Many companies are adopting AI, yet they encounter common, complex challenges when it comes to getting models into production and keeping them there. A global survey suggests that more than half of machine learning projects fail to move beyond the PoC stage into production. This underscores a key reality: the more complex problem isn’t building a strong model—it’s integrating that model into products and workflows and running it reliably. In real-world AI/ML systems, model code is only a small fraction of the whole; the rest consists of data collection and processing, infrastructure, integration, monitoring, and other supporting elements.

Source: McKinsey

McKinsey also points out that over 90% of ML development failures are not due to model quality, but to weak productization and poor integration into operational environments. In other words, to truly leverage AI, you need a strategy that covers everything after model development—data pipelines, infrastructure management, deployment, monitoring, security, and governance.

In this post, we’ll walk through 10 essential things to know when operating AI models. Each section distills practical insights grounded in sources and industry examples so you can tame operational complexity and maximize business value.

1. Data Pipeline Management

Data is the fuel of AI. Without a reliable supply of high‑quality data, even a state‑of‑the‑art algorithm won’t perform well. Model accuracy and trustworthiness depend on the quality of training data and live inputs, and because real‑world data distributions shift constantly, you need continuous collection, cleaning, and management. At a large scale, manual handling hits its limits—automated data pipelines are essential.

If data pipelines are weak, you get “garbage in, garbage out.” Bad or incomplete inputs result in bad decisions. If data isn’t ready on time, deployments get delayed. If data quality deteriorates or data drift (distribution shifts over time) goes unchecked, predictive performance can drop sharply. In high‑velocity domains like financial transaction monitoring, failing to filter anomalies leads to false positives/negatives and significant losses.

A strong data pipeline improves model accuracy and reliability. With clean, consistent data, models learn current patterns, maintain performance, and enhance business decision quality. Well‑maintained pipelines also make it easy to add new sources or modify features so your AI adapts quickly to change.

First, build a continuous, automated pipeline. Automate every step—ingestion, cleaning, feature engineering—and add data quality monitoring to detect anomalies. A Feature Store ensures consistent features as the “fuel” for all models. Manage schemas and metadata to track data lineage, and apply data versioning and access controls to reinforce data governance. This is how you reliably secure the correct data at the right time and maximize model performance.

2. Building an MLOps (Machine Learning Operations) Process

Source: McKinsey

MLOps is the process and culture that productizes models quickly and reliably. Organizations that implement MLOps level up their AI impact—moving beyond a handful of experiments to business transformation and competitive advantage. According to McKinsey, adopting MLOps often marks the difference between “staying in AI experiments” and “changing the business with AI.” A well‑structured lifecycle—from development to deployment and operations—speeds delivery, improves quality and consistency, and enables scaling to many models.

Source: Google Cloud

Without MLOps, even strong models may never reach production. Google notes that the real challenge is not model training, but integrating it into a system you can run continuously. Without automation, manual deployments lead to human error, poor reproducibility, deployment delays, and environment mismatches—for example, training–serving skew, where preprocessing differs between training and serving.

Source: MLOps as The Key to Efficient AI Model Deployment and Maximum ROI

Conversely, effective MLOps shortens cycles, reduces incidents, and improves ROI. With standardized pipelines and automated testing/deployment, teams ship new models and updates faster—improving market responsiveness. MLOps also brings structured impact analysis and monitoring across model/data changes—raising quality and reliability while reducing risk. Ultimately, organizations with solid MLOps use AI not as an experiment but as a core business engine—creating a significant advantage over competitors.

So how do you make it happen?

The key is to apply DevOps principles across ML, connecting development, deployment, and operations as one flow. That means pipeline automation, reproducible experiments, standardized releases, operational monitoring, and flexible GPU infrastructure working seamlessly together.

First, in environments with frequent code and model changes, CI/CD pipelines are essential. When data prep, training, validation, and deployment are expressed as one declarative workflow, pre-/post‑deployment checks and release testing naturally become part of team practice. Once your operational pipeline is established, collaboration improves, release cycles shorten, and quality issues shrink—lowering your overall AI operating cost.

Second, for collaboration and reproducibility, experiment metadata and artifacts must be consistently recorded and shared. If everyone can see which model was trained on which data and where it was deployed, communication costs fall, and auditability rises. Connecting your Model Registry to pipelines is especially effective.

