Three Years Ago, You Had Data Pipelines to Support Analytics; They Worked, Had Adequately Accurate Dashboards, and Had Acceptable Latency, But Now That Same Pipeline is Feeding into a Machine Learning (ML) Model, and Everything is Breaking in Unexpected and Subtly Different Ways.
Features Do Not Match Training Data, Predictions are Drifting, Models are Demonstrating Erratic Behavior, and Now Your Data Infrastructure for AIis Taking 30-40% of Your Engineering Time Debugging Errors that Nobody Expected to Encounter.
This is the Real World Most ML Teams Work with.
If You're a Staff or Principal Engineer Building an AI System, This Article Will Educate You on the Following:
- What Data Infrastructure for AI is in the Real World
- What Data Layer is Underestimated by Most Engineering Teams
- How to Build a Reliable, Scalable Data Layer for ML Models
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What Is Data Infrastructure for AI? The Plain English Definition
Data Infrastructure for AI is the System that Provides a Reliable Data Flow from Source to Model and Back to Production Systems.
Analogy 1 - Supply Chain
Think of Data Infrastructure as a Supply Chain:
- Raw Materials = Raw Data
- Processing Plants = Transformations
- Distribution Network = Pipelines
- Retail Stores = Model Outputs
If One of the Three Parts of the Supply Chain Breaks, Your Final
Essential Elements
A functional data infrastructure for AI typically consists of:
| Component | Function |
|---|---|
| Ingestion Layer | Manages collection of raw data from sources. |
| Storage Layer | Holds structured and unstructured data. |
| Processing Layer | Transforms data into a format that can be easily used. |
| Orchestration Layer | Manages the flow of data through the pipeline, including all dependencies between tasks that make up the complete pipeline. |
| Observability Layer | Responsible for monitoring the health of the AI system and the quality of the data. |
What Problems It Will Solve
Without a functional data infrastructure for AI, the following issues arise:
Data quality:
Data is no longer consistent across different data sources.
Model Inputs:
Models are no longer being trained using reliable and consistent inputs.
Model Outputs:
Model outputs lose reliability over time.
With a functional data infrastructure for AI, business can expect that:
Data Quality:
Data will continue to be available in an adequate state of consistency, and will remain traceable throughout the entire data pipeline.
Workflow:
Data pipelines will function effectively with minimal delays and disruptions.
Model Performance:
AI model outputs will be reliable.
Key Takeaway
A functional data infrastructure for AI is about ensuring that data is managed in a way that creates trust within the pipeline at every stage of the data pipeline.
Why Having a Functional Data Infrastructure for AI is More Important in 2026 Than Ever
Importance has increased greatly due to the following reasons:
1. Rapid Growth in AI Adoption
The numbers of organizations now deploying the following AI-based applications have increased significantly since:
- Recommendation System
- Fraud Detection Models
- Predictive Analytics Models
A large number of organizations are still relying on analytics-based infrastructure, rather than dedicated AI infrastructure, to manage their AI data pipeline.
2. Explosion of Data Volume and Complexity
Modern systems now handle:
- Real-time Data Streams
- Integration of multiple sources
- High-frequency updates
As a result, there are more:
- Dates Points of Failure
- Inconsistent Data
3. Cost Associated With Failed Pipelines Will Continue to Increase
When data pipelines fail:
Predictions:
Models generate erroneous predictions.
Business Decisions:
Poorly-made (based upon erroneous predictions) business decisions are made.
Customer Experience:
Customers experience a decline in their service.
Example of a Transition
Before Transition - To an Analytics Based System
Batch Processing, Delays were tolerated, Limited Dependency between tasks that perform complete work product of pipeline.
After Transition - To an AI Based System
Real-time Requirements, High Dependency Chains, Zero Tolerance for Errors.
Statistics
About 70% of machine learning project failures are due to data issues rather than the design problems related to the machine learning model itself, according to industry estimates.
Important Insight:
Failure to account for data infrastructure in AI Development is not detrimental immediately, but will cause problems over time due to compounding risk.
Data Infrastructure (for AI) consists of multiple components that combine structures to operate effectively.
Identify the architecture of AI
1. Data Ingestion Layer
What is Included:
- API's
- Event Streams
- Database Ingest
What is Required:
- Reliability
- Scalability
- Schema Validation
2. Data Storage Layer
Includes:
- Data Lakes
- Data Warehouses
Required:
- Supports Structured or Unstructured Data
- Supports Historical datasets for Training
3. Data Processing Layer
Will:
- Clean Up®
- Enable Feature Engineering®
- Aggregate Data®
Most inconsistencies occur here.
4. Data Orchestration Layer:
Manages:
- Workflow Dependency
- Relationship between Workflows
- Scheduling Workflow Execution
- Retry Failed Workflows
Data Orchestration Layer Ensures All Pipelines Execute in the Correct Order
5. Data Observability Layer
Monitors:
- Data Quality®
- Pipeline Health
- Data Freshness
Typically does not have adequate funding but is still very critical.
Includes:
- Data Movement®
- Data Transformation®
- Data Monitoring
Will not include:
- Model Training Algorithms®
- Model Deployment Systems®
Key Insight;
The strength of your data pipeline is determined by the components' integrity.
