SlideShare a Scribd company logo

System design for recommendations and search

•
•
Eugene Yan Ziyou
Eugene Yan Ziyou

Shared at San Francisco Big Analytics Meetup (2021-07-13)

Data & Analytics
1 of 54
Download to read offline
System Design for Recommendations & Search Eugene Yan, Applied Scientist @ Amazon San Francisco Big Analytics Meetup (2021-07-13)
About me ● Applied Scientist @ Amazon ○ Machine Learning for Recommendations & Search ● Previously: VP, Data Science @ Lazada ○ Led the team on e-commerce ML systems ○ Facilitated integration with Alibaba (acquired 2016) ● Previously: Data Science Lead @ Healthtech startup ○ Early detection of preventable diseases ○ Healthcare resource allocation ● More at eugeneyan.com
Topics ● Context: Batch vs. Real-time (i.e., on-demand) ● System design ○ Offline vs. Online environments ○ Candidate retrieval vs. Ranking ● Industry Examples ○ Alibaba, FaceBook, DoorDash ● How to build an MVP ○ If there’s time...
Batch vs. Real-time
Source: https:/ /softwareengineeringdaily.com/2018/11/09/converged-data-platform-unifying-streaming-data-using-mapr/
Recommender (REST/gRPC) Recommender (key-value DB) Recommendations refreshed periodically Recommendations generated on-demand
Recommender (REST/gRPC) Recommender (key-value DB) Recommendations refreshed periodically Recommendations generated in real-time
Recommender (REST/gRPC) Recommender (key-value DB) Recommendations refreshed periodically Recommendations generated in real-time ● Pre-computed ● Decouple compute from serving ● Lower operational load
Recommender (REST/gRPC) Recommender (key-value DB) Recommendations refreshed periodically Recommendations generated in real-time ● Pre-computed ● Decouple compute from serving ● Lower operational load ● Responsive to time-sensitive context ● Reduced cost on non-visiting users
Recommender (REST/gRPC) Recommender (key-value DB) Recommendations refreshed periodically Recommendations generated in real-time ● Pre-computed ● Decouple compute from serving ● Lower operational load We will focus on real-time aka on-demand ● Responsive to time-sensitive context ● Reduced cost on non-visiting users
System Design
Online Offline
Online Offline ● Host batch processes such as training, index/graph building ● Load data into feature stores
Online Offline ● Host batch processes such as training, index/graph building ● Load data into feature stores ● Uses artifacts from offline environment to serve requests ● Candidate retrieval and ranking
Retrieval Ranking
Retrieval Ranking ● Fast but coarse ● Searches millions of items to get hundreds of candidates ● Approx NN, Graphs, etc.
Retrieval Ranking ● Fast but coarse ● Searches millions of items to get hundreds of candidates ● Approx NN, Graphs, etc. ● Slower but more precise ● Ranks hundreds of candidates ● Adds more features ● Classification or learning-to-rank
Online Offline Retrieval Ranking
Online Offline Retrieval Ranking Train embedding model Train ranking model Embed items from catalog Build Approx. NN index Embed input item or query Retrieve top k candidates Build feature store (e.g., item, user) Add features to candidates Rank top k candidates
Online Offline Retrieval Ranking Train embedding model Training data Embed items from catalog Build Approx. NN index Item data
Online Offline Retrieval Ranking Train embedding model Training data Training data Train ranking model Embed items from catalog Build Approx. NN index Item data Build feature store (e.g., item, user) Item & user data
Online Offline Retrieval Ranking Train embedding model Training data Training data Train ranking model Embed items from catalog Build Approx. NN index Item data Embedding model ANN Index Build feature store (e.g., item, user) Item & user data Feature Store Ranking model
Online Offline Retrieval Ranking Embed input item or query Retrieve top k candidates Request Embedding model ANN Index
Online Offline Retrieval Ranking Embed input item or query Retrieve top k candidates Request Embedding model ANN Index Add features to candidates Rank top k candidates Feature Store Ranking model Results
Online Offline Retrieval Ranking Train embedding model Training data Training data Train ranking model Embed items from catalog Build Approx. NN index Item data Embed input item or query Retrieve top k candidates Request Embedding model ANN Index Build feature store (e.g., item, user) Item & user data Add features to candidates Rank top k candidates Feature Store Ranking model Results
Industry Examples
Building item embeddings for candidate retrieval (Alibaba) Source: https:/ /arxiv.org/abs/1803.02349
Source: https:/ /arxiv.org/abs/1803.02349 Building item embeddings for candidate retrieval (Alibaba)
Source: https:/ /arxiv.org/abs/1803.02349 Ranker Train embedding Candidate Retrieval Building item embeddings for candidate retrieval (Alibaba)
Building a graph network for ranking (Alibaba) Source: https:/ /arxiv.org/abs/2005.12002
Building a graph network for ranking (Alibaba) Source: https:/ /arxiv.org/abs/2005.12002
Building a graph network for ranking (Alibaba) Source: https:/ /arxiv.org/abs/2005.12002
Building a graph network for ranking (Alibaba) Source: https:/ /arxiv.org/abs/2005.12002 Ranker Candidate Retrieval Train Ranker Feature Stores Graph
Building embeddings for retrieval in search (Facebook) Source: https:/ /arxiv.org/abs/2006.11632
Building embeddings for retrieval in search (Facebook) Source: https:/ /arxiv.org/abs/2006.11632 Train Embedding model
Building embeddings for retrieval in search (Facebook) Source: https:/ /arxiv.org/abs/2006.11632 Train Embedding model
Building embeddings for retrieval in search (Facebook) Source: https:/ /arxiv.org/abs/2006.11632 Train Embedding model
Building embeddings for retrieval in search (Facebook) Source: https:/ /arxiv.org/abs/2006.11632 Train Embedding model
Building embeddings for retrieval in search (Facebook) Source: https:/ /arxiv.org/abs/2006.11632 Train Embedding model Offline Online
Building embeddings for retrieval in search (Facebook) Source: https:/ /arxiv.org/abs/2006.11632 Train Embedding model Retrieval Ranker Feature Store Offline Online
Building graphs for query expansion & retrieval (DoorDash) Source: https:/ /doordash.engineering/2020/12/15/understanding-search-intent-with-better-recall/
Building graphs for query expansion & retrieval (DoorDash) Source: https:/ /doordash.engineering/2020/12/15/understanding-search-intent-with-better-recall/
Building graphs for query expansion & retrieval (DoorDash) Source: https:/ /doordash.engineering/2020/12/15/understanding-search-intent-with-better-recall/
Building graphs for query expansion & retrieval (DoorDash) Source: https:/ /doordash.engineering/2020/12/15/understanding-search-intent-with-better-recall/
Building graphs for query expansion & retrieval (DoorDash) Source: https:/ /doordash.engineering/2020/12/15/understanding-search-intent-with-better-recall/
Building graphs for query expansion & retrieval (DoorDash) Source: https:/ /doordash.engineering/2020/12/15/understanding-search-intent-with-better-recall/ Knowledge Graph Retrieval Ranker
How to build an MVP
Source: https:/ /ai.facebook.com/blog/powered-by-ai-instagrams-explore-recommender-system/ Instagram’s ig2vec Training: Self-supervised Representation Learning
Source: https:/ /eng.uber.com/uber-eats-query-understanding/ Uber’s Query2Vec Training: Self-supervised Representation Learning
Training: Self-supervised Representation Learning Source: https:/ /arxiv.org/abs/1803.02349 Alibaba’s item2vec
Retrieval: Approximate nearest neighbors Source: https:/ /eugeneyan.com/writing/real-time-recommendations/
Ranking: Logistic regression Source: https:/ /eugeneyan.com/writing/real-time-recommendations/ • • • User features Item features Probability of interaction
Serving: Multiple instances + Load Balancer (or SageMaker) Source: https:/ /eugeneyan.com/writing/real-time-recommendations/
Fin eugeneyan • eugeneyan.com

