1. PRO6268 – Injecting AI into Applications to Make Them Smarter
Hi everyone, today we are going to be discussing applications of AI and data science for business use cases, and some of the challenges that come along with that pursuit.
PRO6268 – Injecting AI into Applications to Make Them Smarter
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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, timing, and pricing of any features or functionality described for Oracle’s products may change and remains at the sole discretion of Oracle Corporation.
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4. Speaking today Elena Albright Principal Product Manager,
Oracle Data Science Platform
PRO6268 – Injecting AI into Applications to Make Them Smarter
This session takes a deep dive into how Oracle’s data science platform helps enterprise data scientists collaborate on building, training, and deploying models on Oracle Cloud. Explore the platform's ease of use, project-based workflows, and other features that help data scientists be more productive in helping application developers and business analysts consume more artificial intelligence and machine learning. In addition learn how the platform integrates with the various IaaS and PaaS offerings in Oracle Cloud to enable powerful and scalable data science workflows.
My name is Elena Albright and I am a Principal Product Manager with the Oracle Data Science Platform.
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5. Today’s Agenda 1
Driving value with AI for your business What are the challenges? How a data science platform can unblock your organization Demonstration Q&A 2
Today’s agenda 3 4 5
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6. Today’s Agenda 1
Driving value with AI for your business What are the challenges? How a data science platform can unblock your organization Demonstration Q&A 2
We’ll discuss how to drive value with AI for your business 3 4 5
Confidential – Oracle Internal/Restricted/Highly Restricted

7. Today’s Agenda 1
Driving value with AI for your business What are the challenges? How a data science platform can unblock your organization Demonstration Q&A 2
Some of the challenges of doing data science in an enterprise organization 3 4 5
Confidential – Oracle Internal/Restricted/Highly Restricted

8. Today’s Agenda 1
Driving value with AI for your business What are the challenges? How a data science platform can unblock your organization Demonstration Q&A 2
How a data science platform can help out 3 4 5
Confidential – Oracle Internal/Restricted/Highly Restricted

9. Today’s Agenda 1
Driving value with AI for your business What are the challenges? How a data science platform can unblock your organization Demonstration Q&A 2
I’ll show you a demonstration of the Oracle Data Science Platform 3 4 5
Confidential – Oracle Internal/Restricted/Highly Restricted

10. Today’s Agenda 1
Driving value with AI for your business What are the challenges? How a data science platform can unblock your organization Demonstration Q&A 2
And finally, I’ll be available for questions after the session 3 4 5
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11. But first, let’s do a terminology check
Data Science, Artificial Intelligence, Machine Learning, Deep Learning… am I doing it?
Artificial Intelligence: Systems or machines that mimic human intelligence - a catch-all term for applications that perform complex tasks that once required human input.
Data Science: An interdisciplinary field that combines statistics with computer science concepts like machine learning and artificial intelligence to extract insights from big data.
Before we dive in, I want to set the stage of exactly what AI is – You’ve probably heard of all of these terms before – data science, artificial intelligence, machine learning, deep learning – but you may be wondering what you are actually doing already within your organization, or you may be considering building out teams to do this work – so we’ll set some definitions.
Data science is an interdisciplinary field that encompasses statistics as well as machine learning and deep learning techniques
Artificial intelligence is a catch-all term for applications that can perform complex tasks that once required human input.
Machine learning is a branch of artificial intelligence focused on building systems that “learn”
Deep learning is an ML technique that uses the concept of a biological brain to do complex processing.
Machine Learning: A branch of artificial intelligence focused on building systems that “learn” — or improve performance — based on the data they consume.
Deep Learning: An ML technique loosely based on the structure of a nervous system. Interconnected layers perform operations before passing the result to the next.

12. How do these fields relate to each other?
The overlap between artificial intelligence and data science is a significant one. Nearly everything under the artificial intelligence umbrella can be actively leveraged today by a data scientist to deliver business value.
You may have noticed that a lot of those definitions used the other terms in their definitions. So how are these fields related?
We can see that the overlap between AI and data science is significant.
Nearly everything under the artificial intelligence umbrella can be actively leveraged today by a data scientist to deliver business value.