Third, in production, you need to monitor performance metrics, latency, error rates, and data/model drift continuously, with automated alerts → rollback → retraining triggers for threshold breaches. The more tightly monitoring is wired into the pipeline, the faster you respond to degradation.

Finally, GPU infrastructure determines the balance between cost and agility. Pull from a multi‑cloud/on‑prem resource pool, schedule automatically based on workload characteristics, mix spot and on‑demand, and enable auto‑scaling. Use a standardized serving layer (container‑based) and zero‑downtime deployment as defaults to minimize risks from environment mismatches and manual releases.

You can stitch these together with disparate tools. Still, results are often better with an integrated MLOps platform where training, validation, deployment, and monitoring are first‑class connections, and where multi‑cloud GPU optimization and cost visibility are built in.

VESSL absorbs this operational flow, allowing your teams to spend less time on infrastructure and more on model quality and business outcomes.

3. Automating GPU Infrastructure (GPUOps)

In the age of deep learning and large models, GPU infrastructure is a core asset. Training and serving at scale require high‑performance GPUs, but running them internally is hard. With soaring demand, stable GPU operations have become a central challenge. If you don’t operate large GPU clusters efficiently, R&D slows, costs explode, and downtime can become critical. That’s why GPUOps—automating and optimizing GPU operations—matters. With GPUOps, you can allocate limited GPUs to the right jobs at the right time to maximize utilization, letting developers focus on improving models.

If you manage GPU resources manually or fail to optimize, problems follow: idle GPUs drain money, or conversely, shortages delay training and services. Before GPUOps, engineers often spent substantial time firefighting infrastructure issues, hurting productivity and raising costs.

GPU driver conflicts
Library/version mismatches
Organizational issues in resource allocation and usage

Frequent issues like these also undermine service stability. In short, neglecting GPUOps wastes time and money, delays model development, and reduces agility and reliability.

Conversely, automating and optimizing GPU operations yields visible improvements. As seen in Scatter Lab’s case, VESSL’s approach lets engineers focus on core product functions rather than infra firefighting, improving platform completeness and customer trust—contributing to business growth. Automated resource management boosts cluster stability and reduces interruptions, while large‑scale parallelism accelerates training and shortens time‑to‑experiment. Higher utilization means you can do more with the same GPU budget or do the same work for less, driving cost savings.

Source: Google Cloud

For automated GPU management, adopt specialized cluster management and scheduling. With Kubernetes, you can enable auto‑scaling, scheduling, and isolation for GPU workloads. VESSL builds on Kubernetes to dynamically scale ML jobs across diverse GPU environments—unifying all ML workloads. With automated request/return and priority‑based schedulers, you avoid idle gaps and route capacity to urgent jobs. For instance, VESSL Cluster optimizes GPU utilization across the fleet. Finally, build monitoring dashboards to visualize team/project usage, and set quotas and budget alerts to optimize consumption continually. This GPUOps stack reduces operational burden and speeds innovation.

4. Using GPUaaS (GPU as a Service)

GPUaaS lets you flexibly use GPU infrastructure in the cloud, offering cost and scale advantages versus building and running everything yourself. AI projects often experience demand spikes or require top‑tier GPUs (A100, H100)—hard to cover with on‑prem alone. With GPUaaS, you can secure massive GPU capacity on demand, responding elastically to shifts.

Relying only on on-prem GPUs invites under-investment or over-investment risk. When demand surges, you lack capacity; when it dips, expensive GPUs sit idle. Some VESSL customers (e.g., Scatter Lab, LINER, Wanted) adopted GPUaaS to access varied, modern GPUs and prepare for scaling their MLOps. Because GPU tech advances quickly, fixed on‑prem hardware ages into inefficiency. You also bear power/cooling and maintenance staffing costs. Without GPUaaS, you face scale and cost disadvantages—and, in the worst case, experiments stall or projects fail due to infra constraints or budget overruns.

Used wisely, GPUaaS maximizes flexibility and efficiency. You can run large workloads without over‑investing, paying only for what you use. Global clouds (AWS, Azure, Google Cloud) provide the newest GPU architectures, high‑speed networks, and parallel storage, letting you process large AI workloads without performance penalties. They also offer expert support, helping your teams resolve issues quickly.

To adopt with confidence, start by choosing trusted providers. Consider a multi‑cloud strategy to optimize cost. Platforms like VESSL can route jobs to the most cost‑effective GPU across clouds, delivering significant savings. For existing on‑prem, build a hybrid architecture: run baseline workloads internally and burst to cloud at peaks.