Recommended

[QCon.ai 2019] People You May Know: Fast Recommendations Over Massive Data
[QCon.ai 2019] People You May Know: Fast Recommendations Over Massive Data[QCon.ai 2019] People You May Know: Fast Recommendations Over Massive Data
[QCon.ai 2019] People You May Know: Fast Recommendations Over Massive DataSumit Rangwala
 
Recent Trends in Personalization: A Netflix Perspective
Recent Trends in Personalization: A Netflix PerspectiveRecent Trends in Personalization: A Netflix Perspective
Recent Trends in Personalization: A Netflix PerspectiveJustin Basilico
 
Recommender Systems
Recommender SystemsRecommender Systems
Recommender SystemsCarlos Castillo (ChaTo)
 
Deep Learning for Recommender Systems
Deep Learning for Recommender SystemsDeep Learning for Recommender Systems
Deep Learning for Recommender SystemsJustin Basilico
 
Deep Learning for Personalized Search and Recommender Systems
Deep Learning for Personalized Search and Recommender SystemsDeep Learning for Personalized Search and Recommender Systems
Deep Learning for Personalized Search and Recommender SystemsBenjamin Le
 
Recommender system introduction
Recommender system introductionRecommender system introduction
Recommender system introductionLiang Xiang
 
Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorial
Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorialLearning to Rank for Recommender Systems - ACM RecSys 2013 tutorial
Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorialAlexandros Karatzoglou
 
Anatomy of an eCommerce Search Engine by Mayur Datar
Anatomy of an eCommerce Search Engine by Mayur DatarAnatomy of an eCommerce Search Engine by Mayur Datar
Anatomy of an eCommerce Search Engine by Mayur DatarNaresh Jain
 

Recommended

[QCon.ai 2019] People You May Know: Fast Recommendations Over Massive Data
[QCon.ai 2019] People You May Know: Fast Recommendations Over Massive Data[QCon.ai 2019] People You May Know: Fast Recommendations Over Massive Data
[QCon.ai 2019] People You May Know: Fast Recommendations Over Massive DataSumit Rangwala
 
Recent Trends in Personalization: A Netflix Perspective
Recent Trends in Personalization: A Netflix PerspectiveRecent Trends in Personalization: A Netflix Perspective
Recent Trends in Personalization: A Netflix PerspectiveJustin Basilico
 
Recommender Systems
Recommender SystemsRecommender Systems
Recommender SystemsCarlos Castillo (ChaTo)
 
Deep Learning for Recommender Systems
Deep Learning for Recommender SystemsDeep Learning for Recommender Systems
Deep Learning for Recommender SystemsJustin Basilico
 
Deep Learning for Personalized Search and Recommender Systems
Deep Learning for Personalized Search and Recommender SystemsDeep Learning for Personalized Search and Recommender Systems
Deep Learning for Personalized Search and Recommender SystemsBenjamin Le
 
Recommender system introduction
Recommender system introductionRecommender system introduction
Recommender system introductionLiang Xiang
 
Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorial
Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorialLearning to Rank for Recommender Systems - ACM RecSys 2013 tutorial
Learning to Rank for Recommender Systems - ACM RecSys 2013 tutorialAlexandros Karatzoglou
 
Anatomy of an eCommerce Search Engine by Mayur Datar
Anatomy of an eCommerce Search Engine by Mayur DatarAnatomy of an eCommerce Search Engine by Mayur Datar
Anatomy of an eCommerce Search Engine by Mayur DatarNaresh Jain
 
A Multi-Armed Bandit Framework For Recommendations at Netflix
A Multi-Armed Bandit Framework For Recommendations at NetflixA Multi-Armed Bandit Framework For Recommendations at Netflix
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
 
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...Xavier Amatriain
 
Recommender Systems (Machine Learning Summer School 2014 @ CMU)
Recommender Systems (Machine Learning Summer School 2014 @ CMU)Recommender Systems (Machine Learning Summer School 2014 @ CMU)
Recommender Systems (Machine Learning Summer School 2014 @ CMU)Xavier Amatriain
 
Learning to Rank Presentation (v2) at LexisNexis Search Guild
Learning to Rank Presentation (v2) at LexisNexis Search GuildLearning to Rank Presentation (v2) at LexisNexis Search Guild
Learning to Rank Presentation (v2) at LexisNexis Search GuildSujit Pal
 
Sequential Decision Making in Recommendations
Sequential Decision Making in RecommendationsSequential Decision Making in Recommendations
Sequential Decision Making in RecommendationsJaya Kawale
 
Learn to Rank search results
Learn to Rank search resultsLearn to Rank search results
Learn to Rank search resultsGanesh Venkataraman
 
Recommendation engines
Recommendation enginesRecommendation engines
Recommendation enginesGeorgian Micsa
 
Past, Present & Future of Recommender Systems: An Industry Perspective
Past, Present & Future of Recommender Systems: An Industry PerspectivePast, Present & Future of Recommender Systems: An Industry Perspective
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
 
Crafting Recommenders: the Shallow and the Deep of it!
Crafting Recommenders: the Shallow and the Deep of it! Crafting Recommenders: the Shallow and the Deep of it!
Crafting Recommenders: the Shallow and the Deep of it! Sudeep Das, Ph.D.
 
Personalized Page Generation for Browsing Recommendations
Personalized Page Generation for Browsing RecommendationsPersonalized Page Generation for Browsing Recommendations
Personalized Page Generation for Browsing RecommendationsJustin Basilico
 
Deep Learning for Recommender Systems
Deep Learning for Recommender SystemsDeep Learning for Recommender Systems
Deep Learning for Recommender SystemsYves Raimond
 
Introduction to Recommendation Systems
Introduction to Recommendation SystemsIntroduction to Recommendation Systems
Introduction to Recommendation SystemsTrieu Nguyen
 
Warsaw Data Science - Factorization Machines Introduction
Warsaw Data Science - Factorization Machines IntroductionWarsaw Data Science - Factorization Machines Introduction
Warsaw Data Science - Factorization Machines IntroductionBartlomiej Twardowski
 
Recent advances in deep recommender systems
Recent advances in deep recommender systemsRecent advances in deep recommender systems
Recent advances in deep recommender systemsNAVER Engineering
 
Learning to rank
Learning to rankLearning to rank
Learning to rankBruce Kuo
 
Recommendation System Explained
Recommendation System ExplainedRecommendation System Explained
Recommendation System ExplainedCrossing Minds
 
Recommendation system
Recommendation systemRecommendation system
Recommendation systemAkshat Thakar
 
Recommender systems: Content-based and collaborative filtering
Recommender systems: Content-based and collaborative filteringRecommender systems: Content-based and collaborative filtering
Recommender systems: Content-based and collaborative filteringViet-Trung TRAN
 
Making Netflix Machine Learning Algorithms Reliable
Making Netflix Machine Learning Algorithms ReliableMaking Netflix Machine Learning Algorithms Reliable
Making Netflix Machine Learning Algorithms ReliableJustin Basilico
 
An introduction to Recommender Systems
An introduction to Recommender SystemsAn introduction to Recommender Systems
An introduction to Recommender SystemsDavid Zibriczky
 
Web Page Ranking using Machine Learning
Web Page Ranking using Machine LearningWeb Page Ranking using Machine Learning
Web Page Ranking using Machine LearningPradip Rahul
 
Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022
Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022
Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022Jim Dowling
 

More Related Content

What's hot

A Multi-Armed Bandit Framework For Recommendations at Netflix
A Multi-Armed Bandit Framework For Recommendations at NetflixA Multi-Armed Bandit Framework For Recommendations at Netflix
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
 
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...Xavier Amatriain
 
Recommender Systems (Machine Learning Summer School 2014 @ CMU)
Recommender Systems (Machine Learning Summer School 2014 @ CMU)Recommender Systems (Machine Learning Summer School 2014 @ CMU)
Recommender Systems (Machine Learning Summer School 2014 @ CMU)Xavier Amatriain
 
Learning to Rank Presentation (v2) at LexisNexis Search Guild
Learning to Rank Presentation (v2) at LexisNexis Search GuildLearning to Rank Presentation (v2) at LexisNexis Search Guild
Learning to Rank Presentation (v2) at LexisNexis Search GuildSujit Pal
 
Sequential Decision Making in Recommendations
Sequential Decision Making in RecommendationsSequential Decision Making in Recommendations
Sequential Decision Making in RecommendationsJaya Kawale
 
Recommendation engines
Recommendation enginesRecommendation engines
Recommendation enginesGeorgian Micsa
 
Past, Present & Future of Recommender Systems: An Industry Perspective
Past, Present & Future of Recommender Systems: An Industry PerspectivePast, Present & Future of Recommender Systems: An Industry Perspective
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
 
Crafting Recommenders: the Shallow and the Deep of it!
Crafting Recommenders: the Shallow and the Deep of it! Crafting Recommenders: the Shallow and the Deep of it!
Crafting Recommenders: the Shallow and the Deep of it! Sudeep Das, Ph.D.
 
