From team alignment to infrastructure choices, here’s how to lay the groundwork for efficient, secure AI training.
This article is the second installment in our two-part series on building smarter, business-ready AI.
In Part 1, we focused on the importance and benefits of training AI models on your own data. This article will focus on practical steps to take before model training.
To harness AI’s full potential, it’s critical to train models to fit the data needs of your company. But training customized AI can be daunting. With all the different types of models, budgetary concerns and set up required, many organizations delay the implementation of domain-trained AI or simply rely on general-knowledge foundation models. But that means they lose out on the many potential benefits of AI, such as technical chatbots trained on product data or tailored financial risk models.
The alternative, diving right into training, can be tempting, especially if your organization has a mass of optimizable data or works with complicated regulations that AI can streamline. However, rushing into training before aligning your company data, infrastructure and goals can be a crippling mistake, leading to inefficient workflows, mismatched information and valuable time down the drain. Before you move, it’s important to have a plan.
Here’s what to get right before you hit “train”.
Step 1: Align teams and objectives
Ensuring that all stakeholders are on board with your AI training initiative is crucial to determining the specific AI goals for your organization. Include people from application development, data science, IT infrastructure and operations, compliance and the executive team. Each department will likely have specific needs or expectations for how they want to use AI. Having all stakeholders meet and agree on how to move forward ensures that no detail is left unaddressed.
It might be difficult to agree on common objectives with your team, especially if stakeholders span multiple regions and interests or have a variety of technical backgrounds. To help drive consensus, ask specific and actionable questions to get to the root of each person’s needs and obstacles: What do you want AI to do for your department or your application? What processes do you want to apply it to? What challenges do you foresee in this project?
Also important are questions around the exact scope of the project: Are you fine-tuning the model parameters or simply adding references to relevant external data to improve an existing foundation model? Are you targeting inference accuracy or operational automation? How will you validate model performance?
Next, build out processes for ongoing training and continuous improvement as your business evolves. For instance, how frequently will the model be updated? Who will be responsible for driving the updates? Creating new workflows can be a challenging task, but assigning responsibilities right from the beginning will streamline efficiency. In addition, creating and updating thorough documentation of the process and agreed-upon goals will ensure that everyone has a central source of truth as a reference.
Consider best practices for security and governance, including contingency plans, and build responsible AI frameworks from the start. How will you assess and mitigate bias? How will you maintain transparency and explainability? Each of these checkpoints will be crucial for situations that may arise once your AI model is deployed, so it’s important that all team members understand the plans and frameworks and can help ensure that the outcomes are what the organization wants.
Step 2: Get your data house in order
Gather all necessary data
Now that your team is aligned on objectives, it’s time to identify the right data sources. That requires a data inventory, where you map out all the sources of information across the organization. These may include customer logs, internal documentation, support tickets, financial records, and so on. To determine the correct data sources, consider the goals you outlined in the previous step. What did your team agree was the primary purpose of the AI model? What questions would it answer? Who would it serve? If your model is internal facing, gather any internal documentation or help tickets that might be needed to train from. If your model is meant to answer technical questions, collect product sheets, website data or sales information. The main objective is to use data that accurately captures how your organization actually operates.
Assess data quality
But collecting data isn’t as simple as scooping everything into a warehouse. Proprietary datasets are often messy, siloed or inconsistent across departments, and your model will only be as good as the information it’s fed. You’ll need to assess data quality in regard to accuracy, completeness and relevance. Accuracy refers to whether the data is correct, such as if the values are true or if labels are consistent across records. Completeness means there are no missing fields and there is adequate coverage of all necessary variables so that your model isn’t misled. Relevance refers to how useful the data is to the main problem being addressed. Is it useful and within the right context? All three pillars of data quality are needed to ensure your model is performing at peak efficiency.
Clean data
To avoid the pitfalls of inaccurate, incomplete or irrelevant data, focus on standardizing data formats before consolidation (such as CSV, SQL, or DataFrame) and implementing governance policies that define what data can and cannot be used. Done right, gathering proprietary data is less about volume and more about curation—selecting the right data, cleaning it, and ensuring it reflects the realities of the business. That foundation is what turns an off-the-shelf model into one that delivers differentiated, enterprise-grade intelligence.
Cleaning data entails tasks such as identifying and filling in missing values, removing duplicate data, standardizing time formats and numerical values, fixing inconsistencies and errors and detecting and handling outliers. Data scientists, engineers and analysts typically do this work, either using customized scripts, existing data pipelines with frameworks, data prep platforms or built-in AI/ML tooling.