Real World Example of How AI Uses Data Infrastructure:
Let's use a standard example to
Data Ingestion:
Activity data will be provided to the application from multiple sources including: the internet, mobile applications and back-end systems.
Data Storage:
Stored Data will include files stored in the Centralized Data Lake and the files will be partitioned in order to provide more efficient access.
Data Processing:
Transforming the data prior to using it for training will include (in this order): cleaning missing values, creating features and aggregating metrics.
Model Training:
The processed data will be utilized for two purposes: as a training data set, and as a feature store.
Model Inference:
The model will generate predictions such as recommendations, risk scores, and insights.
Tracking and Monitoring:
You should monitor the following metrics: prediction accuracy, data drift and pipeline performance.
Identifying Pipeline Breakdowns:
Common causes of pipeline breakdowns with your data infrastructure are: data schema changes, data delays and transformation errors.
If you have no observability into your data infrastructure, you will not be aware of pipeline breakdowns.
Key Insight:
The real world is not a linear system, all events are interrelated and occur simultaneously, therefore, any breakdown in one system will impact multiple other systems.
Common Mistakes Teams Make Regarding Data Infrastructure for AI:
Even the most experienced teams will make common mistakes that could have been avoided.
1. Over-engineering the solution from the very beginning results in: too much complexity and a longer development timeline.
2. Adding insufficient levels of observability will result in: no ability to track and monitor the occurrence of issues, late detection of the existence of issues, and a lack of ease in debugging issues.
3. Avoiding the creation of data contracts will increase the potential for your pipelines to fail when changes to the data schema occur.
4. Treating data infrastructure as a one-time project instead of an evolving entity and therefore old static systems will not be able to continue to meet the requirements of your new operating environment.
Key Insight:
The majority of failures with data infrastructure result from a systemic lack of tools.
What Do High-Performing Teams Do Differently Regarding Data Infrastructure for AI?
All of the high-performing teams in the study exhibited similar behaviors.
Automate as Much as Possible
- Validation of Data
- Alerts
- Monitoring
Automating These Processes Lessens Errors Made by Humans.
Use Your Infrastructure as if it were Software and Version Control Your Configuration so You Can Reproduce It.
Makes Your Environment More Reliable.
Design Systems To Fail
Add
- Retry Logic
- Circuit Breakers
- Dead Letter Queues
Failures Happens All The Time, So Design for Them.
Identify Your SLAs Early
Define What Each SLA Represents Like
- Pipeline Reliability
- Data Freshness
Does This Create a Common Understanding and Create Alignment Among Teams and Other Stakeholders.
Choose Integrated Platforms
Use Logiciel:
- Eliminates the Cost of Finding Out What Went Wrong by Providing Observability and Lineage
- Automates Redundancys
- Eliminates Additional Staff That Are Needed as a Result of Premature Breakdowns
Important Insight:
Consistency & Automation are the Foundation for Scalable Data Engineering Systems.
In Conclusion
The Data Layer is the Most Under-Rated Layer of your AI Data Infrastructure
3 Key Points to Remember
- Most ML Projects Fail Because of Issues with Data
- Investing in Infrastructure that will Last Long-Term Reduces Risk
- Without Observability & Reliability, a Working AI System will Drop in Value Too Much, It is therefore Challenging to Build One
Incremental Design & Development - Do Not Over Develop and Do Not Develop Too Quickly
This is From Complexity - To Overcome Complexity is Requires:
- Reliable AI Systems
- Speed of Innovation
- Better Business Outcomes
Data Infrastructure ROI Calculator
Use this ROI calculator to measure maintenance cost, inefficiencies, and hidden losses in your data stack.
Take Action
If You are In the Middle of Building/Scaling Your AI System:
Step 1:
Build Your Infrastructure - Data Infrastructure Framework - Build Your Roadmap for Your Engineering Team
Step 2:
Take Action - Click Here to See How Logiciel Can Help You Build an Infrastructure Ready For Your AI Development.
How We Help Engineering Teams Build Reliable, Scalable, Production Ready AI First Data System by Using:
- Data Engineering Knowledge
- Intelligent Automation
- Proven Frameworks
To Work Together To Transition From Experimenting to Operational Excellence.
Frequently Asked Questions
What is the Most Important Layer of Your Data Infrastructure To Use For AI
The Data Layer, the Layer That Delivers Data To The Model, that has Data Quality Assurance, Data Reliability and Data Consistency.
What Are The Main Reasons for Failure Of Machine Learning Systems Occur Due To Data Related Issues?
The Most Frequent Causes of Failure in Machine Learning Systems Occur Due To Poor Quality Data, Changes In The Data Schema or Connectivity Problems In Your Data Pipeline, Quality Of Data Affects How Well Your Model Works.
What Can Teams Do To Improve The Reliability Of Data For AI Systems?
Create Data Contracts, Use Automated Data Validation, Construct Robust Monitoring Capabilities For Your Data Pipeline, and Use Observability.
What Is Data Observability for AI Infrastructure?
The Ability to Monitor Your Data Pipeline for Unexpected Results and to Understand What is Happening in Real Time in Your Data Pipeline.
Where Should Teams Start When Building AI Infrastructure Data Infrastructure?
Start With The Most Important Pipelines, Implement Monitoring and Validation, and Build Incrementally.