Personalized Page Generation for Browsing Recommendations
Personalized Page Generation for Browsing RecommendationsPersonalized Page Generation for Browsing Recommendations
Personalized Page Generation for Browsing RecommendationsJustin Basilico
 
Deep Learning for Recommender Systems
Deep Learning for Recommender SystemsDeep Learning for Recommender Systems
Deep Learning for Recommender SystemsYves Raimond
 
Introduction to Recommendation Systems
Introduction to Recommendation SystemsIntroduction to Recommendation Systems
Introduction to Recommendation SystemsTrieu Nguyen
 
Warsaw Data Science - Factorization Machines Introduction
Warsaw Data Science - Factorization Machines IntroductionWarsaw Data Science - Factorization Machines Introduction
Warsaw Data Science - Factorization Machines IntroductionBartlomiej Twardowski
 
Recent advances in deep recommender systems
Recent advances in deep recommender systemsRecent advances in deep recommender systems
Recent advances in deep recommender systemsNAVER Engineering
 
Learning to rank
Learning to rankLearning to rank
Learning to rankBruce Kuo
 
Recommendation System Explained
Recommendation System ExplainedRecommendation System Explained
Recommendation System ExplainedCrossing Minds
 
Recommendation system
Recommendation systemRecommendation system
Recommendation systemAkshat Thakar
 
Recommender systems: Content-based and collaborative filtering
Recommender systems: Content-based and collaborative filteringRecommender systems: Content-based and collaborative filtering
Recommender systems: Content-based and collaborative filteringViet-Trung TRAN
 
Making Netflix Machine Learning Algorithms Reliable
Making Netflix Machine Learning Algorithms ReliableMaking Netflix Machine Learning Algorithms Reliable
Making Netflix Machine Learning Algorithms ReliableJustin Basilico
 
An introduction to Recommender Systems
An introduction to Recommender SystemsAn introduction to Recommender Systems
An introduction to Recommender SystemsDavid Zibriczky
 

What's hot (20)

A Multi-Armed Bandit Framework For Recommendations at Netflix
A Multi-Armed Bandit Framework For Recommendations at NetflixA Multi-Armed Bandit Framework For Recommendations at Netflix
A Multi-Armed Bandit Framework For Recommendations at Netflix
 
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...
Recsys 2016 tutorial: Lessons learned from building real-life recommender sys...
 
Recommender Systems (Machine Learning Summer School 2014 @ CMU)
Recommender Systems (Machine Learning Summer School 2014 @ CMU)Recommender Systems (Machine Learning Summer School 2014 @ CMU)
Recommender Systems (Machine Learning Summer School 2014 @ CMU)
 
Learning to Rank Presentation (v2) at LexisNexis Search Guild
Learning to Rank Presentation (v2) at LexisNexis Search GuildLearning to Rank Presentation (v2) at LexisNexis Search Guild
Learning to Rank Presentation (v2) at LexisNexis Search Guild
 
Sequential Decision Making in Recommendations
Sequential Decision Making in RecommendationsSequential Decision Making in Recommendations
Sequential Decision Making in Recommendations
 
Learn to Rank search results
Learn to Rank search resultsLearn to Rank search results
Learn to Rank search results
 
Recommendation engines
Recommendation enginesRecommendation engines
Recommendation engines
 
Past, Present & Future of Recommender Systems: An Industry Perspective
Past, Present & Future of Recommender Systems: An Industry PerspectivePast, Present & Future of Recommender Systems: An Industry Perspective
Past, Present & Future of Recommender Systems: An Industry Perspective
 
Crafting Recommenders: the Shallow and the Deep of it!
Crafting Recommenders: the Shallow and the Deep of it! Crafting Recommenders: the Shallow and the Deep of it!
Crafting Recommenders: the Shallow and the Deep of it!
 
Personalized Page Generation for Browsing Recommendations
Personalized Page Generation for Browsing RecommendationsPersonalized Page Generation for Browsing Recommendations
Personalized Page Generation for Browsing Recommendations
 
Deep Learning for Recommender Systems
Deep Learning for Recommender SystemsDeep Learning for Recommender Systems
Deep Learning for Recommender Systems
 
Introduction to Recommendation Systems
Introduction to Recommendation SystemsIntroduction to Recommendation Systems
Introduction to Recommendation Systems
 
Warsaw Data Science - Factorization Machines Introduction
Warsaw Data Science - Factorization Machines IntroductionWarsaw Data Science - Factorization Machines Introduction
Warsaw Data Science - Factorization Machines Introduction
 
Recent advances in deep recommender systems
Recent advances in deep recommender systemsRecent advances in deep recommender systems
Recent advances in deep recommender systems
 
Learning to rank
Learning to rankLearning to rank
Learning to rank
 
Recommendation System Explained
Recommendation System ExplainedRecommendation System Explained
Recommendation System Explained
 
Recommendation system
Recommendation systemRecommendation system
Recommendation system
 
