Blog Archives - Vertice

What is Oracle AIDP for Life Sciences? A Plain-English Guide for Pharma, MedTech and Clinical Research Leaders

Conor Kehoe — Tue, 19 May 2026 11:52:30 +0000

Author: John Fitts

The 30-second answer

Oracle AI Data Platform (AIDP) is a cloud-based platform that brings enterprise data, generative AI models, and AI agents together in a single, governed environment. When applied to life sciences, it provides a foundation for organizations across pharmaceuticals, medical devices, clinical research, and healthcare to unify their data and apply AI to research, clinical trials, post-market safety, and commercialization activities.

Oracle launched the general AI Data Platform in October 2025 and announced Oracle Life Sciences AI Data Platform in January 2026. The latter is pre-populated with more than 129 million de-identified longitudinal Oracle Health Real-World Data (OHRWD) records, alongside the customer’s own data and any third-party sources they choose to bring in. Throughout this article, “AIDP” refers to the underlying platform. Where the distinction matters, the Life Sciences variant is named directly.

Why does the Oracle AIDP for Life Sciences conversation matter now?

Oracle AIDP for Life Sciences is designed to help organizations accelerate drug development, run faster and smarter clinical trials, monitor post-market safety, and generate the evidence needed for commercial and regulatory decisions. The outcomes it targets are measurable: shorter time to insight, earlier identification of disease, deeper understanding of patient populations, and reduced operational cost.

Delivering against those outcomes depends on one thing first. Not the AI, but the data underneath it.

This was the central message from Oracle leadership at Oracle AI World 2025. Speaking on the impact of AI on healthcare and life sciences, Seema Verma, Executive Vice President and General Manager of Oracle Health and Life Sciences, identified data as the defining issue.

“One of the big problems that is driving those inefficiencies is data and the lack of data or too much data or understanding what the data means,” she said. “And so, this is like the perfect use case for AI.”

She went further on the relationship between data and AI: “It’s not enough to just be able to use AI. You have to be able to, first of all, bring all the data together, and you have to make sense of the data, and you have to make sure the data is clean so that the AI can work.”

The same logic applies across pharmaceuticals, medical devices, and clinical research. Until the underlying data is unified and governed, AI projects tend to stall before they reach production. That is the problem AIDP for Life Sciences is built to address.

What is Oracle AI Data Platform?

Oracle AI Data Platform combines four capabilities in a single environment:

A unified data foundation

AIDP provides a lakehouse architecture using open formats such as Delta Lake and Iceberg, designed to eliminate data duplication and bring structured and unstructured data into one place. A unified catalog provides governance across all data and AI assets.

Built-in generative AI and agentic capability

The platform includes integrated access to generative AI models, vector indexing, retrieval-augmented generation (RAG), and tools for building, deploying, and managing AI agents. Oracle’s Agent Flows allows business users to interact with multiple agents through a single interface, and the platform supports open standards including Agent2Agent (A2A) and Model Context Protocol (MCP).

Integration with the wider Oracle ecosystem

AIDP connects to Oracle Health applications and existing enterprise databases through Zero-ETL and Zero Copy capabilities. It also supports multicloud and hybrid environments, with the ability to process data from on-premises and edge sources.

Enterprise-grade security and governance

The platform is built on Oracle Cloud Infrastructure (OCI) with the security, compliance, and data governance controls required for regulated industries.

How does Oracle AIDP for Life Sciences build on Oracle AIDP

The Oracle Life Sciences AI Data Platform is a version of AIDP shaped for the data, workflows, and regulatory environment of life sciences organizations. Three things distinguish it from a generic implementation.

Pre-loaded real-world data

The platform provides access to Oracle Health Real-World Data, a dataset of more than 129 million de-identified longitudinal patient records sourced through the Oracle Learning Health Network. The data is delivered in both the Oracle Core Data Model and the Observational Medical Outcomes Partnership (OMOP) Common Data Model, with mapping to standard clinical ontologies such as ICD-10 and SNOMED. De-identification follows the Expert Determination methodology defined in the HIPAA Privacy Rule.

Industry-specific tooling

Users access the platform through Oracle Autonomous Data Warehouse for SQL-based analysis and cohort identification, Oracle Cloud Infrastructure Data Science for notebook-based modeling in Python, R, and Spark, and Oracle Analytics Cloud for visualization and reporting. OCI Object Storage supports unstructured and semi-structured data ingestion.

Service variants for different organization types

Oracle offers the platform in distinct service configurations, including a Pharma Cloud Service and a Research Institutes Cloud Service. Each is sized differently and provisioned with the OCI services appropriate to that use.

It is worth noting one specific limitation: the OHRWD Patient Dataset and the insights generated from it are not regulatory grade and are not approved for use in regulatory submissions. The platform supports the workflows around regulatory activity, but the dataset itself is intended for research, not filing.

What does this mean for Life Sciences?

Customers can start with pre-curated healthcare datasets, built-in AI tooling, existing healthcare ontologies and fully managed infrastructure. This means faster delivery of proof-of-concept timelines, analytics deployment, cohort analysis, and AI experimentation.

Because the data is centralized and standardized, organizations can reuse the same platform across many domains. Once the governed healthcare data foundation exists, new applications become composable, AI becomes reusable and experimentation becomes faster.

Oracle AIDP for Life Sciences use cases

Oracle’s announcement of the Life Sciences AI Data Platform identified a number of use cases the platform is designed to support. Each represents a distinct business outcome rather than a technical capability.

Label expansion

Organizations can use the platform to identify potential indications for existing therapies by analyzing real-world patient data alongside their own clinical evidence, supporting commercial and regulatory strategy for approved products.