13. How do these fields relate to each other?
The overlap between artificial intelligence and data science is a significant one. Nearly everything under the artificial intelligence umbrella can be actively leveraged today by a data scientist to deliver business value. So, if you have data scientists on staff, you probably have the human resources to inject AI into your business applications.
So, if you have data scientists on staff, you probably have the human resources to use AI in many applications in your business.
We often say that data scientists are the engineers of digital transformation for modern organizations

14. How can AI be used in my business applications?
We are going to zero-in on use cases where a predictive model could drive better outcomes, improving customer satisfaction, efficiency, or even revenue.
For today’s discussion, we are going to zero-in on predictive models that could be used to drive better business outcomes in terms of customer satisfaction, efficiency, or even revenue.
You’ll see these fall into the machine learning category.

15. What are some applications of ML in my business?
The applications of AI in your business cross every team and vertical. Some examples:
Recommendation Engines
Chatbots
Lifetime Value Models
Image Recognition
Applications of machine learning can be used not only in every vertical but also across departments and disciplines within an organization. From Chatbots to Image Recognition – you’ll find applications in Sales, Marketing, HR, Finance and more.
Our example today will be dynamic pricing of hotel rooms based on demand which will allow us to maximize revenue.
Speech Recognition
Fraud Detection
Dynamic Pricing
Customer Segmentation
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16. What’s holding organizations back?
SCARCITY OF SPECIALIZED SKILLS
Domain specific models, machine learning skills
DEVELOPMENT COMPLEXITY
Data management, non-standardized environments, ML frameworks, libraries, GPUs
COLLABORATION & WORKFLOW INEFFICIENCY
Data scientists, app developers, IT Admins, LOB Experts
But before we go to that example, let’s discuss what is holding organizations back from using ML across their lines of business.
There is an overall scarcity of talent with the specialized skills to do this work.
In organizations that do have the talent on staff, there is a lot of complexity around development. From data management, non-standardized environments, and specialized tools and hardware like GPU. It requires the full buy-in from the organization and significant investment.
Lastly, there are inefficiencies of collaboration and workflow. Line of business experts need to collaborate with data scientists, who, in turn, need to collaborate with IT admins and app developers
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17. What’s holding organizations back?
SCARCITY OF SPECIALIZED SKILLS
Domain specific models, machine learning skills
COLLABORATION & WORKFLOW INEFFICIENCY
Data scientists, app developers, IT Admins, LOB Experts
DEVELOPMENT COMPLEXITY
Data management, non-standardized environments, ML frameworks, libraries, GPUs
For today’s discussion, we are going to pay special attention to the interactions between data scientists and application developers who are at the center of actually getting ML models into production.
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18. The Data Scientist/App Developer Handoff
Inefficiencies between data scientists and app developers limit AI innovation
Traditional development workflows
With a data science platform
Key-person risk and fragmented model repositories
Delays and wasted translation efforts
Lack of model lineage and reproducibility
Latest version waiting for production
Slow testing and iteration cycles
Centralized model development and storage
There are many challenges to get ML models into production, powering business applications.
Traditional development workflows suffer from:
Key-person risk – if a data scientist is working on their laptop and they leave the organization, a model that they deployed to production could be left with no one to update or maintain it going forward
To this point, there is a lack of lineage and organizational transparency. Without this, there is no possibility of reproducing work at a later date.
When deploying models to production, many times, a data scientist will develop a model in their language of choice like Python or R and then hand it off to the application developer who may translate the model to the language of the application such as Java. In some cases, this is necessary because of the deployment architecture or performance requirements, but in many cases, this is an unnecessary effort
Because of this handoff process and possible translation, oftentimes, there are gaps between when the latest and greatest model is available and when it can actually be used in production.
Testing and iteration cycles are slow and cumbersome.
With a data science platform, you can overcome these challenges by having
A centralized source of truth for all model development and storage
Self-documented projects and consistent dependency management, enabling repeatable analyses and results.
Models can be deployed as REST APIs with the endpoint handed off
This improves the testing and iteration cycles which can be wholly owned by the data scientist who can retrain their models and instantly update production.
Self-documenting projects, dependency mgmt
Deploy models as REST APIs, handoff endpoint
Retrain models and instantly update production
Testing and iteration wholly-owned by data scientist
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19. Centralized model development and storage
Features:
Projects
Projects organize team’s work around answering business questions. Many stakeholders can be invited to collaborate.
Some features of a data science platform that improve centralization and collaboration are
Projects where you can organize work and invite collaborators