Ensure data governance
Part of cleaning your data is managing sensitive data by bolstering data governance and privacy protocols, especially if you’re in a regulated industry. This means defining ownership of each data set, refining access controls and tracking data sources, as well as confirming any data retention policies that need to be clarified. Depending on your industry, anonymization of data and verifying regulatory compliance will also be crucial.
Split data into different sets
To train and evaluate an AI model fairly, the cleaned dataset is divided into three groups:
-
-
- Training set – Typically 70% or 80% of the available data, which is used to teach the model
- Validation set – About 10–15% of the data, used during training to tune hyperparameters
- Test set – The remaining 10–15%, which is held back to evaluate the model’s performance on unseen data
-
Splitting and using your data in this way prevents “leakage,” which is where your model simply memorizes the training data instead of learning to generalize.
Step 3: Choose the right infrastructure
Training AI models requires frameworks and compute power that can keep up, and today you have multiple options to choose from. GPU-based infrastructure is typically the most popular choice for its parallel computing capabilities, which means it can execute thousands of operations simultaneously. The most impactful issue, however, particularly for small to medium-sized businesses with limited budgets, is that while GPUs are ideal for the intensive operations that AI training requires, they are also very expensive, especially at scale.
When considering AI training infrastructure, you also have options and your decision will likely be based on your AI goals, costs, need for data privacy, and existing frameworks.
On-premises training
Training AI models physically on-site ensures that you have full control of your data and user access, which can eliminate the headache of potential privacy breaches. With increasingly rigid government and industry regulations and evolving data sovereignty policies, on-premises training can be a great asset.
However, there are trade-offs as well, and the biggest one is price. Even if you already have some existing infrastructure in place, you will still need to consider not only the number of GPU clusters you’ll need, but also all the required cooling systems, backup systems, maintenance costs and high-capacity storage.
Cloud platforms
Cloud GPU instances allow you to avoid the logistical complications that come with on-premises training. Renting cloud GPUs comes with much lower upfront costs (because you don’t have to purchase all the hardware), enables you to use the latest features and capabilities offered by your cloud provider, and eliminates worries about managing infrastructure. With this option, you can focus on working and achieving your AI objectives, rather than administrative or IT concerns.
However, in the long run, training AI in the cloud really isn’t less expensive. You’ll still require the same number of GPUs, even if they’re located elsewhere, resulting in monthly workloads and rental charges that can accumulate very quickly. If you require a long-running AI model with repeated training, renting GPUs may actually begin to crush your budget, ultimately surpassing the cost of an investment in your own infrastructure.
In addition, your access to GPU instances in the public cloud can fluctuate based on demand. The GPU types you’re looking for may not be available when you need them, leaving you with limited options. And putting your proprietary data in the cloud means it’s constantly exposed to the risk of security compromise. Not to mention that some sensitive data sets, like those in healthcare, finance or government, are often legally bound to stay on-premises and can’t be moved externally for cloud training.
Hybrid solutions
A hybrid approach may be the best of both worlds, depending on your training needs. With this solution, you can keep sensitive data on-premises for training while taking advantage of the cloud’s GPU-leasing for non-confidential data. For instance, you can train a model in the cloud on non-confidential data, then fine-tune your model on-premises with your sensitive data. More advanced setups also exist, such as federated learning or multi-node distributed training, where the cloud trains on one set of data, on-prem systems train on a different dataset, and then the model parameters are merged.
The downsides to a hybrid solution can include data movement costs in the form of bandwidth and egress fees; consistency and synchronization of how data is aligned, normalized and fed to the pipeline; and operational complexity, with the need for highly specialized people to orchestrate pipelines across environments.
Build the right foundation for AI success
Aligning teams, curating the right data and choosing the right infrastructure are the three essentials of any AI training strategy. But of the three, infrastructure often proves to be the biggest hurdle. Even if objectives are clear and data is well-prepared, training will stall if the compute environment can’t keep up. Enterprises must strike a balance between cost, privacy and performance, whether that means investing in on-premises resources, renting GPUs in the cloud or orchestrating a hybrid approach.
This is where Phison’s aiDAPTIV provides a powerful advantage. By extending GPU VRAM with specialized flash memory SSDs, aiDAPTIV+ allows organizations to train larger models locally without needing massive GPU clusters or exposing sensitive data to the cloud. It delivers the speed and scalability AI training demands while lowering costs and maintaining strict data privacy.
