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Artificial Intelligence and Machine Learning with the Oracle Data Science Cloud

Artificial Intelligence and Machine Learning with the Oracle Data Science Cloud

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Artificial Intelligence and Machine Learning with the Oracle Data Science Cloud

  1. 1. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Artificial Intelligence and Machine Learning with the Oracle Data Science Cloud Juarez Barbosa Principal Developer Advocate – Oracle EMEA
  2. 2. Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
  3. 3. 4 Data Science Overview
  4. 4. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | What is Data Science? 5 • Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. • A data scientist’s responsibilities include preparing data, exploring and visualizing data, and building models using programming languages like Python or R. • Data science includes methods and techniques such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). ARTIFICIAL INTELLIGENCE A program that can sense, reason, act, and adapt MACHINE LEARNING Algorithms whose performance improves as they are exposed to more data over time DEEP LEARNING Subset of ML in which multi-layered neural networks learn from vast amounts of data DATA SCIENCE
  5. 5. 61%Organizations identify machine learning as the most significant data initiative for next year** $57.6BEstimated spend on AI and machine learning by 2021 compared to $12B in 2017* Market Opportunity 5 $3.5-5.8TPotential annual AI-derived business value across 19 industries*** * Source: Deloitte, Machine learning: things are getting intense, 2017 ** Source: 2018 Outlook: Machine Learning and Artificial Intelligence, A Survey of 1,600+ Data Professionals. MEMSQL *** Source: Notes from the AI frontier: Applications and value of deep learning, Mckinsey Global Institute
  6. 6. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | Data Science is Critical in Every Industry and Function 7 Government Retail Technology Automotive Consumer Goods Media Communications Healthcare Gaming Travel Energy Finance & Insurance HR Finance Sales Tech and Product Marketing Support DATA SCIENCE
  7. 7. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | Sample Data Science Use Cases 8 Marketing Response Models Scheduled Jobs Customer churn APIs Text sentiment analysis Reports Lifetime Value Apps Computer vision and image tagging Apps Transactional data ETL Scheduled Jobs Forecasting Reports Risk management with machine learning APIs Recommendation engines APIs Data discovery and auditing Reports
  8. 8. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | Data Science is Critical to Digital Transformation 9 Build and enhance products and services Enable more efficient operations and processes Create new channels and business models
  9. 9. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | What’s Holding Companies Back? 10 Data Scientists Cannot work efficiently App Developers Cannot access usable ML IT Admins Too much time on support • Lengthy waits for resources and data • Difficulty collaborating with teammates • Long delays of days or weeks to deploy work • Many tools to manage • No access to well-trained models • Access points not flexible for deployment in all scenarios • Scalability of deployment left out to the app developer Business Executives Do not see full ROI • Growing list of open source tools • Continually building and updating environments • Limited standardization across workflows • No transparency into work • No model integration with decision making systems • Unable to access or share outputs • Difficult to collaborate with data scientists Despite the promise of data science, and huge investments in data science teams, inefficient workflows are holding companies back from realizing the full potential of machine learning.
  10. 10. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | What’s the Impact? 11 Organizations are losing out on millions of dollars in profits and cost savings from operations that should be driven by machine learning
  11. 11. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | Enterprise Data Science Requires a Comprehensive Platform to Simplify Operations and Deploy Models at Scale 12 A robust, easy-to-use data science platform removes barriers to deploying valuable machine learning models in production by: • Accelerating use of proper open source tools, frameworks, and infrastructure • Overcoming resource bottlenecks by making work more self-service through a collaborative platform • Quickly leveraging predictive analytics to drive positive business outcomes Manage data and tools Collaborate securely Power business Work in standardized environments
  12. 12. 13 Oracle Solution
  13. 13. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | Oracle Data Science Cloud 14 The Oracle Data Science Cloud enables data science teams to organize their work, easily access data and computing resources, and build, train, deploy, and manage models on the Oracle Cloud. The Oracle Data Science Cloud makes data science teams more productive, and enables them to deploy more work faster to power their organizations with machine learning. What is It? What’s the Value? *Final name pending legal review and approval
