Ensure You Have the Capabilities to Win with GenAI
Data and Infrastructure Readiness
Workshop
Get training-ready—data, pipelines, and platforms that won’t break at scale
Successful model training starts long before the first run—it depends on having the right data, quality controls, and infrastructure in place. This workshop helps teams prioritize training data.
Leave with a readiness blueprint—data priorities, quality and governance plan, and pipeline/infrastructure requirements.
The Challenge
Teams often begin training initiatives before they have the data and platform foundations to do it reliably.
- Training data isn’t prioritized: Data is available, but not curated, representative, or aligned to the outcomes the model.
- Quality and governance are inconsistent: Gaps in data quality, lineage, and approvals create delays and rework.
- Pipelines and infrastructure don’t scale: Tooling and platform choices aren’t designed for repeatable, scalable training workflows.
Without readiness, model training becomes slow and fragile—driving cost without consistent improvement.
Our Solution
We guide your team through a practical approach to prepare the data and infrastructure foundations required for effective model training.
- Training Data Prioritization and Collection: Identify the highest-value data sources and define what data is needed to meet training.
- Data Quality and Governance Guardrails: Establish standards and workflows for quality, approvals, access, and stewardship.
- Data Preparation and Feature Engineering Plan: Define preparation steps and feature readiness needs to support reliable training and evaluation.
- Infrastructure and Tooling Requirements: Identify platform capabilities and tools required to support training workflows and iteration.
- Scalable Training Data Pipelines: Design pipelines that support repeatability, change management, and scalability over time.
Area of Focus
- Identifying and Gathering High-Priority Training Data
- Ensuring Data Quality and Proper Governance
- Performing Data Preparation and Feature Engineering
- Addressing Infrastructure and Tooling Requirements
- Designing Scalable Data Pipelines for Model Training
Participants Will
- Identify priority training data and define what “good” looks like for representativeness and coverage.
- Establish data quality and governance practices that reduce rework and enable responsible access.
- Define preparation and feature engineering needs to support repeatable training outcomes.
- Clarify infrastructure and tooling requirements to enable scalable training workflows.
- Leave with a readiness plan and next steps to build training pipelines that support.
Who Should Attend:
Solution Essentials
Format
Facilitated workshop (in-person or virtual)
Duration
4 hours
Skill Level
Intermediate
Tools
Slides, workshop templates, key worksheets, checklists, and collaboration tools.