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HCSA-Presales-AI V1.0: From AI Use Case to Infrastructure Conversation

H19-465 V1.0 is indexed as HCSA-Presales-AI V1.0. Available reference material places its scope across AI fundamentals, large-model concepts, computing platforms, training and inference, servers, AI storage, networks, and data-center requirements. That breadth reflects the presales role: translate an AI initiative into infrastructure and solution requirements without pretending that every project is the same type of model workload.

The right preparation sequence starts with the business use case and follows the data and model lifecycle. Ask what the system is expected to do, how accuracy or usefulness is measured, where data originates, whether the workload trains or only infers, what latency and throughput are required, and what security or governance constraints apply. Infrastructure choices should be consequences of those answers.

Separate AI business value from the technology used to deliver it

A customer may ask for AI when the real goal is document automation, forecasting, recommendation, visual inspection, conversational support, anomaly detection, or code assistance. Presales should identify the decision or workflow being improved and the metric that defines success. Without that, an impressive model demonstration can become a project with no measurable business outcome.

AI fundamentals provide useful vendor-neutral context for distinguishing machine learning, deep learning, generative AI, and common use cases. Use those concepts to clarify the problem before discussing Huawei infrastructure.

Training and inference create very different infrastructure patterns

Training can require large datasets, high compute utilization, fast interconnects, checkpointing, distributed coordination, and long-running jobs. Inference may prioritize response latency, concurrency, model serving, cost efficiency, and availability. Some customers do both; others consume a pretrained model and only need inference capacity. Presales should identify which lifecycle stages are in scope.

Create two architecture sketches for the same model: one for training and one for production inference. Mark compute, storage, network, orchestration, monitoring, and security dependencies. This makes it easier to see why a single sizing number such as “number of accelerators” is not enough to describe an AI platform.

Data pipelines can constrain AI more than raw compute capacity

Models need data that is available, clean, appropriately labeled or structured, and governed. Large training jobs can stall when storage throughput or data preprocessing cannot feed compute fast enough. Inference systems may need feature data, vector stores, databases, or document repositories close to the serving layer. Presales should ask how data moves before sizing accelerators.

The broader machine-learning workflow in professional machine-learning engineering is useful context. Map ingestion, preparation, training, evaluation, deployment, monitoring, and retraining to infrastructure dependencies. That prevents the AI proposal from focusing only on the most visible compute component.

Large-model discussions should include memory, interconnect, and serving behavior

Large models introduce practical questions around parameter size, memory footprint, parallelism, training duration, model checkpoints, quantization, batch size, and inference concurrency. An associate presales candidate does not need to derive every distributed-training equation, but should understand why model scale changes compute, storage, and network requirements.

Scaling machine-learning models provides a useful cross-platform reference for distributed workload thinking. The transferable lesson is that scaling is a systems problem: compute, data, network, orchestration, and failure recovery have to grow together.

Network design matters because distributed AI moves large amounts of data

Distributed training can exchange gradients or parameters at high rates, while inference clusters may need low-latency service connectivity and large model downloads. East-west traffic can therefore become an important design constraint. Presales should ask whether workloads are single-node, multi-node, multi-rack, or geographically distributed and what network behavior the framework expects.

Network planning should also include management, storage access, user traffic, and security boundaries. A high-speed fabric does not remove the need for segmentation, observability, or failure isolation. The AI system must remain operable when one server, link, or process fails.

Storage should be mapped to datasets, checkpoints, models, and production data

AI environments can contain raw datasets, prepared datasets, temporary training shards, checkpoints, model artifacts, logs, and inference inputs. Those data classes have different performance, durability, retention, and sharing needs. Presales should avoid treating “AI storage” as one undifferentiated capacity pool.

For each data class, record read/write pattern, size, growth, sharing, retention, security, and recovery importance. A checkpoint store may prioritize throughput and restart speed; model artifacts may need strong version control and distribution; sensitive training data may require tighter access and retention rules.

Responsible AI should appear in discovery before deployment decisions

AI projects can create risks around privacy, bias, unsafe outputs, intellectual property, explainability, and inappropriate automation. Presales is not the organization’s ethics board, but it should ask who owns model approval, what data is permitted, what human review is required, and how outputs are monitored. These requirements can influence deployment architecture and data boundaries.

Responsible AI practices offer useful grounding. Translate those principles into concrete project questions: what should the system never do automatically, what evidence is logged, how are users informed, and how are problematic outputs escalated?