Recommender systems: Content-based and collaborative filtering
Recommender systems: Content-based and collaborative filteringRecommender systems: Content-based and collaborative filtering
Recommender systems: Content-based and collaborative filtering
 
Making Netflix Machine Learning Algorithms Reliable
Making Netflix Machine Learning Algorithms ReliableMaking Netflix Machine Learning Algorithms Reliable
Making Netflix Machine Learning Algorithms Reliable
 
An introduction to Recommender Systems
An introduction to Recommender SystemsAn introduction to Recommender Systems
An introduction to Recommender Systems
 

Similar to System design for recommendations and search

Web Page Ranking using Machine Learning
Web Page Ranking using Machine LearningWeb Page Ranking using Machine Learning
Web Page Ranking using Machine LearningPradip Rahul
 
Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022
Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022
Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022Jim Dowling
 
Continuous delivery for machine learning
Continuous delivery for machine learningContinuous delivery for machine learning
Continuous delivery for machine learningRajesh Muppalla
 
How To Implement Your Online Search Quality Evaluation With Kibana
How To Implement Your Online Search Quality Evaluation With KibanaHow To Implement Your Online Search Quality Evaluation With Kibana
How To Implement Your Online Search Quality Evaluation With KibanaSease
 
Elastic loves Graphs
Elastic loves GraphsElastic loves Graphs
Elastic loves GraphsGraphRM
 
Graphs in Action: In-depth look at Neo4j in Production
Graphs in Action: In-depth look at Neo4j in ProductionGraphs in Action: In-depth look at Neo4j in Production
Graphs in Action: In-depth look at Neo4j in ProductionNeo4j
 
Real-time Machine Learning with Hopsworks
Real-time Machine Learning with Hopsworks Real-time Machine Learning with Hopsworks
Real-time Machine Learning with Hopsworks AlbaTorrado
 
Building a Dataset Search Engine with Spark and Elasticsearch: Spark Summit E...
Building a Dataset Search Engine with Spark and Elasticsearch: Spark Summit E...Building a Dataset Search Engine with Spark and Elasticsearch: Spark Summit E...
Building a Dataset Search Engine with Spark and Elasticsearch: Spark Summit E...Spark Summit
 
Google Search Engine
Google Search EngineGoogle Search Engine
Google Search Engineguestf460ed0
 
Learning to Rank Datasets for Search with Oscar Castaneda
Learning to Rank Datasets for Search with Oscar CastanedaLearning to Rank Datasets for Search with Oscar Castaneda
Learning to Rank Datasets for Search with Oscar CastanedaDatabricks
 
Wix Machine Learning - Ran Romano
Wix Machine Learning - Ran RomanoWix Machine Learning - Ran Romano
Wix Machine Learning - Ran RomanoWix Engineering
 
Interleaving, Evaluation to Self-learning Search @904Labs
Interleaving, Evaluation to Self-learning Search @904LabsInterleaving, Evaluation to Self-learning Search @904Labs
Interleaving, Evaluation to Self-learning Search @904LabsJohn T. Kane
 
The right path to making search relevant - Taxonomy Bootcamp London 2019
The right path to making search relevant - Taxonomy Bootcamp London 2019The right path to making search relevant - Taxonomy Bootcamp London 2019
The right path to making search relevant - Taxonomy Bootcamp London 2019OpenSource Connections
 
Amazon Machine Learning im Einsatz: smartes Marketing - AWS Machine Learning...
Amazon Machine Learning im Einsatz: smartes Marketing - AWS Machine Learning...Amazon Machine Learning im Einsatz: smartes Marketing - AWS Machine Learning...
Amazon Machine Learning im Einsatz: smartes Marketing - AWS Machine Learning...AWS Germany
 
Deployment Design Patterns - Deploying Machine Learning and Deep Learning Mod...
Deployment Design Patterns - Deploying Machine Learning and Deep Learning Mod...Deployment Design Patterns - Deploying Machine Learning and Deep Learning Mod...
Deployment Design Patterns - Deploying Machine Learning and Deep Learning Mod...All Things Open
 

Similar to System design for recommendations and search (20)

Web Page Ranking using Machine Learning
Web Page Ranking using Machine LearningWeb Page Ranking using Machine Learning
Web Page Ranking using Machine Learning
 
Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022
Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022
Real-Time Recommendations with Hopsworks and OpenSearch - MLOps World 2022
 
Continuous delivery for machine learning
Continuous delivery for machine learningContinuous delivery for machine learning
Continuous delivery for machine learning
 
How To Implement Your Online Search Quality Evaluation With Kibana
How To Implement Your Online Search Quality Evaluation With KibanaHow To Implement Your Online Search Quality Evaluation With Kibana
How To Implement Your Online Search Quality Evaluation With Kibana
 
Elastic loves Graphs
Elastic loves GraphsElastic loves Graphs
Elastic loves Graphs
 
Graphs in Action: In-depth look at Neo4j in Production
Graphs in Action: In-depth look at Neo4j in ProductionGraphs in Action: In-depth look at Neo4j in Production
Graphs in Action: In-depth look at Neo4j in Production
 
Real-time Machine Learning with Hopsworks
Real-time Machine Learning with Hopsworks Real-time Machine Learning with Hopsworks
Real-time Machine Learning with Hopsworks
 
Building a Dataset Search Engine with Spark and Elasticsearch: Spark Summit E...
Building a Dataset Search Engine with Spark and Elasticsearch: Spark Summit E...Building a Dataset Search Engine with Spark and Elasticsearch: Spark Summit E...
Building a Dataset Search Engine with Spark and Elasticsearch: Spark Summit E...
 