Health Economics and Outcomes Research (HEOR)

The platform supports population-level analysis to evaluate the cost, effectiveness, and real-world impact of treatments, work that increasingly determines reimbursement decisions and market access.

Synthetic control arms

Using real-world data as a comparator, organizations can construct synthetic control arms for clinical trials, reducing the need for placebo cohorts in certain study designs and shortening trial timelines.

Post-market safety monitoring

AI agents can monitor disparate sources of safety data, surfacing signals earlier than manual processes typically allow.

Regulatory submission support

While the underlying patient dataset is not itself regulatory grade, the platform supports the analysis, evidence generation, and workflow management that contribute to submissions.

In addition to these defined use cases, Oracle’s platform supports open-ended hypothesis generation. A researcher can ask a question in natural language, and AI agents will clarify intent, propose analyses for review, and act within user-defined guardrails, with full visibility into data lineage.

Oracle AIDP for Life Sciences Case Study: UCD Clinical Research Centre

We built an AI-powered patient companion application for asthma and chronic obstructive pulmonary disease (COPD) on Oracle AI Data Platform, in partnership with University College Dublin Clinical Research Centre (UCD CRC), the largest clinical research centre on the island of Ireland. The application was built on the underlying Oracle AI Data Platform rather than the Oracle Life Sciences AI Data Platform variant, which was announced subsequently. UCD CRC handled the clinical study design and managed the research requirements. We handled the technical delivery, including the lakehouse, the AI-elements, and the application itself.

The application captures the patient’s experience in their own voice, in the moment it happens, and uses retrieval-augmented generation to correlate that narrative with clinical and environmental data. The pilot is live, the partnership is ongoing, and we are now working with UCD CRC to explore further use cases beyond respiratory disease using the same architecture.

Watch the full case study

Is Oracle AIDP for Life Sciences right for your organization?

Two signals tell you that AIDP for Life Sciences is worth a serious look. First, you can name a specific use case tied to a measurable business outcome, for example, whether that is shortening trial reporting cycles, or expanding the indications for an approved therapy. Second, you have a baseline understanding of what data you hold, where it lives, and some level of governance around it. If both are in place, the conversation is a productive one. If either is missing, the right first step is foundational data work, not platform procurement.

A fuller breakdown of the readiness signals, the most common gap (data governance), and the framework we use to assess it is covered in our companion piece: Oracle AIDP Readiness: How to Tell If You’re Ready (and What to Do If You’re Not).

What to Do Next

To explore whether AIDP for Life Sciences fits your organization. Contact Us.

Frequently Asked Questions

What is Oracle AI Data Platform?

Oracle AI Data Platform (AIDP) is a cloud-based platform that connects generative AI models with enterprise data, applications, and workflows. It is built on Oracle Cloud Infrastructure, Oracle Autonomous AI Database, and OCI Generative AI service. Oracle announced general availability in 2025.

What is Oracle Life Sciences AI Data Platform?

A generative AI-enabled solution announced in January 2026, designed for pharmaceutical, medical device, research, and life sciences organizations. It supports use cases across research and development, clinical trials, post-market safety, and commercialization.

Oracle AI Data Platform vs Oracle Life Sciences AI Data Platform

The Original AI Data Platform (AIDP Workbench) and Life Sciences AI Data Platform which we refer to as a “flavours” (FAIDP would be another flavour) differ mainly in prebuilt content. AIDP Workbench is a blank canvas where everything needs to be built from the ground up. The variants, like Life Sciences AI Data Platform, include prebuilt aspects aligned to a specific industry or line of business, such as Oracle applications, KPIs, and agents, enabling faster time-to-value.

What is Oracle Health Real-World Data?

A dataset of more than 129 million de-identified longitudinal patient records sourced through the Oracle Learning Health Network. It is delivered in the Oracle Core Data Model and the OMOP Common Data Model, with mapping to ICD-10 and SNOMED. De-identification follows the Expert Determination methodology defined in HIPAA Privacy Rule § 164.514(b)(1).

What types of patient data are included?

Demographics, vitals, test results, medications, and encounter specifics including diagnosis, symptoms, and provider details. Not all fields are completed for all patients.

Can the data be used for regulatory submissions?

No. The Patient Dataset and the insights derived from it are not regulatory grade and are not approved for use in regulatory submissions.

Can the platform process genetic data?

No. The Cloud Service cannot currently receive or process genetic data.

What use cases does the platform support?

Oracle has identified label expansion, population-level Health Economics and Outcomes Research, synthetic control arm generation, post-market safety monitoring, and regulatory submission support. The platform also supports open-ended hypothesis generation through AI agents operating within user-defined guardrails.

What tools does the platform include?

Oracle Autonomous Data Warehouse for SQL-based analysis, OCI Data Science for notebook-based modelling in Python, R, and Spark, Oracle Analytics Cloud for visualization, and OCI Object Storage for unstructured data.

What service variants are available?

Oracle offers a Pharma Cloud Service pre-populated with the Patient Dataset, and a Research Institutes Cloud Service available as a Hosted Named User service with optional Patient Dataset Records. Each is sized and provisioned differently.

How does AIDP integrate with other Oracle products?

It connects to Oracle Cloud Infrastructure, Oracle Life Sciences AI Application Suite, Oracle Fusion Cloud SCM and Sales, and Oracle Health AI Application Suite. The general AIDP also supports Zero-ETL and Zero Copy connections to Fusion, NetSuite, and other enterprise applications across multicloud and hybrid environments.

What is an example of AIDP being used in clinical research?

University College Dublin Clinical Research Centre and Vertice built an AI-powered application for patients with asthma and COPD using Oracle AI Data Platform. Oracle Health and Baylor College of Medicine have also announced a collaboration to advance research into alcohol-related liver disease using the platform and Oracle Health Real-World Data.