Generative AI adds retrieval, prompt, safety, and model-selection decisions

Generative AI projects may combine foundation models with retrieval, enterprise data, prompt templates, guardrails, evaluation, and application logic. Presales should understand whether the customer needs a general model, domain adaptation, retrieval-augmented generation, fine-tuning, or simply an application consuming an existing service. Those choices change cost, data exposure, and infrastructure demand.

The overview of generative AI integration is useful as a vendor-neutral concept reference. Focus on the architecture pattern rather than the product: model endpoint, enterprise data, orchestration, security, evaluation, and application experience.

A good AI presales handoff states uncertainty and validation work explicitly

AI sizing is sensitive to model, framework, precision, batch size, sequence length, dataset, concurrency, and service-level targets. Presales should resist unsupported precision. Instead, document assumptions, propose a proof of concept or benchmark when needed, and define the metrics that will decide whether the design is acceptable.

Viewed in the context of Huawei certifications, H19-465 should build the habit of connecting AI requirements to compute, network, storage, data-center, and governance decisions. The final proposal should say what is known, what is estimated, and what must be measured before scale-out. That is more credible than claiming one configuration is universally “AI-ready.”

Finish with two contrasting cases: a computer-vision inference service at the edge and a centralized large-model training environment. For each, map data, compute, storage, network, security, operations, and success metrics. The differences between the cases will reveal whether the underlying AI infrastructure concepts are actually understood.

Because AI products and model practices are changing quickly, keep all model-size, accelerator, software, and platform-specific claims in a current-reference layer. Use H19-465 V1.0 for architecture and presales reasoning, then validate release-sensitive facts before using them with a customer.

Cost modeling should separate development experiments from steady-state production. Training clusters may run intensively for a limited period, while inference services can create continuous demand. Storage, networking, licensing, power, and operations also contribute to cost. A proposal should show which costs scale with model training, which scale with users or requests, and which remain fixed regardless of utilization.

Evaluation is a first-class architecture requirement. Define how the customer will measure model quality, latency, throughput, safety, and business usefulness before selecting infrastructure. Different models or serving strategies may meet different targets, and a cheaper model can be the better solution if it satisfies the actual acceptance criteria with lower operational cost.

Model lifecycle governance should include versioning, approval, rollback, and monitoring. When a model changes, the organization needs to know which application version uses it, what evaluation supported promotion, and how to revert if quality degrades. Presales should identify whether the proposed platform supports those workflows or requires integration with external MLOps tooling.

Security should follow the AI data path. Protect training data, model artifacts, prompts, retrieval sources, service credentials, logs, and administrative interfaces. Consider whether sensitive information can appear in prompts or outputs and whether model endpoints are exposed to untrusted networks. AI infrastructure is still infrastructure and should be integrated into the organization’s identity, network, logging, and incident-response controls.

Availability requirements differ between experimentation and production inference. A research job may tolerate restart after a node failure, while a customer-facing assistant may require continuous serving across failures and maintenance. Clarify the service class of each workload before adding expensive redundancy everywhere or, conversely, under-protecting a production service.

Proof of concept should use representative data and real success metrics. A small demonstration dataset may hide storage throughput, memory, or accuracy problems that appear at scale. Define what scale factor can be extrapolated safely and which characteristics require full-size testing. Presales should present uncertainty transparently instead of converting a successful demo into an unsupported production guarantee.

Data sovereignty and locality can constrain AI architecture. Customers may require certain datasets, prompts, model artifacts, or logs to remain in a country, region, or private environment. Presales should identify those restrictions early because they can change whether cloud services, centralized training, remote support, or cross-region recovery are acceptable.

Observability should cover both infrastructure and model service behavior. Monitor compute utilization, memory pressure, network throughput, storage latency, queue depth, request latency, error rate, and model-quality indicators where applicable. Infrastructure health alone cannot tell operators whether an AI application is producing acceptable results.

Capacity plans should include model evolution. A system sized for one model may become constrained when the organization adopts larger contexts, higher-resolution inputs, more concurrent users, or a larger model family. Document the scaling trigger and the expansion path so the initial design can evolve without a complete replacement.

Presales should also define what happens when model demand exceeds capacity. Queueing, autoscaling, admission control, graceful degradation, or request prioritization may be preferable to letting the service fail unpredictably. Capacity management is part of user experience, not only an infrastructure concern.

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