Miguel Fierro, Principal Data Science Manager at Microsoft, discusses the challenges of applying ML in robotics and the integration of computer vision in sports analytics. He highlights the role of AI in strategic game analysis and explores the evolution of recommendation systems, emphasizing the importance of real-time architectures for personalized recommendations.
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Quick takeaways
ML in robotics approximates traditional control methods, highlighting the need for precision.
Recommender systems drive significant revenue and business outcomes, emphasizing their value in organizations.
Deep dives
The Evolution of Miguel's Career towards Recommender Systems
Miguel Schierro shifted his career focus to recommender systems after recognizing their immense value, driven by the revelation that recommender systems accounted for 35% of Amazon's revenue. This pivotal shift stemmed from the significant ROI and impact observed in this field, compelling Miguel to transition from a background in computer vision and robotics.
The Significance of Recommender Systems in Revenue Generation
Recommender systems play a critical role in driving revenue, evident in Amazon's case where they contributed significantly to the company's total revenue, estimated at $35 billion. Understanding the substantial impact of recommender systems, Miguel emphasized the importance of leveraging them to enhance business outcomes and capitalize on the value they bring to organizations.
Diversity in Architectures for Recommendation Systems
Different architectural approaches like the BATS, real-time, and hybrid architectures are utilized in recommendation systems, offering varying benefits. The BATS architecture involves storing recommendations in databases generated from ML models, while real-time architecture enables instant model deployment for on-the-fly scoring. Hybrid architecture combines aspects of both, providing a balance between real-time responsiveness and database storage for personalized recommendations.
Driving Business Success through Data-Driven Decision-Making
Demonstrating the tangible impact of AI solutions like recommendation systems requires measuring key metrics and conducting A-B tests to evaluate performance. By translating model improvements into monetary gains and articulating the ROI to decision-makers, such investments in AI initiatives become justifiable and showcase the power of data-driven strategies in enhancing business outcomes.
Miguel Fierro is a Principal Data Science Manager at Microsoft and holds a PhD in robotics.
From Robotics to Recommender Systems // MLOps Podcast #240 with Miguel Fierro, Principal Data Science Manager at Microsoft.
Huge thank you to Zilliz for sponsoring this episode. Zilliz - https://zilliz.com/.
// Abstract
Miguel explains the limitations and considerations of applying ML in robotics, contrasting its use against traditional control methods that offer exactness, which ML approaches generally approximate. He discusses the integration of computer vision and machine learning in sports for player movement tracking and performance analysis, highlighting collaborations with European football clubs and the role of artificial intelligence in strategic game analysis, akin to a coach's perspective.
// Bio
Miguel Fierro is a Principal Data Science Manager at Microsoft Spain, where he helps customers solve business problems using artificial intelligence. Previously, he was CEO and founder of Samsamia Technologies, a company that created a visual search engine for fashion items allowing users to find products using images instead of words, and founder of the Robotics Society of Universidad Carlos III, which developed different projects related to UAVs, mobile robots, humanoid robots, and 3D printers. Miguel has also worked as a robotics scientist at Universidad Carlos III of Madrid (UC3M) and King’s College London (KCL) and has collaborated with other universities like Imperial College London and IE University in Madrid. Miguel is an Electrical Engineer by UC3M, PhD in robotics by UC3M in collaboration with KCL, and graduated from MIT Sloan School of Management.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Website: https://miguelgfierro.com
GitHub: https://github.com/miguelgfierro/RecSys at Spotify // Sanket Gupta // MLOps Podcast #232 - https://youtu.be/byH-ARJA4gkRecommenders joins LF AI & Data as new Sandbox project: https://cloudblogs.microsoft.com/opensource/2023/10/10/recommenders-joins-lf-ai-data-as-new-sandbox-project/
--------------- ✌️Connect With Us ✌️ -------------
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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Miguel on LinkedIn: https://www.linkedin.com/in/miguelgfierro/
Timestamps:
[00:00] Miguel's preferred coffee
[00:11] Takeaways
[02:25] Robotics
[10:44] Simpler solutions over ML
[15:11] Robotics and Computer Vision
[19:15] Basketball object detection
[22:43 - 23:50] Zilliz Ad
[23:51] Mr. Recommenders and Recommender systems' common patterns
[31:35] Embeddings and Feature Stores
[42:34] Experiment ROI for leadership
[47:17] Hi ROI investments
[51:13] LLMs in Recommender Systems
[54:51] Wrap up
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