How long does it take to implement Oracle AIDP for Life Sciences?

Any implementation is dependent on the business, functional and technical requirements. However, the typical pilot is a 4-week sprint where we create outcome use cases. This is followed by 4-week project phases where we create a portfolio of production-ready AI use cases that can be scaled across the organization. You attain ROI via in-house AI Innovation Library of working use cases to then derive your budget internally to productionize.

How should an organization choose an AIDP implementation partner?

Look for live, in-production AIDP implementations, named customer references you can speak to, an active Oracle partnership, and vertical experience in your sector. The right partner takes you from a defined business problem to a working use case in weeks, not quarters. Vertice was the first to implement Oracle AIDP at scale and has proven delivery in life sciences and clinical research.

About John Fitts

John Fitts is a senior business strategist with 25 years translating between the boardroom and the technology stack. As US Vice President for Vertice and CEO of Fairfax Intel, he partners with C-suite leaders on the question most AI and data programs skip: not how, but why.

connect@verticecloud.com

John Fitts

Senior VP North America

The post What is Oracle AIDP for Life Sciences? A Plain-English Guide for Pharma, MedTech and Clinical Research Leaders appeared first on Vertice.

Oracle AIDP Readiness: How to Tell If You’re Ready (and What to Do If You’re Not)

Conor Kehoe — Tue, 19 May 2026 11:22:11 +0000

Author: John Fitts

TLDR

You’re ready for Oracle AIDP when you can name a specific use case tied to a business outcome, and you have a baseline level of data maturity sufficient to act on that use case. The most common readiness gap is data governance, and the Data8 framework is a structured way to assess and close it. The lowest-risk way to test the platform is Vertice’s Pilot, Prototype, Project approach, which lets you prove value on representative data before any infrastructure or procurement commitment. And if it turns out you’re not ready yet, foundational data work is the right next investment, not because AIDP isn’t right for you, but because it’ll work better when you eventually deploy it. This guide is drawn from Vertice’s delivery experience, including our work with University College Dublin Clinical Research Centre

What Does Oracle AIDP Readiness Actually Mean?

Oracle AI Data Platform (AIDP) is Oracle’s unified environment for building, governing, and operationalizing AI and data workloads on enterprise data. Oracle AIDP readiness is your business’s ability to successfully use that platform to deliver reliable, scalable, and business-relevant outcomes.

How do you know you are ready for Oracle AIDP?

There are two things we look for when we’re having this conversation with businesses.

A Clear Use Case Tied to a Business Outcome

The first is a clear use case tied to a business outcome. Not a general interest in AI, but something specific. We want to reduce reporting cycles. We want real-time visibility into trial performance. We want to detect fraud patterns faster. When someone can articulate that clearly, it tells us there’s a real problem to solve, and the investment has a purpose.

Basic Data Maturity

The second is a basic level of data maturity. And that doesn’t mean perfect data (nobody has perfect data). It means having a reasonable understanding of what data you have, where it lives, who is the data owner, as well as some level of governance around it.

What Are the Signs Organizations Are Not Ready for Oracle AIDP?

The clearest sign an organization is not ready for Oracle AIDP is when the conversation about AI is still in the abstract, with no specific use case attached. The language being used sounds like “we should probably do something with AI”, or “we know we need to do something with AI, and we’ve heard Oracle AIDP is the right platform,”. This typically signals that this decision is from external pressure rather than internal clarity.

That being said, there’s nothing wrong with being there, a lot of organizations are. But putting a platform in place before you know what you’re aiming to solve for tends to be where the expensive lessons happen. The value from Oracle AIDP is directly tied to a strong use case. A concrete goal like reducing trial reporting from three weeks to three days, or surfacing fraud patterns in real time, is measurable from day one. A vague goal has no benchmark for success and no clear point at which the platform has earned its cost.

We’d rather have these honest conversations up front. If someone’s ready, we’ll move fast. If they need to do some foundational work first, we’ll say that too. We’ve found that being straightforward about readiness early on builds a much stronger relationship than telling someone what they want to hear.

What's the Most Common Oracle AIDP Readiness Gap?

Data governance. Almost every time.

The instinct to skip past it is something I personally struggle with. I’m not naturally patient. If there’s a room to paint, I want to grab the roller and start covering walls because that’s where the progress feels visible. But I’ve learned the hard way that the real work is in the prep. Filling the nail holes, sanding, and taping off the edges. It feels like you’re not getting anywhere, but skipping it always shows up later. And fixing it later on takes twice as long as just doing it right the first time.

Data governance is the prep work. And part of the reason it gets skipped is that for a lot of organizations, it’s unfamiliar territory. If you haven’t been through it before, it can feel like this big, abstract thing that’s hard to get your arms around. Where do you even start?

That’s exactly why we built our Data8 framework. It gives organizations a practical way to look at their data maturity across eight key dimensions and figure out where the gaps are before they start spending on platforms. It makes governance feel less like a mountain and more like a checklist. Sometimes the answer is that AIDP is the right next step. Sometimes the answer is that there’s foundational work to do first. Either way, understanding where you stand saves a lot of time and money down the road.

Is there a Low-Risk Way to Test Oracle AIDP Before Committing?

Yes. We use a Pilot, Prototype, Project approach that lets you see results on representative data before any infrastructure or procurement commitment.

This is probably the question we enjoy answering the most, because it’s exactly how we like to work.

We use what we call a Pilot, Prototype, Project approach. We run the pilot on our own AIDP environment using synthetic or sample data, so the client isn’t standing up infrastructure or going through lengthy procurement before they’ve seen what’s possible.

