A Blueprint for Scalable & Reliable Enterprise AI/ML Systems // Panel // AIQCON

MLOps.community · Advanced ·📰 AI News & Updates ·1y ago
// Abstract Enterprise AI leaders continue to explore the best productivity solutions that solve business problems, mitigate risks, and increase efficiency. Building reliable and secure AI/ML systems requires following industry standards, an operating framework, and best practices that can accelerate and streamline the scalable architecture that can produce expected business outcomes. This session, featuring veteran practitioners, focuses on building scalable, reliable, and quality AI and ML systems for the enterprises. // Panelists - Hira Dangol: VP, AI/ML and Automation @ Bank of America - Rama Akkiraju: VP, Enterprise AI/ML @ NVIDIA - Nitin Aggarwal: Head of AI Services @ Google - Steven Eliuk: VP, AI and Governance @ IBM A big thank you to our Premium Sponsors @googlecloud & @Databricks for their generous support! Timestamps: 00:00 Panelists discuss vision and strategy in AI 05:18 Steven Eliuk, IBM expertise in data services 07:30 AI as means to improve business metrics 11:10 Key metrics in production systems: efficiency and revenue 13:50 Consistency in data standards aids data integration 17:47 Generative AI presents new data classification risks 22:47 Evaluating implications, monitoring, and validating use cases 26:41 Evaluating natural language answers for efficient production 29:10 Monitoring AI models for performance and ethics 31:14 AI metrics and user responsibility for future models 34:56 Access to data is improving, promising progress
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 15 Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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 33 Specific challenges in Machine Learning
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 35 Humans in the Loop are a defining factor in Machine Learning
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 36 Learning from real life Machine Learning failures
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 37 Survivorship Bias in machine learning tutorials
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 38 Swiss Cheese model in Machine Learning
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 39 Resume driven development in Machine learning & software engineering
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 41 Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
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 42 Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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 46 Deeper thinking from data scientists around platform blackholes
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 47 Checkpointing, metadata, and confidence in your data
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 48 Adjacent usecases and multistep feature engineering
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 49 Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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 50 Reproducability flaws in end to end Machine Learning debugging
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 51 3rd wave of data scientists
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 52 MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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Chapters (11)

Panelists discuss vision and strategy in AI
5:18 Steven Eliuk, IBM expertise in data services
7:30 AI as means to improve business metrics
11:10 Key metrics in production systems: efficiency and revenue
13:50 Consistency in data standards aids data integration
17:47 Generative AI presents new data classification risks
22:47 Evaluating implications, monitoring, and validating use cases
26:41 Evaluating natural language answers for efficient production
29:10 Monitoring AI models for performance and ethics
31:14 AI metrics and user responsibility for future models
34:56 Access to data is improving, promising progress
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