The message is clear: Don’t let infrastructure be the bottleneck. With careful planning and the right tools, your organization can build an AI foundation that is not only aligned and data-driven, but also powerful enough to support innovation at scale.
Want to dive deeper into the economics and infrastructure behind GPU-powered AI? Download our free ebook on GPU processing for AI training and see how to balance cost, performance and scale: https://phisonaidaptiv.com/resources/aidaptiv-solution-brief/
常见问题 (FAQ)：
Why is preparing data and infrastructure important before training AI models?
AI training depends heavily on the quality of data and the availability of compute resources. Without proper preparation, organizations risk training models on inconsistent datasets or running workloads on infrastructure that cannot scale.
Preparation ensures that teams align on objectives, datasets are curated and cleaned, and compute environments are capable of supporting AI workloads. When these elements are coordinated early, organizations reduce training inefficiencies and accelerate deployment of reliable models.
What teams should be involved in an AI training initiative?
AI initiatives typically require collaboration across multiple departments. Data scientists define model architectures and training pipelines. IT infrastructure teams manage compute resources and storage systems. Application developers integrate AI outputs into products or services.
Compliance and governance teams ensure the use of data aligns with regulatory requirements, while executive leadership helps prioritize business objectives. Cross-functional alignment ensures AI initiatives solve real operational challenges rather than isolated technical experiments.
What types of data are typically used to train enterprise AI models?
Enterprise AI models often rely on proprietary datasets that reflect real business workflows. Examples include customer support logs, product documentation, internal knowledge bases, operational metrics, financial records, and transaction histories.
The goal is to train models using data that accurately represents the organization’s processes. When AI systems learn from real operational data, they can deliver more precise insights, automate workflows, and improve decision-making across departments.
How should organizations evaluate data quality before training AI?
Data quality should be assessed using three key factors: accuracy, completeness, and relevance. Accuracy verifies whether records are correct and labels are consistent. Completeness ensures datasets contain sufficient coverage of the variables needed for training.
Relevance determines whether the data actually supports the model’s objective. Even large datasets can degrade model performance if they include outdated or unrelated information. Effective AI pipelines focus on curated, high-quality datasets rather than raw volume.
Why do AI datasets need training, validation, and test splits?
Separating data into training, validation, and test sets helps ensure model performance is evaluated correctly. The training set teaches the model patterns within the dataset. The validation set is used during training to tune hyperparameters and optimize model performance.
The test set remains untouched until final evaluation. This prevents the model from memorizing the training data and instead measures its ability to generalize to new, unseen information.
What infrastructure is typically required for AI model training?
AI training requires compute infrastructure capable of processing large datasets and executing thousands of parallel operations. GPU-accelerated environments are commonly used because they significantly accelerate deep learning workloads.
In addition to compute, organizations also require high-performance storage, efficient data pipelines, and networking infrastructure to move large training datasets quickly between systems.
Should organizations train AI models on-premises or in the cloud?
The decision often depends on cost structure, data sensitivity, and workload duration. Cloud environments allow organizations to quickly access GPU resources without purchasing hardware. However, long-term training workloads may accumulate significant rental costs.
On-premises infrastructure provides full control over sensitive datasets and eliminates recurring GPU rental fees but requires higher upfront investment. Many organizations evaluate both options before selecting a training environment.
What are the advantages of a hybrid AI training approach?
Hybrid AI training combines on-premises infrastructure with cloud-based compute resources. Organizations may train initial models using cloud GPUs and then fine-tune them locally with sensitive proprietary datasets.
This approach allows enterprises to scale compute resources when needed while maintaining control over regulated or confidential information. However, hybrid environments require careful orchestration of data pipelines and infrastructure management.
How can storage technology improve AI training performance?
AI training often requires large datasets that exceed the memory capacity of GPUs. High-performance storage solutions can help address this limitation by accelerating data access and enabling larger training workloads.
Optimized storage architectures ensure datasets are delivered to GPUs quickly, minimizing idle compute cycles and improving overall training efficiency.
How does Phison aiDAPTIV help organizations train AI models more efficiently?
群联的 爱达普替夫 architecture extends GPU memory capacity using high-performance SSD storage. This approach allows AI workloads to access significantly larger datasets without requiring massive GPU clusters.
By expanding GPU VRAM with flash-based storage, aiDAPTIV enables organizations to train larger models locally while maintaining low-latency data access. This reduces infrastructure costs, improves scalability, and allows enterprises to keep sensitive data within controlled environments rather than exposing it to public cloud systems.
简体中文 
English 
繁體中文 
日本語 
Deutsch 
한국어