Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

Webinar: Accelerate Data Science with Fusion 5.1

Presented by Justin Sears, VP Product Marketing, Lucidworks
and Sanket Shahane, Product Manager, AI

  • Soyez le premier à commenter

Webinar: Accelerate Data Science with Fusion 5.1

  1. 1. 1 Introducing Fusion 5.1 W I T H D ATA S C I E N C E T O O L K I T I N T E G R AT I O N : T E N S O R F L O W, S PA C Y & S C I K I T - L E A R N Justin Sears, VP of Product Marketing Sanket Shahane, Data Scientist & Product Manager for Artificial Intelligence
  2. 2. 2 Today’s Speakers Justin Sears VP of Product Marketing Sanket Shahane Product Manager - Artificial Intelligence
  3. 3. 3 Agenda • Introduction to Fusion & Version 5.1 • Fusion’s Jupyter Notebook Integration – Architecture, Scope & Purpose – Demo: Reading Data, Writing Data, SQL Aggregation • Deploying ML Models with Seldon Core – Architecture, Scope & Purpose – Demo: Deploying a Custom Model • Q&A
  4. 4. 4 Fusion Overview I N T R O D U C I N G F U S I O N 5 . 1
  5. 5. 5 How We Do It Fusion leverages existing knowledge & maximizes the velocity of data discovery Understanding Content Understanding Users Delivering at Scale
  6. 6. 6 FILTER VISUALIZATION ACTIVITY CONTENT INDEX NATURAL LANGUAGE BOOSTED RESULTS MACHINE LEARNING QUERY RULE MATCHING USER SIGNALS FACET, TOPIC & CLUSTER D ATA Human Generated System Generated Application Generated S O L U T I O N Digital Workplace Digital Commerce
  7. 7. 7 Advanced connectors and ML enrichment, delivered by intuitive applications, deployed on-prem, in the cloud or as a PaaS. D ATA Any format, any platform S O L U T I O N Personalized insights for each user STORAGE & SEARCH INTENT PREDICTIO N APP CREATION DATA INGEST & PREP F U S I O N P L AT F O R M Human Generated System Generated Application Generated Digital Workplace Digital Commerce
  8. 8. 8 STORAGE & SEARCH INTENT PREDICTION APP CREATIONDATA INGEST & PREP NLP: NER, phrases, POS Document classification Anomaly detection Clustering Topic detection Search engine & data processing Connectors ETL pipelines Scheduling & alerting SQL engine Rules engine Query pipelines Query intent detector Automatic relevancy Signals & query analytics Recommenders A/B testing Modular components Stateless architecture User-focused experience Geospatial mapping Results preview Rapid prototyping S C A L A B L E O P E R AT I O N S SECURITYCDCRCLOUDSCALABLEEXTENSIBLE
  9. 9. 99 C L O U D - N AT I V E , M I C R O S E R V I C E S A R C H I T E C T U R E O R C H E S T R AT E D B Y K U B E R N E T E S A U T O S C A L I N G P O L I C I E S D Y N A M I C A L LY M A N A G E R E S O U R C E S N AT I V E S U P P O R T F O R P Y T H O N M L M O D E L S E A S Y I N T E G R AT I O N W I T H D ATA S C I E N C E T O O L S E . G . T E N S O R F L O W, S C I K I T- L E A R N , S PA C Y, J U P Y T E R N O T E B O O K S S PA R K S T R E A M I N G F O R S I G N A L S F U S I O N 5 . 1 – C LO U D N AT I V E & D ATA S C I E N C E R E A DY
  10. 10. 10 • Workplace apps with a consumer-like experience • Contextual, personalized discovery of insights • Successful cross-functional adoption of applied ML • Employee engagement, collaboration & retention The Hyper- Personalized Workplace
  11. 11. 11 • Real-time, personalized, relevant search results • Proactive recommendations with ML that work on Day 1 • Machine intelligence at scale, with merchandisers in charge • A trove of customer insight to inform strategic decisions Hyper- Personalized Commerce
  12. 12. 12 Jupyter Notebook Integration R E A D I N G D ATA , W R I T I N G D ATA & S Q L A G G R E G AT I O N
  13. 13. 13 Architecture, Scope & Purpose Jupyter Notebook runs as its own independent service Deployed on the analytics node pool in the Kube deployment
  14. 14. 14 Architecture, Scope & Purpose Jupyter Notebook Scope • Interacts with Fusion, Solr Collections, and the outside world (access permitting) • Current scope limited to dev and exploration (should not be used for production workloads. • Fusion proxy authenticated endpoint • Supports Scala, Python, and other language kernels • Hosts Spark for manipulating heavy data
  15. 15. 15 Architecture, Scope & Purpose Jupyter Notebook Use Cases • Explore data from Solr • Load data into Solr from other storage sources • Export data from Solr to other storage sources • Run Fusion SQL • Dev and Test custom SQL Aggregations
  16. 16. 1616 Demo
  17. 17. 17 Sample usage
  18. 18. 18 Sample usage
  19. 19. 19 Sample usage
  20. 20. 20 Sample usage
  21. 21. 21 Sample usage
  22. 22. 22 Sample usage
  23. 23. 23 Deploying ML Models with Seldon Core W O R K I N G W I T H C U S T O M M O D E L S
  24. 24. 24 What is Data Science Toolkit Integration? Data Science Toolkit Integration is a model service that provides seamless integration with Fusion’s Query and Index Pipelines. It adds intelligence for processing incoming queries and documents. Fusion integrates with Seldon Core, an open source framework for model deployment management. Objectives • Streamline production of search-focused ML models • Reduce data science teams dependencies on DevOps teams and vice versa • Increase productivity, drive experimentation to fail fast, iterate, and improve Process Train model Build and Publish Docker Image Deploy in Fusion Integrate
  25. 25. 25 Workflow Development and Publishing 1. Develop ML model using choice of framework. 2. Persist model and other objects 3. Create docker image consisting of python packages, prediction class, and model objects 4. Publish to a docker repository 5. Deploy in Fusion using template job
  26. 26. 26 Usage – Query and Index Pipelines Query Pipelines • Process user queries • Multiple stages for specific purposes • Return results to the user Index Pipelines • Process documents • Multiple stages for specific purposes • Store documents to Solr for Query Pipelines
  27. 27. 27 Usage ML Models are immutable Docker images deployed and scaled independently.
  28. 28. 28 Usage Seldon Core balances the workload between model replicas.
  29. 29. 29 Usage ML Service is a proxy and keeps track of models available in Fusion.
  30. 30. 30 Usage Machine Learning stages interact with ML Service and Seldon core via GRPC protocol.
  31. 31. 31 Usage ML Models are immutable Docker images deployed and scaled independently. Seldon Core balances the workload between model replicas ML Service is a proxy and keeps track of models available in Fusion. Machine Learning stages interact with ML Service and Seldon core via GRPC protocol.
  32. 32. 3232 Demo
  33. 33. 33 Learn More Read the blog: Fusion 5.1 Is Here: Faster Deployment of Data Science and Innovation https://lucidworks.com/post/latest-fusion-release/ Test drive Fusion on your own! https://lucidworks.com/try/ Try in the Cloud Try in our Sandbox (Github) Contact us: https://lucidworks.com/contact/ Check out these resources to learn more about Fusion 5.1
  34. 34. 3434 Questions & Answers
  35. 35. 35 THANK YOU

×