Google Search Engine
Google Search EngineGoogle Search Engine
Google Search Engine
 
Google Search Engine
Google Search EngineGoogle Search Engine
Google Search Engine
 
Chris regan schema
Chris regan schemaChris regan schema
Chris regan schema
 
Google
GoogleGoogle
Google
 
Learning to Rank Datasets for Search with Oscar Castaneda
Learning to Rank Datasets for Search with Oscar CastanedaLearning to Rank Datasets for Search with Oscar Castaneda
Learning to Rank Datasets for Search with Oscar Castaneda
 
Wix Machine Learning - Ran Romano
Wix Machine Learning - Ran RomanoWix Machine Learning - Ran Romano
Wix Machine Learning - Ran Romano
 
Interleaving, Evaluation to Self-learning Search @904Labs
Interleaving, Evaluation to Self-learning Search @904LabsInterleaving, Evaluation to Self-learning Search @904Labs
Interleaving, Evaluation to Self-learning Search @904Labs
 
The right path to making search relevant - Taxonomy Bootcamp London 2019
The right path to making search relevant - Taxonomy Bootcamp London 2019The right path to making search relevant - Taxonomy Bootcamp London 2019
The right path to making search relevant - Taxonomy Bootcamp London 2019
 
Introducing MLOps.pdf
Introducing MLOps.pdfIntroducing MLOps.pdf
Introducing MLOps.pdf
 
Amazon Machine Learning im Einsatz: smartes Marketing - AWS Machine Learning...
Amazon Machine Learning im Einsatz: smartes Marketing - AWS Machine Learning...Amazon Machine Learning im Einsatz: smartes Marketing - AWS Machine Learning...
Amazon Machine Learning im Einsatz: smartes Marketing - AWS Machine Learning...
 
Macran
MacranMacran
Macran
 
Deployment Design Patterns - Deploying Machine Learning and Deep Learning Mod...
Deployment Design Patterns - Deploying Machine Learning and Deep Learning Mod...Deployment Design Patterns - Deploying Machine Learning and Deep Learning Mod...
Deployment Design Patterns - Deploying Machine Learning and Deep Learning Mod...
 

More from Eugene Yan Ziyou

Recommender Systems: Beyond the user-item matrix
Recommender Systems: Beyond the user-item matrixRecommender Systems: Beyond the user-item matrix
Recommender Systems: Beyond the user-item matrixEugene Yan Ziyou
 
Predicting Hospital Bills at Pre-admission
Predicting Hospital Bills at Pre-admissionPredicting Hospital Bills at Pre-admission
Predicting Hospital Bills at Pre-admissionEugene Yan Ziyou
 
OLX Group Prod Tech 2019 Keynote: Asia's Tech Giants
OLX Group Prod Tech 2019 Keynote: Asia's Tech GiantsOLX Group Prod Tech 2019 Keynote: Asia's Tech Giants
OLX Group Prod Tech 2019 Keynote: Asia's Tech GiantsEugene Yan Ziyou
 
Data Science Challenges and Impact at Lazada (Big Data and Analytics Innovati...
Data Science Challenges and Impact at Lazada (Big Data and Analytics Innovati...Data Science Challenges and Impact at Lazada (Big Data and Analytics Innovati...
Data Science Challenges and Impact at Lazada (Big Data and Analytics Innovati...Eugene Yan Ziyou
 
INSEAD Sharing on Lazada Data Science and my Journey
INSEAD Sharing on Lazada Data Science and my JourneyINSEAD Sharing on Lazada Data Science and my Journey
INSEAD Sharing on Lazada Data Science and my JourneyEugene Yan Ziyou
 
SMU BIA Sharing on Data Science
SMU BIA Sharing on Data ScienceSMU BIA Sharing on Data Science
SMU BIA Sharing on Data ScienceEugene Yan Ziyou
 
Culture at Lazada Data Science
Culture at Lazada Data ScienceCulture at Lazada Data Science
Culture at Lazada Data ScienceEugene Yan Ziyou
 
Competition Improves Performance: Only when Competition Form matches Goal Ori...
Competition Improves Performance: Only when Competition Form matches Goal Ori...Competition Improves Performance: Only when Competition Form matches Goal Ori...
Competition Improves Performance: Only when Competition Form matches Goal Ori...Eugene Yan Ziyou
 
How Lazada ranks products to improve customer experience and conversion
How Lazada ranks products to improve customer experience and conversionHow Lazada ranks products to improve customer experience and conversion
How Lazada ranks products to improve customer experience and conversionEugene Yan Ziyou
 
Sharing about my data science journey and what I do at Lazada
Sharing about my data science journey and what I do at LazadaSharing about my data science journey and what I do at Lazada
Sharing about my data science journey and what I do at LazadaEugene Yan Ziyou
 
AXA x DSSG Meetup Sharing (Feb 2016)
AXA x DSSG Meetup Sharing (Feb 2016)AXA x DSSG Meetup Sharing (Feb 2016)
AXA x DSSG Meetup Sharing (Feb 2016)Eugene Yan Ziyou
 
Garuda Robotics x DataScience SG Meetup (Sep 2015)
Garuda Robotics x DataScience SG Meetup (Sep 2015)Garuda Robotics x DataScience SG Meetup (Sep 2015)
Garuda Robotics x DataScience SG Meetup (Sep 2015)Eugene Yan Ziyou
 
