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MLOps Webinar – “Key Strategies Enterprises Can Implement Today to be Successful in AI”

Join 451 Research’s Nick Patience, Datatron Founder/CEO Harish Doddi, and Domino’s Sr. Data Science Manager Zack Fragoso as they discuss MLOps & AI Governance strategies, challenges, and solutions using specific enterprise use cases that have helped them successfully deploy, operate, and govern ML models in production at scale.

 
Part I: Key MLOps Stats – Key Strategies Enterprises Can Implement Today to be Successful in AI
Part II: How Domino’s Leverages ML to be an Industry Leading AI Program at Scale – MLOps Webinar
Part III: How Domino’s Builds a Business Case for AI – MLOps & AI Governance Webinar
Part IV: KPIs & Measuring the Value of AI/ML – MLOps & AI Governance Webinar
Part V: AI Differences in Academia, Research, & Production – MLOps & AI Governance Webinar
Part VI: MLOps Tools, Stack, and the Relationship Between Data Scientists & IT – MLOps Webinar
Part VII: ML Infrastructure Challenges and How to Overcome Them – MLOps & AI Governance Webinar
Part VIII: MLOps and AI Governance Explained: What’s the Difference – MLOps & AI Governance Webinar
Part IX: Model-Centric and Data-Centric Approaches to AI/ML – MLOps & AI Governance Webinar
Part X: Anomaly Detection and Determining the Present Value of a Prediction Model – MLOps Webinar
Part XI: Advice for Early AI/ML Programs from Domino’s & Datatron – MLOps & AI Governance Webinar

Part I: Key MLOps Stats – Key Strategies Enterprises Can Implement Today to be Successful in AI

In Part I of this webinar titled “Key Strategies Enterprises Can Implement Today to be Successful in AI” 451 Research’s Nick Patience kicks off this series sharing key MLOps stats from their semi-annual “Voice of the Enterprise” survey focused on A.I. and Machine Learning.

According to research by multiple analyst firms, more than half of AI initiatives in enterprises end up in failure. Many feel AI is overhyped and not producing any meaningful ROI for the business. Others feel AI is too expensive and can only be used by some of the largest cloud players. Furthermore, there has been considerable negative media coverage on how AI can be biased, resulting in organizations being penalized by regulatory boards or suffering bad press. The good news for enterprises is that AI can be profitable and is no longer inexplicable. The real lack of AI progress has more to do with confusion and lack of knowledge in the market. In this virtual roundtable, hear from leaders in the AI space talking about how they got AI up and running and delivering business value quickly. Panelists will cover real-world “gotchas” and how they tackled them. Learn critical elements that you can implement today to yield positive results tomorrow.

Part II: How Domino’s Leverages ML to be an Industry Leading AI Program at Scale – MLOps Webinar

In Part II of this webinar titled “Key Strategies Enterprises Can Implement Today to be Successful in AI” Domino’s Sr. Data Science Manager Zack Fragoso shares how AI/ML is used at Domino’s across multiple BUs & use cases (Orders, Routing, Staffing, etc.) to be a top AI/ML enterprise and how they interact with more traditional teams. Domino’s has truly cracked the code in AI/ML accelerating model deployment velocity from 1 model/year to 10+ models per year. Learn from a mature AI/ML program.

According to research by multiple analyst firms, more than half of AI initiatives in enterprises end up in failure. Many feel AI is overhyped and not producing any meaningful ROI for the business. Others feel AI is too expensive and can only be used by some of the largest cloud players. Furthermore, there has been considerable negative media coverage on how AI can be biased, resulting in organizations being penalized by regulatory boards or suffering bad press. The good news for enterprises is that AI can be profitable and is no longer inexplicable. The real lack of AI progress has more to do with confusion and lack of knowledge in the market. In this virtual roundtable, hear from leaders in the AI space talking about how they got AI up and running and delivering business value quickly. Panelists will cover real-world “gotchas” and how they tackled them. Learn critical elements that you can implement today to yield positive results tomorrow.