Source: https://treinetic.com/cloud-service-providers/

Finally, enforce spending controls and security by monitoring usage and budgets, setting alerts, and applying strict access policies to protect data and model assets. This approach helps you use just enough GPU, economically, while preserving scale.

5. Model Serving & Deployment

Source: VESSL

Model serving is where trained models become live prediction services—the final gate to AI value. Even the best models are useless if not deployed into production, where users and systems can consume them. To realize business value, models must be integrated into products and workflows for real‑time use (e.g., personalized recommendations, forecasts feeding supply chain decisions). That demands a reliable, 24/7 serving infrastructure.

If serving is poorly managed, problems pile up. First, train–serve mismatches cause errors; Google’s guidelines emphasize verifying that the model trained in one environment performs consistently in serving. If preprocessing differs between training and serving, outputs can be disastrously wrong. Second, scalability and latency issues surface under load—without proper auto‑scaling and resource monitoring, time‑outs and downtime can occur. Third, weak deployment pipelines slow releases and make rollbacks hard; manual steps invite errors and delays—hurting agility.

Well‑engineered serving maximizes value. Stable serving delivers real‑time intelligence for better UX and operations. One e‑commerce example: efficient serving enabled personalized recommendations without page‑load delays, boosting revenue. Another company micro‑servitized ML models and implemented CI/CD, significantly shortening model update cycles and improving product innovation speed. With strong serving, you maximize AI ROI and shorten time‑to‑market for new models, capturing opportunities faster. Automated pipelines let you ship even minor improvements quickly, accelerating the experiment–feedback loop (CI/CD/CT).

Source: VESSL

Among many considerations, standardizing and automating the deployment pipeline are the most important. Containerize the serving environment to reduce drift. Package with Docker and deploy via Kubernetes (or Docker Swarm) for consistent behavior across environments. On VESSL, pre‑configured images and CUDA versions remove packaging friction and improve developer experience.

Hardware selection and scaling strategy are crucial, too. Start CPU‑only where viable, move to GPU as latency requirements rise, and enable auto‑scaling. Adopt zero‑downtime release patterns (blue‑green, canary) to swap models without outages. Integrate performance monitoring into the release flow—track latency, error rates, resource usage, and auto‑rollback on regressions.

Finally, build test automation into CI/CD. On each model update, run regression tests on data/outputs to ensure the new version meets or exceeds the previous one before deployment. This yields reliable, flexible serving so you can scale AI services confidently.

6. Scalability

As AI usage expands, scalability is essential. A setup that works for one or two models can fail when you need dozens or hundreds, or when user traffic grows exponentially. To deliver enterprise‑wide value, you must scale technology and operations across the organization and embed AI/ML into core business processes. That requires architecture and operating models that handle extensive data, infrastructure, and talent demands. A model that handles hundreds of millions of daily events might start on one server but must scale to 10 or 100 servers as adoption expands. Otherwise you risk lost opportunities and degraded UX.

Ignoring scalability leads to the trap of scale. Manual processes that worked for a small pilot collapse under broader adoption. Managing tens or hundreds of models by hand is impossible; as data volumes surge, pipelines back up and errors multiply. One global company attempted to operate hundreds of forecasting models across countries and product lines; more than half were retired due to infrastructure and process gaps. Scalability issues also create cost inefficiency—small inefficiencies become exponentially expensive at scale. Without scalability, AI hits a ceiling—or its success becomes a burden, leading to outages and operational failures.

Building for scale lets your AI grow with the business. With sufficient scalability, 10× data or user spikes don’t threaten stability, building customer trust. More data also improves accuracy and the granularity of insights.

Source: Measure and Improve AI Workload Performance with NVIDIA DGX Cloud Benchmarking | NVIDIA Technical Blog

For example, NVIDIA shows that scaling GPU clusters for distributed training can dramatically shorten training time while keeping costs under control. With modular microservices for model serving, you can add new model services easily and scale them independently—expanding AI features in parallel. In short, scalable AI infra becomes a growth engine for the business.

How to do it: Design for scale from day one. Use distributed processing and parallelism. Build data storage on a distributed filesystem or data lake and run training with distributed frameworks (Horovod, Distributed TensorFlow). Turn on auto‑scaling so instances increase with load and shrink when it drops (Kubernetes HPA in the cloud). Microservice architectures let you scale specific models without impacting others. Pair scale with cost management (reservations, spot instances, multi‑cloud price checks) to keep growth economical.