In our work with University College Dublin Clinical Research Centre, we used synthetic patient data and sample datasets loaded into AIDP in a matter of days rather than the weeks or months the traditional path would have taken. The compliance and data access requirements were significantly simpler because we weren’t working with sensitive patient data at that stage. The team got to see real results before committing to anything further.

From there, if the value is clear, it moves into a prototype with more structure, and eventually into a full production project with live data and integration. But the starting point is always low risk and fast. We’d rather show someone what’s possible than spend three months talking about it.

What Should You Do If You're Not Ready for Oracle AIDP Yet?

If the readiness conversation suggests you’re not ready for AIDP yet, that’s not a setback. It’s information that saves you a significant amount of money and a significant amount of organizational pain.

The right next step in this scenario is foundational data work. That usually means some combination of cataloging what data you hold, establishing ownership and governance baselines for the datasets that matter most, and getting clarity on the specific business outcomes that would make a future AIDP investment worthwhile. The Data8 framework is the structured way we do this with clients, and the engagement is meaningfully shorter and less expensive than a full platform implementation.

The other thing worth saying is that the foundational work makes the eventual AIDP rollout faster, cheaper, and much more likely to succeed. Organizations that invest in the prep work tend to deploy AIDP in a fraction of the time it takes organizations that try to do both at once, because the platform doesn’t have to absorb the cost of unresolved data ownership and governance questions during deployment.

Next Steps

If you’re at the stage of thinking about Oracle AIDP and you want an honest readiness conversation, Contact Us.

If you’re still working out what Oracle AIDP is and how the platform is structured, our practitioner’s guide covers the architecture, the Workbench, and the use cases in depth. Our Guide: Oracle AI Data Platform

About John Fitts

connect@verticecloud.com

John Fitts

Senior VP North America

The post Oracle AIDP Readiness: How to Tell If You’re Ready (and What to Do If You’re Not) appeared first on Vertice.

Setting Up LACP Bonding (Mode 4) for Oracle Linux Virtualisation Manager (OLVM)

Conor Kehoe — Mon, 20 Apr 2026 13:53:23 +0000

Author: Jason Beattie

Applies to: Oracle Linux 8 / 9, OLVM 4.x, Cisco Nexus or MLAG-capable switches

Overview

In an OLVM environment, Link Aggregation Control Protocol (LACP) bonding enables multiple NICs to act as a single high-performance link.

This provides bandwidth aggregation and fault tolerance, essential for virtualized workloads and management networks.

Network Architecture

Typical layout for each OLVM host:

				
					ens1f0np0  \
             > bond0 (mode 4, LACP)
ens1f1np1  /

bond0.11  → VLAN 11 (Storage)
bond0.20  → VLAN 20 (Management)

Each NIC connects to a Cisco or similar switch, configured as an LACP trunk.

Step 1: Configure the Switch

Example Cisco NX-OS configuration:

				
					interface Ethernet1/22
  description OLVM-Host01-NIC1
  switchport
  switchport mode trunk
  switchport trunk allowed vlan 11,20
  channel-group 15 mode active
  no shutdown

interface Ethernet1/23
  description OLVM-Host01-NIC2
  switchport
  switchport mode trunk
  switchport trunk allowed vlan 11,20
  channel-group 15 mode active
  no shutdown

interface port-channel15
  description OLVM-Host01-Bond0
  switchport
  switchport mode trunk
  switchport trunk allowed vlan 11,20
  spanning-tree port type edge trunk

Requirements:

Both ports in the same port-channel
Same VLANs, MTU, and speed
FEC (Forward Error Correction) aligned (Auto or Off)
channel-group mode active (for LACP)

Step 2: Configure OLVM Host Bonding

				
					# Create bond
nmcli con add type bond ifname bond0 mode 802.3ad miimon 100 lacp-rate fast xmit-hash-policy layer3+4

# Add slaves
nmcli con add type ethernet ifname ens1f0np0 master bond0
nmcli con add type ethernet ifname ens1f1np1 master bond0

# Bring interfaces up
nmcli con up bond0
nmcli con up ens1f0np0
nmcli con up ens1f1np1

Step 3: Create VLAN Interfaces

				
					nmcli con add type vlan con-name bond0.11 dev bond0 id 11
nmcli con add type vlan con-name bond0.20 dev bond0 id 20

Assign IP to management VLAN:

				
					nmcli con mod bond0.20 ipv4.addresses 10.178.7.83/24
nmcli con mod bond0.20 ipv4.gateway 10.178.7.1
nmcli con mod bond0.20 ipv4.method manual
nmcli con up bond0.20

Step 4: Verify Operation

				
					cat /proc/net/bonding/bond0

Expected:

				
					Bonding Mode: IEEE 802.3ad Dynamic link aggregation
LACP active: on
Aggregator ID: 1
Number of ports: 2
Partner Mac Address: <Switch MAC>

Both interfaces must have matching Aggregator IDs and Partner MACs.

Troubleshooting

Issue	Cause	Fix
Partner Mac Address: 00:00:00:00:00:00	Switch not sending LACP PDUs	Ensure channel-group mode active
Only one NIC active	Aggregator mismatch	Verify VLAN/MTU match
Drops or slow throughput	FEC or link negotiation issue	Align FEC or disable auto-neg
LACP flapping	Cabling or optics mismatch	Reseat transceivers, check logs

Summary

Setting	Value
Bond mode	802.3ad (LACP)
Hash policy	layer3+4
LACP rate	fast
VLANs	Trunked
Switch config	Port-Channel Active

LACP bonding offers performance scaling and redundancy, ideal for stable 10 Gb environments or modern 25 Gb optics with validated FEC alignment.

connect@verticecloud.com

Jason Beattie

Senior Architect and Cloud Operations Manager

The post Setting Up LACP Bonding (Mode 4) for Oracle Linux Virtualisation Manager (OLVM) appeared first on Vertice.