Part III: How Domino’s Builds a Business Case for AI – MLOps & AI Governance Webinar

In Part III of this webinar titled “Key Strategies Enterprises Can Implement Today to be Successful in AI” Domino’s Sr. Data Science Manager Zack Fragoso shares how business cases are built within Domino’s.

According to research by multiple analyst firms, more than half of AI initiatives in enterprises end up in failure. Many feel AI is overhyped and not producing any meaningful ROI for the business. Others feel AI is too expensive and can only be used by some of the largest cloud players. Furthermore, there has been considerable negative media coverage on how AI can be biased, resulting in organizations being penalized by regulatory boards or suffering bad press. The good news for enterprises is that AI can be profitable and is no longer inexplicable. The real lack of AI progress has more to do with confusion and lack of knowledge in the market. In this virtual roundtable, hear from leaders in the AI space talking about how they got AI up and running and delivering business value quickly. Panelists will cover real-world “gotchas” and how they tackled them. Learn critical elements that you can implement today to yield positive results tomorrow.


Your AI Program Deserves Liberation.
Datatron is the Answer.
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Part IV: KPIs & Measuring the Value of AI/ML – MLOps & AI Governance Webinar

In Part IV of this webinar titled “Key Strategies Enterprises Can Implement Today to be Successful in AI” we discuss our approach to KPIs in AI/ML and how we measure success, such as model classification error, accuracy, false-positive rate, false-negative rate, etc.). But how do you translate those into KPIs that the business values?

According to research by multiple analyst firms, more than half of AI initiatives in enterprises end up in failure. Many feel AI is overhyped and not producing any meaningful ROI for the business. Others feel AI is too expensive and can only be used by some of the largest cloud players. Furthermore, there has been considerable negative media coverage on how AI can be biased, resulting in organizations being penalized by regulatory boards or suffering bad press. The good news for enterprises is that AI can be profitable and is no longer inexplicable. The real lack of AI progress has more to do with confusion and lack of knowledge in the market. In this virtual roundtable, hear from leaders in the AI space talking about how they got AI up and running and delivering business value quickly. Panelists will cover real-world “gotchas” and how they tackled them. Learn critical elements that you can implement today to yield positive results tomorrow.

Part V: AI Differences in Academia, Research, & Production – MLOps & AI Governance Webinar

In Part V of this webinar titled “Key Strategies Enterprises Can Implement Today to be Successful in AI” we discuss the differences between AI/ML in Academia, Research, and Production. This includes iterating on a model as well as driving ROI in the business world, scalability, and seamlessly integrating with existing DevOps tools and environments.

According to research by multiple analyst firms, more than half of AI initiatives in enterprises end up in failure. Many feel AI is overhyped and not producing any meaningful ROI for the business. Others feel AI is too expensive and can only be used by some of the largest cloud players. Furthermore, there has been considerable negative media coverage on how AI can be biased, resulting in organizations being penalized by regulatory boards or suffering bad press. The good news for enterprises is that AI can be profitable and is no longer inexplicable. The real lack of AI progress has more to do with confusion and lack of knowledge in the market. In this virtual roundtable, hear from leaders in the AI space talking about how they got AI up and running and delivering business value quickly. Panelists will cover real-world “gotchas” and how they tackled them. Learn critical elements that you can implement today to yield positive results tomorrow.

Part VI: MLOps Tools, Stack, and the Relationship Between Data Scientists & IT – MLOps Webinar

In Part VI of this webinar titled “Key Strategies Enterprises Can Implement Today to be Successful in AI” we discuss the evolution of MLOps from traditional DevOps to support the unique intricacies of AI/ML. Additionally, many companies experience growing pains in maturing their ML program due to friction between Data Scientists and IT/DevOps which stunts AI growth. You’ll learn about the realities of these pain points, as well as how to remedy them.

According to research by multiple analyst firms, more than half of AI initiatives in enterprises end up in failure. Many feel AI is overhyped and not producing any meaningful ROI for the business. Others feel AI is too expensive and can only be used by some of the largest cloud players. Furthermore, there has been considerable negative media coverage on how AI can be biased, resulting in organizations being penalized by regulatory boards or suffering bad press. The good news for enterprises is that AI can be profitable and is no longer inexplicable. The real lack of AI progress has more to do with confusion and lack of knowledge in the market. In this virtual roundtable, hear from leaders in the AI space talking about how they got AI up and running and delivering business value quickly. Panelists will cover real-world “gotchas” and how they tackled them. Learn critical elements that you can implement today to yield positive results tomorrow.

