Challenges in Manufacturing Quality Control and Moving from Proof of Concept to Production

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ MLOps Coffee Sessions Special episode with LatticeFlow, How to Build Production-Ready AI Models for Manufacturing, fueled by our Premium Brand Partner, LatticeFlow. Deploying AI models in manufacturing involves navigating several technical challenges such as costly data acquisition, class imbalances, data shifts, leakage, and model degradation over time. How can you uncover the causes of model failures and prevent them effectively? This discussion covers practical solutions and advanced techniques to build resilient, safe, and high-performing AI systems in the manufacturing industry. // Bio Pavol Bielik Pavol earned his PhD at ETH Zurich, specializing in machine learning, symbolic AI, synthesis, and programming languages. His groundbreaking research earned him the prestigious Facebook Fellowship in 2017, representing the sole European recipient, along with the Romberg Grant in 2016. Following his doctorate, Pavol's passion for ensuring the safety and reliability of deep learning models led to the founding of LatticeFlow. Building on a more than a decade of research, Pavol and a dynamic team of researchers at LatticeFlow developed a platform that equips companies with the tools to deliver robust and high-performance AI models, utilizing automatic diagnosis and improvement of data and models. Aniket Singh Vision Systems Engineer AI Researcher Mohan Mahadevan Mohan Mahadevan is a seasoned technology leader with 25 years of experience in building computer vision (CV) and machine learning (ML) based products. Mohan has led teams to successfully deliver real world solutions spanning hardware, software, and AI based solutions in over 20 product families across a diverse range of domains, including Semiconductors, Robotics, Fintech, and Insuretech. Mohan Mahadevan has led global teams in the development of cutting-edge technologies across a range of disciplines including computer vision, machine learning, optical and hardware architectures, system design, computational optimization and more. Jürgen Weichenberger 20+ years of advanced analytics, data science, database design, architecture, and implementation on various platforms to solve Complex Industry Problems. Industrial Analytics is the fusion of manufacturing, production, reliability, integrity, quality, sales- and market-analytics and covering 10 Industries. By combining skills and experience, we are creating the next-generation AI & ML Solutions for our clients. Leveraging a unique formula which allows us to model some of the most challenging manufacturing problems while building, scaling, and enabling the end-user to leverage the next generation data products. The Strategy & Innoation Team at Schneider is specialising on Industrial-Grade Challenges where we are applying ML & AI methods to achieve state of the art results. Personally, I am driving my team and my own education to extend the limits of AI & ML beyond the current possible. I hold more than 15 patents and I am working on new innovations. I am working with our partner eco-system to enrich our accelerators with modern ML/AI techniques and integrating robotic equipment allows me to create next generation solutions. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: https://latticeflow.ai/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/

Timestamps: [00:00] Demetrios' Intro [00:48] Announcements [01:57] Join us at our first in-person conference on June 25 all about AI Quality! [03:39] Speakers' intros [06:00] AI ML uncommon use cases [10:14] Challenges in Implementing AI and ML in Heavy Industries [11:41] Optimizing AI use cases [18:07] Moving from PoC to Production [20:53] Hybrid AI Integration for Safety [28:28] Training AI for Defect Variability [33:18] Challenges in AI Integration [35:39] Metrics for Evaluating Success [37:27] Challenges in AI Integration [44:39] Usage of LLMs [50:34] Fine-tuning AI Models [53:20] Trust Dynamics: TML vs LLM [55:23] Wrap up