Technical Deep Dive: Adding X10 Storage Cells to an Existing Oracle Exadata Environment

Conor Kehoe — Mon, 06 Apr 2026 09:30:00 +0000

Author: Jason Beattie

Objective

This post details the end-to-end process of expanding an existing Oracle Exadata environment by integrating two additional X10 storage cells. The steps include validating system compatibility, creating grid disks, integrating with Oracle ASM, and verifying post-implementation health and capacity.

Note: Images and specific IP Related Details has been masked for security reasons. Please reach out if you have any questions on this blog post.

System Overview

Environment Setup (Before Expansion):

Existing Storage Cells: Cell01, Cell02, Cell03
Database Nodes: DB01, DB02
ASM Disk Groups: DATA, DONOTUSE, RECOVER
Redundancy Mode: High

Storage Cells Added:

New Cell A
New Cell B

Note: All hostnames and IPs referenced in this blog are anonymized for confidentiality.

1. Initial Validation

Before adding the new cells, we validated the following:

Network Reachability: Ensured new storage cells were reachable from both DB nodes via public and private interfaces.
Image Consistency: Confirmed all storage servers were running matching image versions.
CLI Tools: Verified that CellCLI was operational on both new storage cells.

2. Grid Disk Provisioning

On each of the new cells, grid disks were manually created using CellCLi. Each physical disk was divided among the ASM disk groups according to defined sizing policies.

Sample Commands (Executed via CellCLI):

create griddisk DATA_CD_00_ celldisk=CD_00_, size=8.111328125T create griddisk DONOTUSE_CD_00_ celldisk=CD_00_, size=2.77734375T create griddisk RECOVER_CD_00_ celldisk=CD_00_, size=1.4443359375T

This pattern was repeated across six celldisks per cell for both new storage servers.

3. ASM Integration

With grid disks created, the next step was to integrate them into ASM. First, we validated new disks using:

asmcmd lsdsk --candidate

Then, disks were added to each disk group:

DATA

ALTER DISKGROUP DATA ADD DISK 'o//DATA_CD_00_', ... 'o//DATA_CD_05_';

RECOVER

 ALTER DISKGROUP RECOVER ADD DISK 'o//RECOVER_CD_00_', ... 'o//RECOVER_CD_05_';

DONOTUSE

ALTER DISKGROUP DONOTUSE ADD DISK 'o//DONOTUSE_CD_00_', ... 'o//DONOTUSE_CD_05_';

Each operation triggered an automatic rebalance operation handled internally by ASM.

4. Post-Integration Checks

Once disks were added, we ran several validations:

ASM Usage Verification

SELECT NAME, TOTAL_MB/1024 AS TOTAL_GB, FREE_MB/1024 AS FREE_GB FROM V$ASM_DISKGROUP;

Verified all newly added disks were visible

Image Redacted.

Confirmed that rebalance operations completed successfully.
Checked logs for errors (none found in ASM or alert logs).

5. Capacity Results

Disk Group	Capacity Before (TB)	Capacity After (TB)
DATA	13.59	25.16
DONOTUSE	5.24	8.75
RECOVER	2.72	4.35

Total usable capacity saw a significant increase across all critical disk groups.

6. Summary

Task	Status
Image version check	✅ Completed
Networking configuration	✅ Successful
Grid disk creation	✅ Completed
Disk integration into ASM	✅ Rebalance successful
Capacity validation	✅ Verified

Conclusion

The integration of two new X10 storage cells into the existing Oracle Exadata environment was completed successfully. With zero errors during validation and over 80% storage capacity gain in core disk groups, this expansion lays the foundation for enhanced performance and future scalability.

connect@verticecloud.com

Jason Beattie

Senior Architect and Cloud Operations Manager

The post Technical Deep Dive: Adding X10 Storage Cells to an Existing Oracle Exadata Environment appeared first on Vertice.

Oracle Vision AI: A Beginner’s Guide to Custom Models – Part 2

Conor Kehoe — Mon, 23 Mar 2026 10:30:00 +0000

Author: Philip Godfrey

In the previous blog, we introduced the Oracle Vision and creating a Custom Vision model.

The second blog in the series will work through creating and training a Custom Vision model using our previously labelled dataset.

What is Oracle Vision?

One of many AI Services introduced by Oracle, OCI Vision serves as an advanced AI tool aimed at facilitating extensive image analysis powered by deep learning algorithms.

It streamlines the process of integrating image and text recognition features into applications, eliminating the need for specialised knowledge in machine learning.

Accessing Oracle Vision

In OCI, navigate to Analytics & AI, and under the AI Services subheading, you will find Vision.

Once you click here, providing the necessary pre-requisites have been granted, you should see the overview of Oracle Vision.

The focus of this blog will be Custom models, so we need to navigate to Projects on the left-hand side of the menu:

Custom Models are available in the form of Image Classification and Object Detection.

In the previous blog we created a project, make sure to look back on that if you missed it. Blogs before that explained how we created our labelled dataset, that we will use to train a custom model on.

Note: Minimum Number of Labelled Images for each Label for a Custom Model is 10

There are several steps to creating and training a model including:

Type: Object Detection or Image Classification
Training Data: Existing dataset or Create New Dataset (this will take you through to Data Labelling)
Data Source: Data Labelling service (previously labelled data) or Object Storage
Dataset: Select dataset from desired source

Optional Advanced Settings

Test Dataset: select dataset from object storage bucket
Validation Dataset: select dataset from object storage bucket

Train Model

The next stage in the process of to train a model – for this there are some required fields you need to populate including:

Model Display Name: provide a unique display name for your model, which helps when collaborating with team members

Model Description (optional): optional to provide a description of the model, which helps provide extra context to the model

Training Duration: select from one of three options:

Recommended Mode
Quick Mode
Custom

Which training duration should I choose?