Part VII: ML Infrastructure Challenges and How to Overcome Them – MLOps & AI Governance Webinar

In Part VII of this webinar titled “Key Strategies Enterprises Can Implement Today to be Successful in AI,” Zack Fragoso, Sr. Data Science Manager at Domino’s discusses the unique demands of models in the data science world (e.g., batch processing, scaling, real-time inferencing) and why they selected Kubernetes based Datatron.

According to research by multiple analyst firms, more than half of AI initiatives in enterprises end up in failure. Many feel AI is overhyped and not producing any meaningful ROI for the business. Others feel AI is too expensive and can only be used by some of the largest cloud players. Furthermore, there has been considerable negative media coverage on how AI can be biased, resulting in organizations being penalized by regulatory boards or suffering bad press. The good news for enterprises is that AI can be profitable and is no longer inexplicable. The real lack of AI progress has more to do with confusion and lack of knowledge in the market. In this virtual roundtable, hear from leaders in the AI space talking about how they got AI up and running and delivering business value quickly. Panelists will cover real-world “gotchas” and how they tackled them. Learn critical elements that you can implement today to yield positive results tomorrow.


Your AI Program Deserves Liberation.
Datatron is the Answer.
request a demo

Part VIII: MLOps and AI Governance Explained: What’s the Difference – MLOps & AI Governance Webinar

In Part VIII of this webinar titled “Key Strategies Enterprises Can Implement Today to be Successful in AI,” Harish Doddi, Founder/CEO of Datatron explains his vision for what encompasses MLOps (Machine Learning Operations) and where Governance takes over; deploying models versus transparency (monitoring, alerts for bias & drift, troubleshooting, model “health” dashboard) in running a safe AI practice in the organization.

According to research by multiple analyst firms, more than half of AI initiatives in enterprises end up in failure. Many feel AI is overhyped and not producing any meaningful ROI for the business. Others feel AI is too expensive and can only be used by some of the largest cloud players. Furthermore, there has been considerable negative media coverage on how AI can be biased, resulting in organizations being penalized by regulatory boards or suffering bad press. The good news for enterprises is that AI can be profitable and is no longer inexplicable. The real lack of AI progress has more to do with confusion and lack of knowledge in the market. In this virtual roundtable, hear from leaders in the AI space talking about how they got AI up and running and delivering business value quickly. Panelists will cover real-world “gotchas” and how they tackled them. Learn critical elements that you can implement today to yield positive results tomorrow.

Part IX: Model-Centric and Data-Centric Approaches to AI/ML – MLOps & AI Governance Webinar

In Part IX of this webinar titled “Key Strategies Enterprises Can Implement Today to be Successful in AI,” Harish Doddi, Founder/CEO of Datatron shares his expertise on why models directly integrated with apps is not sustainable, won’t scale, and creates complexity in architecture. People move faster with the separation of data and models, which supports better productivity in AI/ML.

According to research by multiple analyst firms, more than half of AI initiatives in enterprises end up in failure. Many feel AI is overhyped and not producing any meaningful ROI for the business. Others feel AI is too expensive and can only be used by some of the largest cloud players. Furthermore, there has been considerable negative media coverage on how AI can be biased, resulting in organizations being penalized by regulatory boards or suffering bad press. The good news for enterprises is that AI can be profitable and is no longer inexplicable. The real lack of AI progress has more to do with confusion and lack of knowledge in the market. In this virtual roundtable, hear from leaders in the AI space talking about how they got AI up and running and delivering business value quickly. Panelists will cover real-world “gotchas” and how they tackled them. Learn critical elements that you can implement today to yield positive results tomorrow.