DataKind SG sharing of our first DataDive
DataKind SG sharing of our first DataDiveDataKind SG sharing of our first DataDive
DataKind SG sharing of our first DataDiveEugene Yan Ziyou
 
Social network analysis and growth recommendations for DataScience SG community
Social network analysis and growth recommendations for DataScience SG communitySocial network analysis and growth recommendations for DataScience SG community
Social network analysis and growth recommendations for DataScience SG communityEugene Yan Ziyou
 
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learnt
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learntKaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learnt
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learntEugene Yan Ziyou
 
Nielsen x DataScience SG Meetup (Apr 2015)
Nielsen x DataScience SG Meetup (Apr 2015)Nielsen x DataScience SG Meetup (Apr 2015)
Nielsen x DataScience SG Meetup (Apr 2015)Eugene Yan Ziyou
 
Statistical inference: Statistical Power, ANOVA, and Post Hoc tests
Statistical inference: Statistical Power, ANOVA, and Post Hoc testsStatistical inference: Statistical Power, ANOVA, and Post Hoc tests
Statistical inference: Statistical Power, ANOVA, and Post Hoc testsEugene Yan Ziyou
 
Statistical inference: Hypothesis Testing and t-tests
Statistical inference: Hypothesis Testing and t-testsStatistical inference: Hypothesis Testing and t-tests
Statistical inference: Hypothesis Testing and t-testsEugene Yan Ziyou
 
Statistical inference: Probability and Distribution
Statistical inference: Probability and DistributionStatistical inference: Probability and Distribution
Statistical inference: Probability and DistributionEugene Yan Ziyou
 
A Study on the Relationship between Education and Income in the US
A Study on the Relationship between Education and Income in the USA Study on the Relationship between Education and Income in the US
A Study on the Relationship between Education and Income in the USEugene Yan Ziyou
 

More from Eugene Yan Ziyou (20)

Recommender Systems: Beyond the user-item matrix
Recommender Systems: Beyond the user-item matrixRecommender Systems: Beyond the user-item matrix
Recommender Systems: Beyond the user-item matrix
 
Predicting Hospital Bills at Pre-admission
Predicting Hospital Bills at Pre-admissionPredicting Hospital Bills at Pre-admission
Predicting Hospital Bills at Pre-admission
 
OLX Group Prod Tech 2019 Keynote: Asia's Tech Giants
OLX Group Prod Tech 2019 Keynote: Asia's Tech GiantsOLX Group Prod Tech 2019 Keynote: Asia's Tech Giants
OLX Group Prod Tech 2019 Keynote: Asia's Tech Giants
 
Data Science Challenges and Impact at Lazada (Big Data and Analytics Innovati...
Data Science Challenges and Impact at Lazada (Big Data and Analytics Innovati...Data Science Challenges and Impact at Lazada (Big Data and Analytics Innovati...
Data Science Challenges and Impact at Lazada (Big Data and Analytics Innovati...
 
INSEAD Sharing on Lazada Data Science and my Journey
INSEAD Sharing on Lazada Data Science and my JourneyINSEAD Sharing on Lazada Data Science and my Journey
INSEAD Sharing on Lazada Data Science and my Journey
 
SMU BIA Sharing on Data Science
SMU BIA Sharing on Data ScienceSMU BIA Sharing on Data Science
SMU BIA Sharing on Data Science
 
Culture at Lazada Data Science
Culture at Lazada Data ScienceCulture at Lazada Data Science
Culture at Lazada Data Science
 
Competition Improves Performance: Only when Competition Form matches Goal Ori...
Competition Improves Performance: Only when Competition Form matches Goal Ori...Competition Improves Performance: Only when Competition Form matches Goal Ori...
Competition Improves Performance: Only when Competition Form matches Goal Ori...
 
How Lazada ranks products to improve customer experience and conversion
How Lazada ranks products to improve customer experience and conversionHow Lazada ranks products to improve customer experience and conversion
How Lazada ranks products to improve customer experience and conversion
 
Sharing about my data science journey and what I do at Lazada
Sharing about my data science journey and what I do at LazadaSharing about my data science journey and what I do at Lazada
Sharing about my data science journey and what I do at Lazada
 
AXA x DSSG Meetup Sharing (Feb 2016)
AXA x DSSG Meetup Sharing (Feb 2016)AXA x DSSG Meetup Sharing (Feb 2016)
AXA x DSSG Meetup Sharing (Feb 2016)
 
Garuda Robotics x DataScience SG Meetup (Sep 2015)
Garuda Robotics x DataScience SG Meetup (Sep 2015)Garuda Robotics x DataScience SG Meetup (Sep 2015)
Garuda Robotics x DataScience SG Meetup (Sep 2015)
 
DataKind SG sharing of our first DataDive
DataKind SG sharing of our first DataDiveDataKind SG sharing of our first DataDive
DataKind SG sharing of our first DataDive
 
Social network analysis and growth recommendations for DataScience SG community
Social network analysis and growth recommendations for DataScience SG communitySocial network analysis and growth recommendations for DataScience SG community
Social network analysis and growth recommendations for DataScience SG community
 
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learnt
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learntKaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learnt
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learnt
 
Nielsen x DataScience SG Meetup (Apr 2015)
Nielsen x DataScience SG Meetup (Apr 2015)Nielsen x DataScience SG Meetup (Apr 2015)
Nielsen x DataScience SG Meetup (Apr 2015)
 