The most appropriate training duration depends on the complexity of your problem, the typical number of objects in an image, the volume of data you are incorporating and budget, along with many other factors.

A summary of each training mode can be found below, to help you determine what is most appropriate

Train Model

As this is a first pass of the model, we have decided to go for Quick mode, with the view we can get an early indication of model performance on a model that is not fully optimized.

Create and Train

The final stage of the process is a review of all steps completed so far.

This provides a summary of the training data asset information, along with Model Information (model name/ type / training duration)

If you need to amend any of these attributes, you can select Edit to update these attributes.

However, if all is as expected, click Create and Train to kick-off the model training process.

The next blog in the series will focus on creating and training a Custom Vision model in OCI.

Once completed, you will see the model has been created and trained successfully.

Interested to know more?

We’d be more than happy to discuss your requirements or questions, please get in touch here.

connect@verticecloud.com

Philip Godfrey

Principal AI & Data Analytics Consultant

The post Oracle Vision AI: A Beginner’s Guide to Custom Models – Part 2 appeared first on Vertice.

Is Your OCI Build Choking Your Own Growth

declanryan — Fri, 13 Mar 2026 15:00:55 +0000

Author: Jason Beattie

Getting support tickets about load balancer limits? The problem probably isn’t Oracle Cloud. It’s the architecture underneath it.

A surprising number of Independent Software Vendors running multi-tenant platforms on Oracle Cloud Infrastructure eventually receive a message that looks something like this:

“Your tenancy has reached its load balancer limit.”

For many organisations, this moment arrives during growth.

A new customer needs onboarding. A new environment needs provisioning.

And suddenly the infrastructure that once felt scalable… isn’t.

At first glance, it’s easy to assume the platform is the problem. Limits exist in every cloud provider, after all.

But in the vast majority of cases, the cloud isn’t the blocker.

The architecture is.

More specifically, it’s often the architecture that was designed early in the journey – sometimes by internal teams, sometimes by external providers – when the platform only had a handful of customers and scale wasn’t yet a concern, or during a rushed migration to the cloud where immediate delivery took priority over long-term scalability.

The Pattern That Quietly Breaks ISV Platforms

One pattern appears again and again in hosted enterprise software environments – particularly platforms built around applications such as Primavera, Oracle E-Business Suite, JD Edwards, PeopleSoft, Unifier, Aconex, and IBM Maximo.

Each customer environment is exposed through its own port.

At first this feels logical. It separates tenants cleanly and allows environments to be provisioned quickly.

It works perfectly when there are five customers.

It works when there are ten.

But by the time platforms reach dozens of customers, the underlying architecture begins to show its age – and organisations start encountering limits that were never meant to be part of the growth journey.

REALITY CHECK
Cloud limits rarely stop platforms from scaling. Architectural patterns that grow infrastructure alongside customers eventually do.

The Question ISVs Should Be Asking

When infrastructure limits begin appearing during growth, the first reaction is often to request higher quotas or deploy additional resources.

But the more important question is this:

Is the architecture designed for where the platform is going – or where it started?

Modern multi-tenant platforms should not require new infrastructure every time a customer is onboarded.

When they do, operational complexity increases alongside the business. Eventually the platform itself becomes the constraint. The good news is that these issues are often solvable with a careful architectural review.

connect@verticecloud.com

Jason Beattie

Senior Architect and Cloud Operations Manager

The post Is Your OCI Build Choking Your Own Growth appeared first on Vertice.

Oracle Vision AI: A Beginner’s Guide to Custom Models – Part 1

declanryan — Mon, 09 Mar 2026 12:36:02 +0000

Author: Philip Godfrey

In the previous blog, we introduced the Oracle Vision service, explaining what it is, any necessary pre-requisites to enable the service, and exploring utilizing pre-trained models within the Vision service.

The next blog in the series will work through creating a Custom Vision model in Oracle Vision.

What is Oracle Vision?

One of many AI Services introduced by Oracle, OCI Vision serves as an advanced AI tool aimed at facilitating extensive image analysis powered by deep learning algorithms.

It streamlines the process of integrating image and text recognition features into applications, eliminating the need for specialised knowledge in machine learning.

Pre-Requisites

To access Vision, there are several steps that need to be taken by an Administrator within your tenancy, including necessary groups, policies and dynamic groups. A full list of requirements can be found here -> About Vision Policies

Accessing Oracle Vision

In OCI, navigate to Analytics & AI, and under the AI Services subheading, you will find Vision.

Once you click here, providing the necessary pre-requisites have been granted, you should see the overview of Oracle Vision.

This has lots of useful resources for you to utilize, including video tutorials introducing the service in simple and easy to consume terms, through to more detailed and in-depth release notes and documentation.

On the left-hand side, you have access to multiple areas:

Overview – the page above
Image Classification – utilize pre-built models to assign classes and confidence scores
Object Detection – utilize pre-built models to identify common objects and confidence scores
Face Detection – utilize pre-built models to identify faces from an image
Text Detection – utilize pre-built models to identify text in documents
Projects – where we can create a custom models (Image classification and object detection) trained on your own labelled dataset.

The focus of this blog will be Custom models, so we need to navigate to Projects on the left-hand side of the menu:

Pre-Requisites

Much like the Vision service, there are some additional setup tasks that need to be performed by an Administrator within the tenancy. These are outlined below.

Custom Models are available in the form of Image Classification and Object Detection.

To get started we select Create project.

We are then presented with a form to specify the details on the project, including Name, Description and any optional Tags.

This will then be available as a custom project from the specified compartment:

The next blog in the series will focus on creating and training a Custom Vision model in OCI.

