Find your focus
Identify the capabilities most relevant to your role, team, and business priorities.
WEREP ACADEMY · FOUNDATIONS TO PRACTICE
AI for Work: Practical & Responsible Use
Use AI for business productivity while comparing cloud, local, private-cloud and hybrid deployment choices, with human review and data controls built into the workflow.
PEOPLE + PROCESS + TECHNOLOGY WE-REP
COURSE OVERVIEW
Build capability with purpose.
Compare managed API families such as GPT, Claude and Gemini with self-hosting candidates including gpt-oss, Qwen, Gemma, Granite, selected Mistral open-weight variants and Llama. Match the specific model to the task, hardware and license; open weights do not mean every model is open source or suitable for every commercial use.
CAPABILITIES YOU CAN DEVELOP
What you’ll learn to do.
- Compare cloud, local, private-cloud and hybrid options against business needs
- Distinguish model families, hosting tools and model-specific licensing
- Identify data access, retention, network and human-review controls
- Use evaluations to choose models and balance quality, latency and total cost
Who it’s for
Business professionals, coordinators and team leads exploring practical AI use.
Prerequisites & delivery
Basic digital literacy; use only approved tools and non-sensitive exercise data.
Dates, instructor, fees, and enrollment conditions are confirmed with each published cohort.
WEREP / CAPABILITY OVERVIEWAI
01Context→
02Task→
03Constraints→
04Review
Areas of focus
01Business use cases, prompt design and output limitations
02Managed API and self-hosted model families
03Deployment architecture, hardware and operational ownership
04Data boundaries, access controls, retention and auditability
05Task-based evaluation, smaller-model routing and controlled escalation
06Caching, usage budgets and total cost of operation
Discuss this courseVALUE FOR YOUR ORGANIZATION
Connect professional growth with business needs.
Plan adoption around measurable quality, privacy and total operating cost. Start with a smaller model that meets evaluation criteria, escalate harder tasks when needed, and assess caching, usage limits, infrastructure, maintenance and human review costs rather than assuming local AI is free.
FROM CONCEPT TO CAPABILITY
Plan your development.
Choose a pathway
Connect your experience and development goals with the right training area.
Shape the approach
Discuss team needs, delivery preferences, and any required software access.
Build capability
Develop skills that support your responsibilities and longer-term professional growth.
Model licenses, capabilities and hosting requirements must be checked for each selected version. Local or private-cloud hosting is not automatically private: network access, telemetry, retention and integrations still require review. Cohort availability and software access are confirmed separately.
Professional and product references
AI FOR BUSINESS · DEPLOYMENT & COST
Choose the right model.
Choose where it works.
Understand cloud, locally hosted, and hybrid AI—and the business decisions behind a useful, sustainable implementation. Model capability and hosting location are separate choices.
Cloud AI
Use a provider-operated service.A practical starting point for variable demand and access to managed model capabilities. Budget for subscriptions or usage, integrations, data handling, and service dependencies.
Local / on-premises AI
Run a suitable model on hardware your business controls.Useful when workload, latency, or data-control needs justify owning the infrastructure. Your team manages capacity, updates, access, monitoring, and recovery.
Hybrid AI
Route work between private infrastructure and approved cloud services.Keep suitable tasks local and send approved workloads to cloud models when needed. Establish clear routing rules, quality thresholds, and data boundaries.
A private cloud is still cloud infrastructure: it may use dedicated resources, but does not necessarily keep data on your premises. Downloadable weights do not automatically mean unrestricted open-source licensing. Check the exact model release, commercial terms, and deployment requirements.
Different capabilities. Different business needs.
Language & reasoning
Draft, summarize, classify, and support analysis—with human review.
Coding models
Assist developers with code understanding, generation, and testing.
Embedding models
Support semantic search and retrieval of relevant business information. Retrieval is not the same as retraining the model.
Multimodal models
Work with supported combinations of text, images, audio, or other inputs. Capabilities vary by model.
Model names you’ll encounter
Examples to help frame the conversation, rather than a ranked shortlist. Availability, licensing, and supported hardware change; review the linked vendor documentation before selecting a release.
| Model family | Deployment | Business context |
|---|---|---|
| GPT · OpenAI ↗ | Managed cloud | General business assistance, reasoning, and multimodal applications. |
| Claude · Anthropic ↗ | Managed cloud | Writing, analysis, coding, and document-centered workflows. |
| Gemini · Google ↗ | Managed cloud | Multimodal assistance and applications using Google’s model services. |
| gpt-oss · OpenAI ↗ | Self-hosted / hosted by a provider | Open-weight reasoning models, including 20b and 120b; hardware needs differ significantly. |
| Qwen ↗ | Selected open-weight variants | General language, reasoning, and coding options across multiple model sizes. |
| Gemma · Google ↗ | Open-model deployment | A family of models for custom applications, with local and managed deployment paths. |
| Granite · IBM ↗ | Open-model deployment | Enterprise-oriented model families for customized business applications. |
| Mistral ↗ | Managed cloud + selected open weights | Choose between hosted services and self-deployable variants according to the exact release. |
| Llama · Meta ↗ | Downloadable model variants | A model family available under release-specific community licensing and use conditions. |
PEOPLEApproved business application
CONTROLIdentity, permissions & routing
EXECUTIONLocal model or approved cloud
Retrieve only the documents the user is authorized to access. Apply output review, redacted monitoring, and retention controls across both paths. Local inference alone does not make an application private or compliant.
Bringing local AI into a business
Define the use case
Start with a bounded task such as internal document search, draft preparation, or classification. Set quality, response-time, and data requirements.
Size and secure the platform
Assess model size, memory, context length, and concurrent users. Evaluate a runtime such as Ollama or an inference server; add authentication, network restrictions, and controlled storage.
Pilot against real requirements
Test approved representative data, verify document permissions and response quality, and compare local, cloud, and hybrid operating costs.
Operate and improve
Assign an owner for model updates, patching, access reviews, recovery, capacity, and quality monitoring. Expand only when the pilot meets agreed criteria.
CONTROL COST WITHOUT LOSING QUALITY
Compare total cost per useful outcome.
Local AI may reduce recurring API spend for suitable, consistently used workloads. It can cost more for occasional use, demanding models, or teams without operational capacity. Compare the same task, quality threshold, and workload.
Infrastructure or API usage+Software, storage & power+Operations & human review=Total operating cost
- Right-size the model: use a smaller capable model for routine work and escalate complex tasks when justified.
- Reduce avoidable usage: limit unnecessary context and output, reuse approved results where appropriate, and use supported caching or batch features.
- Keep capacity productive: measure utilization, concurrency, latency, and idle hardware costs before expanding.
- Include setup and rework: amortize hardware and implementation costs; count failed responses and review effort when calculating cost per completed task.
Potential savings must be demonstrated in a pilot; no fixed savings percentage or zero-cost operation is promised.
INVEST IN WHAT COMES NEXT