In practice, VESSL + Google Cloud GKE dynamically scales out ML workloads as needed, enabling a flexible lifecycle from training to serving. This cloud‑native orchestration supports global‑scale expansion. Microservices allow independent deployment and scaling; if a single model experiences a traffic spike, you scale just that service. Finally, couple scale with cost controls—reservations, spot, and multi-cloud comparisons—to design a cost-optimal growth path. This way, even at 10× or 100× usage, you can respond stably and economically.

7. Cost Optimization

Operating AI costs money: data infra, GPU training and inference, people, and overhead—all add up, often exceeding initial expectations. GPU cloud costs scale with time and capacity, so without discipline, you risk bill shock. No matter how good your model is, if it isn’t economical, it won’t be sustainable. Cost optimization lets you run more experiments and services under the same budget and reduces financial risk—earning executive confidence.

Source: VESSL

Without cost controls, waste and inefficiency creep in, leaving low‑utilization GPU instances running, over‑provisioning, or using oversized resources. Budgets run out early; projects get delayed or cancelled due to poor cost‑to‑value. Redundant runs and long idle times cause runaway cloud bills. Meanwhile, optimized competitors perform the same work more cheaply, gaining price leverage and investment capacity. In short, cost mismanagement threatens AI’s viability and can trigger pullbacks in AI investment.

Source: VESSL

With rigorous optimization, you deliver more AI value on the same budget. Among VESSL customers (e.g., KAIST, Scatter Lab), multi‑cloud strategies and spot usage cut GPU costs by up to 80%, and the time to develop AI models improved up to 4×, saving hundreds of engineering hours—with savings reinvested into experimentation. This strengthens the business case, enabling hires and larger experiments, and supports scaling to more models on the same infrastructure. Efficient cost management underpins sustainable, long‑term innovation.

Start with clear principles and tools to monitor and optimize continuously:

Right‑size resources. Use CPUs for preprocessing or simple inference; reserve GPUs for work that truly needs them.
Automate allocation. Auto‑stop dev boxes after hours; automatically terminate inactive sessions.
Monitor and alert. Use cost tools (e.g., AWS Cost Explorer) or third‑party platforms to break down spend by service/team and alert on budget thresholds.
Build a cost culture. Share cost reports and surface “cost per run.” When scientists see costs clearly, they design more efficient experiments.

With this approach, you can deliver more with the same budget and run AI sustainably.

8. Model Monitoring & Maintenance

Source: VESSL

AI models aren’t “deploy and forget”—they’re living systems. Over time, data distributions change, and outside conditions shift, causing performance decay. Models that initially performed well can gradually accumulate bias or errors. A demand forecast trained pre‑pandemic, for example, may fail post‑pandemic. You must continuously verify that the model behaves correctly in production and performs as expected. Monitoring isn’t just about accuracy; it supports compliance (e.g., fairness monitoring), system stability, and alignment with business KPIs. Think of it as regular health checks so the AI continues to deliver as intended.

Without monitoring, problems go unnoticed. Fraud patterns evolve while models miss new attacks; false positives rise, and losses mount before anyone realizes. Bias or errors in outputs can harm specific groups, escalating into social and regulatory issues. If data drift occurs and accuracy drops, but periodic evaluation is absent, decision quality degrades slowly. Ultimately, unmonitored models lose trust and may fail abruptly, disrupting services. Many AI failures trace back to poor monitoring. Without monitoring and maintenance, you can’t ensure integrity or validity—initial wins fade or turn into costly incidents.

Source: VESSL

Implementing systematic model monitoring and maintenance maximizes model lifespan and performance. Continuous monitoring is a key mechanism for preventing performance degradation and ensuring models continue to meet their intended purpose. Early anomaly detection enables fast response, preventing serious outages. This also improves service uptime: when degradation is detected, you can proactively switch models or tune performance, keeping the best model version in production.

Continuous monitoring and feedback are essential for maintaining reliability. Connecting model metrics with business KPIs quantifies model impact—useful for executive reporting, ROI calculations, and guiding improvements.

Source: https://moldstud.com/articles/p-understanding-model-retraining-how-to-keep-your-ai-models-up-to-date

Finally, monitoring user feedback and edge cases yields a pipeline of improvement ideas. Integrating a real‑world feedback loop lifts accuracy and satisfaction; models refined with live data can boost engagement and satisfaction, with retention uplifts reaching 30%. When the observe–learn–improve loop runs well, models become more precise over time and your organization’s AI capability compounds into competitive advantage.