Part X: Anomaly Detection and Determining the Present Value of a Prediction Model – MLOps Webinar

In Part X of this webinar titled “Key Strategies Enterprises Can Implement Today to be Successful in AI,” we discuss leveraging the training data as a proxy for models whose results can’t be evaluated until the future (E.g., loan approval model). Datatron uses anomaly detection to evaluate how a model behaved in the past, and then scores how a model behaves in real-time and determines if it is an outlier using “ground truth” data.

According to research by multiple analyst firms, more than half of AI initiatives in enterprises end up in failure. Many feel AI is overhyped and not producing any meaningful ROI for the business. Others feel AI is too expensive and can only be used by some of the largest cloud players. Furthermore, there has been considerable negative media coverage on how AI can be biased, resulting in organizations being penalized by regulatory boards or suffering bad press. The good news for enterprises is that AI can be profitable and is no longer inexplicable. The real lack of AI progress has more to do with confusion and lack of knowledge in the market. In this virtual roundtable, hear from leaders in the AI space talking about how they got AI up and running and delivering business value quickly. Panelists will cover real-world “gotchas” and how they tackled them. Learn critical elements that you can implement today to yield positive results tomorrow.

Part XI: Advice for Early AI/ML Programs from Domino’s & Datatron – MLOps & AI Governance Webinar

In the ultimate and final Part XI of this webinar titled “Key Strategies Enterprises Can Implement Today to be Successful in AI,” Domino’s Sr. Data Science Manager highlights the importance of thinking holistically when mapping out the entire AI/ML program to avoid having to take steps backward to remedy early missteps. Datatron Founder/CEO, Harish Doddi adds the importance of removing silos between IT (MLOps, DevOps, ML Engineers), Data Scientists, and business unit leaders to support the scaling of AI.

Here’s a scenario occurring in increasing numbers: Many company’s initial efforts in ML involve building their own in-house MLOps solution leveraging accessible Open Source point solutions. Eventually, frustration grows as Data Scientist’s models are not making it into production, and AI and BU/LOB executives aren’t getting ROI from ML, all the while IT believes their solution is working but the reality is they are bogged down with unpredictable maintenance chores and little support that impedes consistent & reliable progress. This occurs within and across siloed business units. Enter the CoE to save the day. Driving enterprise-wide initiatives these “mature” AI/ML programs circle back to remedy deficiencies in their homegrown solution with an off-the-shelf solution like Datatron to complement (not replace) their initial “build vs. buy” approach. Model deployment velocity skyrockets and model risk exposure plummets, as do fines from compliance gaffes. All are happy and business-focused benefits from AI proliferate in the enterprise!

To conclude, creating a cross-functional team, complementing build with buy, and leveraging a robust AI governance strategy is the winning approach.

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Success Story: Global Bank Monitors 1,000’s of Models On Datatron

A top global bank was looking for an AI Governance platform and discovered so much more. With Datatron, executives can now easily monitor the “Health” of thousands of models, data scientists decreased the time required to identify issues with models and uncover the root cause by 65%, and each BU decreased their audit reporting time by 65%.

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Success Story: Domino’s 10x Model Deployment Velocity

Domino’s was looking for an AI Governance platform and discovered so much more. With Datatron, Domino’s accelerated model deployment 10x, and achieved 80% more risk-free model deployments, all while giving executives a global view of models and helping them to understand the KPI metrics achieved to increase ROI.

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5 Reasons Your AI/ML Models are Stuck in the Lab

AI/ML Executive need more ROI from AI/ML? Data Scientist want to get more models into production? ML DevOps Engineer/IT want an easier way to manage multiple models. Learn how enterprises with mature AI/ML programs overcome obstacles to operationalize more models with greater ease and less manpower.

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Life Cycle of Machine Learning Models

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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Unique Challenges Of Machine Learning Models In Production

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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Model Deployment

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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Experience “The Datatron” product for yourself in this self-guided series of seven, concise, mini-videos that highlight key features, like the “Model Catalog,” and “Health Dashboard,” as well as Use Cases for Data Scientists (Part III), ML Engineers/DevOps (Part IV), and AI Executives & BU/LOB leaders (Part VII). Enjoy! And, when you are ready, Book a Demo

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