedback loop of improvement.
Shikhar Kwatra
Also, from an educational standpoint, there are active courses on Operationalizing AI and deploying AI framework into enterprise. https://www.coursera.org/learn/ibm-ai-ladder-frame...
https://www.coursera.org/learn/ibm-ai-ladder-framework
The AI Ladder: A Framework for Deploying AI in your Enterprise
The AI Ladder: A Framework for Deploying AI in your Enterprise
Offered by IBM. This course is intended for business and technical professionals involved in strategic decision-making focused on bringing AI into their enterprises. Through the use of a conceptual model called “The AI Ladder”, participants in this c...
Sourav Mazumder
From IBM's side we propose 5Cs (Continuous Training, Continuous Validation, Continuous Integration, Continuous Development and Continuous Monitoring) for success of AI Operationalization - https://github.com/ibm-cloud-architecture/refarch-...
https://github.com/ibm-cloud-architecture/refarch-ml-ops/blob/master/README.md
ibm-cloud-architecture/refarch-ml-ops
ibm-cloud-architecture/refarch-ml-ops
Reference architecture for machine learning operations - ibm-cloud-architecture/refarch-ml-ops
Michael Gunzelmann
@mgunzelmann1 A great solution that allows people deploy only what they need and grow over time.
Souvik Banerjee
A platform that can support the entire process of Ingest-Store-Manage-Analyze-Visualize (ISMAV) can handhold in the journey through AI maturity
Shikhar Kwatra
“Enterprise AI” is about solving sophisticated business problems in highly dynamic environments. This requires an understanding of well-defined use cases. IBM's Decision Optimization on CloudPak for Data provides prescriptive analytics - https://www.ibm.com/analytics/decision-optimizatio...
muratguvenc
Let’s get started! Do you agree AI becomes a must-have capability for companies to sustain industry leadership and revenue growth?
https://www.crowdchat.net/s/064bc

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https://www.crowdchat.net/s/064bc

Sourav Mazumder
Yes absolutely. Every customers we are talking today in various industries want to exploit power of AI.
Shikhar Kwatra
It is surely desirable to inculcate AI for sustainable revenue growth. Around 80% of firms expect the number of AI use cases to increase within the next two years.
muratguvenc
According to #IBM Institute of Business Value Study of 13,000 global C-suite 80% of companies are planning large investments in #AI.
Michael Gunzelmann
@gunzelmann1 AI is a great way to make your orgainzation Smarter, Better and Faster
Michael Gunzelmann
@mgunzelmann1 The world has finally started down the path of AI
Michael Gunzelmann
@mgunzelmann1 AI is really about allowing a computer to mimic human responnse rather than just zeros and ones