Interested to know more?

We’d be more than happy to discuss your requirements or questions, please get in touch here.

connect@verticecloud.com

Philip Godfrey

Principal AI & Data Analytics Consultant

The post Oracle Vision AI: A Beginner’s Guide to Custom Models – Part 1 appeared first on Vertice.

Modernise Oracle Forms on OCI

declanryan — Tue, 03 Feb 2026 16:28:30 +0000

Author: Jason Beattie

Oracle Forms and Reports continue to power some of the world’s most mission-critical enterprise systems. Yet for many organisations, aging infrastructure, complex upgrades, and rising operational costs are slowing innovation and increasing risk.

With Oracle Forms 14c (14.1.2) fully supported on Oracle Cloud Infrastructure (OCI), enterprises now have a clear, low-risk path to modernize.

Why OCI Is the Right Home for Oracle Forms

Using the Oracle WebLogic Suite UCM Marketplace Image, organizations can deploy a certified, production-ready Oracle stack that is license-inclusive, removing the need for complex BYOL licensing.

The Marketplace image Currently includes:

Oracle Forms 14.1.2 (14c)
Oracle WebLogic Server 14.1.2 (14c)

For the database tier, organisations can leverage a license-inclusive Oracle Database 19c instance, simplifying procurement and ensuring predictable costs.

This approach eliminates manual installations, reduces configuration errors, and accelerates time-to-value. Allowing IT teams to focus on business outcomes instead of infrastructure management.

The Business Benefits

1. Simplified Licensing

License-inclusive middleware and database options reduce compliance risk and operational overhead.

2. Enterprise-Grade Security

Built-in OCI security services, networking controls, and IAM capabilities protect critical workloads.

3. Oracle Support Alignment

Run on certified, fully supported Oracle versions to minimise risk and extend application lifespan.

4. Future-Ready Architecture

Establish a foundation for gradual modernisation, integration, and cloud-native services on your timeline.

Where Vertice Fits In

Migrating Oracle Forms isn’t just about moving servers! it’s about doing it right.

Vertice helps organisations:

Migrate Oracle Forms and Reports to OCI using certified Marketplace images

Upgrade existing environments to Forms 14c
Optimise WebLogic and database configurations for performance, security, and resilience
Define a roadmap toward future modernisation
Vertice supports the migration as a true partner. We work closely alongside our customers, ensuring they retain control throughout the journey.
Our collaborative approach means nothing happens in the dark. All knowledge is shared, decisions are transparent, and your teams stay empowered every step of the way.

OCI allows enterprises to preserve their Oracle Forms investment while gaining the agility, scalability, and security of the cloud. With the right partner, migration becomes predictable, and low risk.

Vertice makes Oracle Forms cloud migration simple. So, your business can move forward into the modern world!

connect@verticecloud.com

Jason Beattie

Senior Architect and Cloud Operations Manager

The post Modernise Oracle Forms on OCI appeared first on Vertice.

Oracle Vision: Harnessing Pre-Trained Models for Smarter Insights

declanryan — Wed, 28 Jan 2026 12:30:15 +0000

Author: Philip Godfrey

One of many AI Services introduced by Oracle, OCI Vision serves as an advanced AI tool aimed at facilitating extensive image analysis powered by deep learning algorithms.

It streamlines the process of integrating image and text recognition features into applications, eliminating the need for specialised knowledge in machine learning.

Pre-Requisites

Accessing Oracle Vision

In OCI, navigate to Analytics & AI, and under the AI Services subheading, you will find Vision.

Once you click here, providing the necessary pre-requisites have been granted, you should see the overview of Oracle Vision.

On the left-hand side, you have access to multiple areas:

Overview – the page above
Image Classification – utilize pre-built models to assign classes and confidence scores
Object Detection – utilize pre-built models to identify common objects and confidence scores
Face Detection – utilize pre-built models to identify faces from an image
Text Detection – utilize pre-built models to identify text in documents
Projects – where we can create a custom models (Image classification and object detection) trained on your own labelled dataset.

Pre-Trained Models

Object Detection

As an example, we can pass in an image from a previous blog series (car parks / spaces) to see how the pre-trained model performs in identifying cars.

If we select Object Detection we can then navigate to a Local File and pass in a png/jpeg file, the pre-trained model is then invoked and objects identified are returned. It has successfully (and confidently) identified all cars in the image with a good level of confidence!

Image Classification

As a further image, we can also call the pre-trained Image Classification model, if we provide another example image, we can see what it has identified:

Exploring the results we can see that Cars have been identified with the greatest confidence (which you would expect) however it has also identified there are Vehicles, Land vehicle, and it has also identified there is a parking lot in the picture! So, we can get lots of useful information from the pre-trained models.

Join our next blog in the series where we cover Custom Models in detail, and how we can create Vision models based on our own data.

Custom Models

There are several pretrained models available for Image Classification, Object Detection and Text Recognition.

Custom Models are available in the form of Image Classification and Object Detection.

Pretrained Models:

Image Classification
Object Detection
Text Recognition

Custom Models:

Image Classification
Object Detection

The next blog in the series will explore creating a Custom model in Oracle Vision and show you how easy it is to get up and running.

connect@verticecloud.com

Philip Godfrey

Principal AI & Data Analytics Consultant

The post Oracle Vision: Harnessing Pre-Trained Models for Smarter Insights appeared first on Vertice.

Adding X10 Storage Cells to an Existing Oracle Exadata Environment

colmmccarthy — Fri, 02 Jan 2026 11:36:45 +0000

Author: Jason Beattie

Objective

System Overview

Environment Setup (Before Expansion):

Existing Storage Cells: Cell01, Cell02, Cell03
Database Nodes: DB01, DB02
ASM Disk Groups: DATA, DONOTUSE, RECOVER
Redundancy Mode: High

Storage Cells Added:

New Cell A
New Cell B

Note: All hostnames and IPs referenced in this blog are anonymized for confidentiality.