  14. 14. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | Oracle Data Science Cloud Workflow 15 Reproducibility Data Versioning Code Versioning Model Versioning Environment Management Model Deployment Operationalize Models as Scalable APIs Model Management Monitor and Optimize Model Performance Data Exploration Collaborative Data Analysis / Feature Engineering Model Build and Train with Open Source Frameworks Collaborators ∙ Data Scientists ∙ Business Stakeholders ∙ App Developers ∙ IT Admins
  15. 15. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | End-to-end platform for enterprise data scientists Oracle Data Science Cloud Core Capabilities 16 • Data science workflow: Collaboration for enterprise data science teams in projects • Model building and training*: Python development in Jupyter notebooks • Model deployment: Deploy models as APIs, serve predictions in real-time • Version control: External Git Provider required for files • Access to open-source: Curated sets of packages for data science use cases • Access to compute: Self-service access to spin up containers on OKE Cluster of OCI VMs (CPU only) • Access to data: Oracle Object Store * Model training in single Jupyter container with reserved CPU/memory (non-distributed over multiple containers)
  16. 16. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | Oracle Data Science Cloud Key Components & Benefits 17 Collaborative Project driven UI enables teams to easily work together on end-to-end modeling workflows with self-service access to data and resources Integrated Support for latest open source tools, version control, and tight integration with OCI and Oracle Big Data Platform Enterprise-Grade A fully managed platform built to meet the needs of the modern enterprise Core Benefits: Oracle Data Science Cloud Oracle PaaS & IaaS Projects Notebooks Open Source Languages & Libraries Version Control Use Case Templates Model Build & Train Self-Service Scalable Compute (OCI) Object Store Catalog Data Lake Streaming Autonomous Data Warehouse Model Deployment Model Monitoring Access Controls & Security
  17. 17. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | Oracle Data Science Cloud is COLLABORATIVE 18 ● Project-driven UI simplifies data science operations and enables teams to work together ● Built-in version control ensures all data, code, and models can be tracked and reproduced ● Granular access controls enable managers or admins to control who has access to projects and data ● Support for teams to collaboratively build, train, deploy, and manage models from a central workspace
  18. 18. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | Oracle Data Science Cloud is INTEGRATED 19 ● Platform supports a wide range of open source tools, libraries, and languages to tackle different use cases ● Native support for most popular version control providers (Github, Gitlab, and Bitbucket) ensures all work is synced across the platform ● Tight integration with OCI and Oracle Big Data Platform provides data scientists with self-service access to scalable compute and the data they need to get to work quickly Data Analysis, ML, AI Version ControlTools & Languages Visualization Use the Best of Open Source Easily Access Data and Compute Streams Batch Data Warehouse NoSQL Databases Self-Service Scalable Compute (OCI) Object Store Data Lake Spark Catalog
  19. 19. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | Oracle Data Science Cloud is ENTERPRISE-GRADE 20 Fully Managed Highly Available • Fully managed platform built on Kubernetes • Platform is highly available — ensuring anytime, anywhere availability and access • Support for large teams with containerized workloads, preventing resource contention on a scalable cluster • Integration with Oracle IDCS enables robust access control management • Designed to leverage high performance Oracle Cloud Infrastructure Scalable Secure AD 1 AD 2 AD 3 Oracle PaaS Oracle IDCS
  20. 20. Copyright © 2018 , Oracle and/or its affiliates. All rights reserved. | Innovative approach to ModelOps/Management: Comprehensive model management from data acquisition to model retirement Oracle Data Science Cloud Future Vision: Key Themes 21 Automation across lifecycle: Automation around model development (AutoML) and post deployment model management Leading data science ecosystem: AI APIs, data stores, use-case playbooks, AI App integrations, and more Infinitely scalable “horsepower”: Deep integration with Oracle SaaS, PaaS, IaaS to tackle any use case at any scale with any data Best-in-class modelops/management, scalable “horsepower,” automation, and a leading data science ecosystem

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  • bigfoot26

    Mar. 25, 2020

Artificial Intelligence and Machine Learning with the Oracle Data Science Cloud

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