Statistical inference: Statistical Power, ANOVA, and Post Hoc tests
Statistical inference: Statistical Power, ANOVA, and Post Hoc testsStatistical inference: Statistical Power, ANOVA, and Post Hoc tests
Statistical inference: Statistical Power, ANOVA, and Post Hoc tests
 
Statistical inference: Hypothesis Testing and t-tests
Statistical inference: Hypothesis Testing and t-testsStatistical inference: Hypothesis Testing and t-tests
Statistical inference: Hypothesis Testing and t-tests
 
Statistical inference: Probability and Distribution
Statistical inference: Probability and DistributionStatistical inference: Probability and Distribution
Statistical inference: Probability and Distribution
 
A Study on the Relationship between Education and Income in the US
A Study on the Relationship between Education and Income in the USA Study on the Relationship between Education and Income in the US
A Study on the Relationship between Education and Income in the US
 

Recently uploaded

代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改
代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改
代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改atducpo
 
BabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptxBabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptxolyaivanovalion
 
RA-11058_IRR-COMPRESS Do 198 series of 1998
RA-11058_IRR-COMPRESS Do 198 series of 1998RA-11058_IRR-COMPRESS Do 198 series of 1998
RA-11058_IRR-COMPRESS Do 198 series of 1998YohFuh
 
定制英国白金汉大学毕业证（UCB毕业证书） 成绩单原版一比一
定制英国白金汉大学毕业证（UCB毕业证书） 成绩单原版一比一定制英国白金汉大学毕业证（UCB毕业证书） 成绩单原版一比一
定制英国白金汉大学毕业证（UCB毕业证书） 成绩单原版一比一ffjhghh
 
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% SecureCall me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% SecurePooja Nehwal
 
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...Suhani Kapoor
 
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service Bhilai
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service BhilaiLow Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service Bhilai
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service BhilaiSuhani Kapoor
 
Industrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfIndustrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfLars Albertsson
 
Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusTimothy Spann
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data 2023Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data 2023ymrp368
 
April 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's AnalysisApril 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's Analysismanisha194592
 
FESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfFESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfMarinCaroMartnezBerg
 
VidaXL dropshipping via API with DroFx.pptx
VidaXL dropshipping via API with DroFx.pptxVidaXL dropshipping via API with DroFx.pptx
VidaXL dropshipping via API with DroFx.pptxolyaivanovalion
 
Mature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxMature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxolyaivanovalion
 
Introduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptxIntroduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptxfirstjob4
 
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service Amravati
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service AmravatiVIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service Amravati
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service AmravatiSuhani Kapoor
 
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Callshivangimorya083
 
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfMarket Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfRachmat Ramadhan H
 
Unveiling Insights: The Role of a Data Analyst
Unveiling Insights: The Role of a Data AnalystUnveiling Insights: The Role of a Data Analyst
Unveiling Insights: The Role of a Data AnalystSamantha Rae Coolbeth
 

Recently uploaded (20)

代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改
代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改
代办国外大学文凭《原版美国UCLA文凭证书》加州大学洛杉矶分校毕业证制作成绩单修改
 
BabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptxBabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptx
 
RA-11058_IRR-COMPRESS Do 198 series of 1998
RA-11058_IRR-COMPRESS Do 198 series of 1998RA-11058_IRR-COMPRESS Do 198 series of 1998
RA-11058_IRR-COMPRESS Do 198 series of 1998
 
定制英国白金汉大学毕业证（UCB毕业证书） 成绩单原版一比一
定制英国白金汉大学毕业证（UCB毕业证书） 成绩单原版一比一定制英国白金汉大学毕业证（UCB毕业证书） 成绩单原版一比一
定制英国白金汉大学毕业证（UCB毕业证书） 成绩单原版一比一
 
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% SecureCall me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure
 
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
VIP High Profile Call Girls Amravati Aarushi 8250192130 Independent Escort Se...
 
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service Bhilai
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service BhilaiLow Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service Bhilai
Low Rate Call Girls Bhilai Anika 8250192130 Independent Escort Service Bhilai
 
Industrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfIndustrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdf
 
Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and Milvus
 
Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data 2023Data-Analysis for Chicago Crime Data 2023
Data-Analysis for Chicago Crime Data 2023
 
April 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's AnalysisApril 2024 - Crypto Market Report's Analysis
April 2024 - Crypto Market Report's Analysis
 
FESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdfFESE Capital Markets Fact Sheet 2024 Q1.pdf
FESE Capital Markets Fact Sheet 2024 Q1.pdf
 
VidaXL dropshipping via API with DroFx.pptx
VidaXL dropshipping via API with DroFx.pptxVidaXL dropshipping via API with DroFx.pptx
VidaXL dropshipping via API with DroFx.pptx
 
Sampling (random) method and Non random.ppt
Sampling (random) method and Non random.pptSampling (random) method and Non random.ppt
Sampling (random) method and Non random.ppt
 
Mature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptxMature dropshipping via API with DroFx.pptx
Mature dropshipping via API with DroFx.pptx
 
Introduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptxIntroduction-to-Machine-Learning (1).pptx
Introduction-to-Machine-Learning (1).pptx
 
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service Amravati
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service AmravatiVIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service Amravati
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service Amravati
 
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
꧁❤ Greater Noida Call Girls Delhi ❤꧂ 9711199171 ☎️ Hard And Sexy Vip Call
 
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfMarket Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
 
Unveiling Insights: The Role of a Data Analyst
Unveiling Insights: The Role of a Data AnalystUnveiling Insights: The Role of a Data Analyst
Unveiling Insights: The Role of a Data Analyst
 

System design for recommendations and search