Unified tooling that is standardized across the organization
Model tracking that makes model storage and retrieval standardized and documented
Unified tooling
With all teammates using a consistent toolset, key-person risk is minimized. Teammates can understand and pick up where another left off.
Model tracking
A centralized storage and consistent retrieval paradigm mitigates the risk of losing track of model source and one-off maintenance.
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20. Self-documenting projects, dependency management
Features:
Activity tracking
Projects become self-documenting with activity tracking. Data scientists and managers can dig into lineage and retrace teammates’ steps.
In order to have transparency and consistent, repeatable work, a data science platform offers
Activity tracking for project lineage
Environments for consistent package and library versions. A lot of times, even a simple version bump to a package can cause a model to fail or get different results.
And Version control – which is a software engineering best practice – All project assets should be versioned so that ultimately, work is reproducible by teammates or later in time.
Environments
Build, train, and deploy models in consistent environments, minimizing errors and mismatches.
Version control
Versioning everything – code, data, models, environments – makes analyses and models reproducible.
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21. Deploy models as REST APIs, handoff endpoint
Features:
Deployment workflows
Deploy a model as a REST API in just a few clicks. Putting deployment in the hands of data scientists eliminates inefficiencies.
A data science platform should make it easy to deploy models, offering
Deployment workflows to deploy via an SDK or UI
Elimination of the translation inefficiencies by enabling data scientists to deploy in their preferred language
And ultimately, the API endpoint should be handed off to the app developer so they can embed it in the application in the language of the application
Models remain in lingua data science
Translating from the language of the modeler to the application may improve performance but is unnecessary for many use cases.
Endpoint translation
Handoff the endpoint to the app developer in multiple languages like Node, Python, or cURL.
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22. Retrain models and instantly update production
Features:
Model versioning
Run multiple versions side-by-side. Set up A/B or champion/challenger experiments to statistically test performance.
Models should be on a retraining cycle to keep them fresh and updated in production. You’ll need
Model versioning, which enables models to run side-by-side in A/B test scenarios which let you understand the statistical performance differences between model approaches
Automated retraining pipelines which allow data scientists to go out to lunch or on vacation, knowing that their models are constantly updated
Static endpoints that allow you to swap out model versions in production on the fly
Retraining pipelines
Setting up an automated pipeline to retrain and redeploy the model helps to prevent bad predictions from thwarting your AI efforts.
Static endpoints
Point your application to /latest or /production to always send the request to the most updated version.
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23. Testing and iteration wholly-owned by data scientist
Features:
Testing models in staging
Data scientists can iterate quickly and test deployed models in staging prior to pushing to production.
Lastly, a data science platform should enable testing and iteration to be wholly-owned by the data scientist who is going to understand the problem space and the methodology best, with:
Model testing in staging prior to pushing to production
Model evaluation and interpretation techniques that help the data scientist understand why their model is doing what it is doing
And monitoring and health checking to make sure it’s always performing well – both operationally and in terms of accuracy
Model evaluation and interpretation
Keeping the iteration loop tight enables data scientists to keep a close eye on evaluation metrics and ensure predictions are ethical.
Model monitoring and health checking
When model performance or health diminishes, the data scientist can be a first responder to make crucial updates.
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24. The Oracle Data Science Platform
Collaborate in Projects
Now, I’ll do a brief demonstration and show you some of these features in the Oracle Data Science Platform.
Deploy a model as an API
Easily handoff models from data scientist to developer

25. Oracle Data Science Platform Key Components & Benefits
Core Benefits:
Oracle Data Science Platform
Collaborative
Projects
Notebooks
Open Source Languages & Libraries
Model
Build & Train
Project driven UI enables teams to easily work together on end-to-end modeling workflows with self-service access to data and resources
Model Deployment
We just saw how the Oracle Data Science Platform enables data scientists to deploy ML models to be used in applications, improving business outcomes.
The platform offers projects, open source tools, and software engineering best practices to enable data scientists to build, train, deploy, and manage models, all using Oracle Cloud Infrastructure.
Version Control
Use Case Templates
Access Controls & Security
Model Monitoring
Integrated
Oracle IaaS
Support for latest open source tools, version control, and tight integration with OCI and Oracle Big Data Platform
Self-Service Scalable Compute (OCI)
Object Store
Catalog
Data Lake
Streaming
Autonomous Data Warehouse
Enterprise-Grade
A fully managed platform built to meet the needs of the modern enterprise
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26. Questions?
Any questions? Come see me on the side after!

27. Thanks for joining!