AI and Data in Business
Small Language Models and Edge AI
Small Language Models and Edge AI helps organizations move from promising demonstrations to reliable, governed business use. Participants examine SLM and edge use cases, model selection and compression, and device deployment before producing a defensible solution design and evaluation plan.
Overview
Practical learning for workplace transfer.
The course is designed around the decisions practitioners actually face in Small Language Models and Edge AI. Its progression—from SLM and edge use cases through privacy latency and energy—uses architecture choices, evidence-based evaluation, and an applied solution review. The final edge solution assessment requires participants to justify recommendations, test assumptions, and set practical next steps.
Objectives
- Explain the role, scope, and business significance of SLM and edge use cases in helping organizations move from promising demonstrations to reliable, governed business use.
- Diagnose model selection and compression through architecture choices, evidence-based evaluation, and an applied solution review and prioritize the most material gaps.
- Design an approach to device deployment with the roles, safeguards, dependencies, and evidence needed to produce a defensible solution design and evaluation plan.
- Evaluate privacy latency and energy using measures and failure scenarios appropriate to architecture choices, evidence-based evaluation, and an applied solution review.
- Complete the edge solution assessment and translate its findings into owned actions leading toward a defensible solution design and evaluation plan.
Target audience
- AI, data, analytics, and digital-product leaders
- Data scientists, engineers, architects, and business analysts
- Product, automation, knowledge, and transformation teams
- Risk, assurance, and operational owners of AI-enabled services
Program outline
A clear structure for the learning journey.
Program outline
Outline points are grouped in one designed block instead of being treated as separate module cards.
Module 1: SLM and edge use cases
Interpret the issue through the organization’s processes, information, responsibilities, and risk appetite. Establish the vocabulary, boundaries, stakeholders, and decision context for SLM and edge use cases.
Convert the analysis into a role-specific solution canvas with explicit owners and decision gates. Separate established requirements and reliable evidence from untested assumptions about SLM and edge use cases.
Peer-review feasibility, unintended effects, and the evidence required before approval. Map SLM and edge use cases to the organization’s current responsibilities, dependencies, and constraints.
Module 2: model selection and compression
Interpret the issue through the organization’s processes, information, responsibilities, and risk appetite. Diagnose the current state of model selection and compression using a structured evaluation lab.
Convert the analysis into a role-specific evaluation lab with explicit owners and decision gates. Compare alternative methods and select an approach suited to risk, maturity, and scale.
Peer-review feasibility, unintended effects, and the evidence required before approval. Document requirements, owners, decision criteria, and exceptions for model selection and compression.
Module 3: device deployment
Interpret the issue through the organization’s processes, information, responsibilities, and risk appetite. Design the workflow, safeguards, and handoffs required for device deployment.
Convert the analysis into a role-specific production-readiness review with explicit owners and decision gates. Test normal, failure, and edge-case scenarios before operational adoption.
Peer-review feasibility, unintended effects, and the evidence required before approval. Review the design for security, quality, accessibility, sustainability, or assurance implications as relevant.
Module 4: privacy latency and energy
Interpret the issue through the organization’s processes, information, responsibilities, and risk appetite. Define meaningful measures, evidence, review cadence, and escalation thresholds for privacy latency and energy.
Convert the analysis into a role-specific solution canvas with explicit owners and decision gates. Investigate performance gaps and separate root causes from symptoms.
Peer-review feasibility, unintended effects, and the evidence required before approval. Plan corrective action, controlled change, and accountable follow-through.
Module 5: Applied Small Language Models and Edge AI Workshop
Interpret the issue through the organization’s processes, information, responsibilities, and risk appetite. Complete a evaluation lab that integrates the course decisions around edge solution assessment.
Convert the analysis into a role-specific evaluation lab with explicit owners and decision gates. Defend recommendations against a realistic stakeholder challenge or scenario.
Peer-review feasibility, unintended effects, and the evidence required before approval. Produce a prioritized workplace action plan with owners, dependencies, and review points.
Materials provided
- Course workbook and subject reference guide
- Applied scenarios, worksheets, and decision templates
- Implementation checklist or roadmap canvas
- 4D Certificate of Completion
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
For Small Language Models and Edge AI, 4D configures the scenarios, evidence, and final deliverable—a defensible solution design and evaluation plan—around the client’s operating reality. The facilitator challenges participants’ decisions and leaves the team with reviewed work products, not only presentation notes. Third-party certification, regulatory approval, and guaranteed compliance are never implied.
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