The Power of Bayesian Models in Predictive Analysis
Agents of Innovation: AI-Powered Product Ideation with Synthetic Consumer Testing clip // MLOps Podcast #306 with Luca Fiaschi, Partner of PyMC Labs.
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Luca shares how switching to Bayesian models made forecasting more trustworthy—and way easier to explain to skeptical CMOs and CFOs. Think crystal-clear confidence intervals and forecasts you can actually defend!
// Abstract
Traditional product development cycles require extensive consumer research and market testing, resulting in lengthy development timelines and significant resource investment. We've transformed this process by building a distributed multi-agent system that enables parallel quantitative evaluation of hundreds of product concepts. Our system combines three key components: an Agentic innovation lab generating high-quality product concepts, synthetic consumer panels using fine-tuned foundational models validated against historical data, and an evaluation framework that correlates with real-world testing outcomes. We can talk about how this architecture enables rapid concept discovery and digital experimentation, delivering insights into product success probability before development begins. Through case studies and technical deep-dives, you'll learn how we built an AI powered innovation lab that compresses months of product development and testing into minutes - without sacrificing the accuracy of insights.
// Bio
With over 15 years of leadership experience in AI, data science, and analytics, Luca has driven transformative growth in technology-first businesses. As Chief Data & AI Officer at Mistplay, he led the company’s revenue growth through AI-powered personalization and data-driven pricing. Prior to that, he held executive roles at global industry leaders such as HelloFresh ($8B), Stitch Fix ($1.2B) and Rocket Internet ($1B).
Luca's core competencies include machine learnin
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Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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Remote Collaboration as a Data Scientist
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MLOps Manifesto with Luke Marsden from Dotscience
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MLOps lifecycle description
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What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
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Life purpose and too many spreadsheets
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Explainability, Black boxes and EU white paper on reproducibility
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Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
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Automatically Retrain Machine Learning Models? Are best practices worth it?
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Building an MLOps Team? Key ideas to keep in mind
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Hierarchy of MLOps Needs
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Bare necessities for getting an ML model into production
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MLOps and Monitoring
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How Phil Winder got into Data Science and Software Engineering
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Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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Friction Between Data Scientists and Software Engineers
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MLOps Problems in different size companies
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ML tooling in large companies
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ML Platforms - The build vs buy question
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ML Services Gateway at SurveyMonkey
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Message buses, Async and sync architecture
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MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
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Hybrid Data Science Teams @SurveyMonkey
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How do you handle ML version control at SurveyMonkey
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Doing ML with Personal Information
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Evolution of the ML feature store @SurveyMonkey
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Developing a Machine Learning Feature Store
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Auto retrain ML models is not the question
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3 key parts to Machine Learning monitoring
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MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
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MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
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MLOps: Airflow Pros and Cons
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Specific challenges in Machine Learning
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Current State Of Machine Learning
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Humans in the Loop are a defining factor in Machine Learning
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Learning from real life Machine Learning failures
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Survivorship Bias in machine learning tutorials
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Swiss Cheese model in Machine Learning
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Resume driven development in Machine learning & software engineering
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Who has the highest standards in ML?
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Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
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Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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Speed, Trust, Evolution and Scale in MLOps
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More difficult transition for data scientists to become ML engineers
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How many models in prod til I need a dedicated ML platform?
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Deeper thinking from data scientists around platform blackholes
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Adjacent usecases and multistep feature engineering
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Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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Reproducability flaws in end to end Machine Learning debugging
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3rd wave of data scientists
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MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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Are Kubeflow and Airflow complementary?
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Why Kubeflow gained so much traction=open community
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Who decides the dirrection of Kubeflow
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