1. Initial Validation

Before adding the new cells, we validated the following:

Network Reachability: Ensured new storage cells were reachable from both DB nodes via public and private interfaces.
Image Consistency: Confirmed all storage servers were running matching image versions.
CLI Tools: Verified that CellCLI was operational on both new storage cells.

2. Grid Disk Provisioning

On each of the new cells, grid disks were manually created using CellCLi. Each physical disk was divided among the ASM disk groups according to defined sizing policies.

Sample Commands (Executed via CellCLI):

				
					create griddisk DATA_CD_00_<cellname> celldisk=CD_00_<cellname>, size=8.111328125T create griddisk 
DONOTUSE_CD_00_<cellname> celldisk=CD_00_<cellname>, size=2.77734375T create griddisk 
RECOVER_CD_00_<cellname> celldisk=CD_00_<cellname>, size=1.4443359375T

This pattern was repeated across six celldisks per cell for both new storage servers.

3. ASM Integration

With grid disks created, the next step was to integrate them into ASM. First, we validated new disks using:

				
					asmcmd lsdsk --candidate

Then, disks were added to each disk group:

DATA

				
					ALTER DISKGROUP DATA ADD DISK 'o/<private_ip_set>/DATA_CD_00_<cellA>', ... 'o/<private_ip_set>/DATA_CD_05_<cellB>';

RECOVER

				
					 ALTER DISKGROUP RECOVER ADD DISK 'o/<private_ip_set>/RECOVER_CD_00_<cellA>', ... 'o/<private_ip_set>/RECOVER_CD_05_<cellB>';

DO NOT USE

				
					ALTER DISKGROUP DONOTUSE ADD DISK 'o/<private_ip_set>/DONOTUSE_CD_00_<cellA>', ... 'o/<private_ip_set>/DONOTUSE_CD_05_<cellB>';

Each operation triggered an automatic rebalance operation handled internally by ASM.

4. Post-Integration Checks

Once disks were added, we ran several validations:

ASM Usage Verification

				
					SELECT NAME, TOTAL_MB/1024 AS TOTAL_GB, FREE_MB/1024 AS FREE_GB FROM V$ASM_DISKGROUP;

Verified all newly added disks were visible.

Confirmed that rebalance operations completed successfully.
Checked logs for errors (none found in ASM or alert logs).

5. Capacity Results

Total usable capacity saw a significant increase across all critical disk groups.

6. Summary

Conclusion

connect@verticecloud.com

Jason Beattie

Senior Architect and Cloud Operations Manager

The post Adding X10 Storage Cells to an Existing Oracle Exadata Environment appeared first on Vertice.

Blog Archives - Vertice

What is Oracle AIDP for Life Sciences? A Plain-English Guide for Pharma, MedTech and Clinical Research Leaders

Author: John Fitts

The 30-second answer

Why does the Oracle AIDP for Life Sciences conversation matter now?

What is Oracle AI Data Platform?

A unified data foundation

Built-in generative AI and agentic capability

Integration with the wider Oracle ecosystem

Enterprise-grade security and governance

How does Oracle AIDP for Life Sciences build on Oracle AIDP

Pre-loaded real-world data

Industry-specific tooling

Service variants for different organization types

What does this mean for Life Sciences?

Oracle AIDP for Life Sciences use cases

Label expansion

Health Economics and Outcomes Research (HEOR)

Synthetic control arms

Post-market safety monitoring

Regulatory submission support

Oracle AIDP for Life Sciences Case Study: UCD Clinical Research Centre

Is Oracle AIDP for Life Sciences right for your organization?

What to Do Next

Frequently Asked Questions

What is Oracle AI Data Platform?

What is Oracle Life Sciences AI Data Platform?

Oracle AI Data Platform vs Oracle Life Sciences AI Data Platform

What is Oracle Health Real-World Data?

What types of patient data are included?

Can the data be used for regulatory submissions?

Can the platform process genetic data?

What use cases does the platform support?

What tools does the platform include?

What service variants are available?

How does AIDP integrate with other Oracle products?

What is an example of AIDP being used in clinical research?

How long does it take to implement Oracle AIDP for Life Sciences?

How should an organization choose an AIDP implementation partner?

About John Fitts

John Fitts

Oracle AIDP Readiness: How to Tell If You’re Ready (and What to Do If You’re Not)

Author: John Fitts

TLDR

What Does Oracle AIDP Readiness Actually Mean?

How do you know you are ready for Oracle AIDP?

A Clear Use Case Tied to a Business Outcome

Basic Data Maturity

What Are the Signs Organizations Are Not Ready for Oracle AIDP?

What's the Most Common Oracle AIDP Readiness Gap?

Is there a Low-Risk Way to Test Oracle AIDP Before Committing?

What Should You Do If You're Not Ready for Oracle AIDP Yet?

Next Steps

About John Fitts

John Fitts

Setting Up LACP Bonding (Mode 4) for Oracle Linux Virtualisation Manager (OLVM)

Author: Jason Beattie

Overview

Jason Beattie

Technical Deep Dive: Adding X10 Storage Cells to an Existing Oracle Exadata Environment

Author: Jason Beattie

Objective

System Overview

1. Initial Validation

2. Grid Disk Provisioning

3. ASM Integration

DATA

RECOVER

DONOTUSE

4. Post-Integration Checks

ASM Usage Verification

5. Capacity Results

Disk Group

Capacity Before (TB)

Capacity After (TB)

6. Summary

Task

Status

Conclusion

Jason Beattie