A comprehensive monitoring program should include:

Performance monitoring. Define metrics: accuracy/precision/recall, error rates, latency, availability—and business KPIs (revenue lift, churn reduction). Build real‑time dashboards and alert on thresholds.
Automated drift detection. Use statistical tests to detect input distribution changes, and track output distribution shifts over time (e.g., KS test, PSI).
Scheduled re‑evaluation and retraining. Weekly, monthly, or quarterly depending on model criticality. Re‑evaluate on holdout or fresh data; trigger retraining when performance falls below thresholds.
Exception logging. Capture low‑confidence and mispredicted cases; label and fold them into the next training cycle for continuous improvement.
Incident playbooks. On severe anomalies, auto‑rollback to the last good model and launch a root‑cause/response process immediately.

9. Security

Protecting AI models and data assets is critical for corporate trust and legal compliance. Operating AI models often involves handling sensitive data, and the models themselves are core intellectual property. If the data underpinning a model’s decisions is leaked, or if the model service API is attacked and malfunctions, it can lead to massive losses and reputational damage. In fact, the number of cases where careless AI operations have resulted in legal and financial risks is steadily increasing.

A representative example is Italy’s temporary suspension of ChatGPT in 2023 due to personal data concerns. In the US, copyright infringement lawsuits have been filed over AI-generated content, signaling that AI-related regulations are becoming a reality. The EU’s upcoming AI Act includes provisions for fines of up to 7% of a company’s global revenue for violations, which means that failures in security and regulatory compliance can have very serious financial consequences. In short, neglecting security can expose companies to legal, financial, and reputational damage arising from data breaches, cyberattacks, and regulatory sanctions.

What can go wrong:

Data leaks of PII or confidential business data—triggering GDPR/Privacy Law violations, fines, lawsuits, and loss of trust.
Model theft or misuse. Expensive models can be exfiltrated or tampered with (e.g., parameter manipulation, backdoors), leading to poor decisions or incidents.
Attacks on serving infrastructure—DDoS or adversarial inputs—can lead to downtime or incorrect responses.
Bias/ethics issues escalating into legal disputes and reputational harm (e.g., discriminatory outcomes).

A strong security and compliance posture delivers real benefits. Organizations implementing Responsible AI report higher efficiency and cost savings, greater consumer trust, better brand reputation, and fewer AI incidents.

According to McKinsey’s 2025 global survey, 47% of respondent companies experienced at least one negative outcome from the use of generative AI. Another study found that degraded data quality and resulting declines in AI performance translate into an average annual revenue loss of 6%. In other words, neglecting AI security and governance creates short-term incident response and regulatory risks, while also accumulating long-term negative effects on reputation and revenue.

Conversely, there are major benefits to thoroughly managing security and regulatory compliance. Companies that have implemented Responsible AI are reducing risks and building trust, thereby maximizing AI’s potential.

Source: McKinsey

According to McKinsey, companies that invest in Responsible AI report clear outcomes such as improved business efficiency and cost reduction (42%), increased consumer trust (34%), enhanced brand reputation (29%), and reduced AI-related incidents (22%). This shows that only when a robust security and ethical framework is in place can the full benefits of AI adoption be realized.

Strengthening security also increases internal trust in data and model assets, which helps create an organizational culture where various departments feel safe to actively use AI. In addition, proactively meeting regulatory requirements allows firms to respond flexibly to future legal changes and build resilience against legal risk. For example, one financial institution implemented governance to transparently manage model decision logic and data, was recognized as a best practice by regulators, and simultaneously gained customer trust and increased its market share. In this way, a solid security and compliance framework becomes a firm foundation for the sustainability and social trust of AI projects.

From a data security perspective, all data used to train AI models should be controlled with least-privilege access and stored with encryption.

Taking VESSL AI as an example, the company applies Google Cloud’s fine-grained access control when storing datasets and model artifacts on the cloud, so that only authorized users can access critical assets. Data should also be de-identified or masked before training to protect personal information, and techniques such as differential privacy can be introduced when needed to eliminate the possibility of privacy breaches at the root.

From an application security perspective, model API endpoints should be protected with a Web Application Firewall (WAF), and rate limiting, authentication, and authorization should be enforced to block abnormal traffic and unauthorized access.

Model security must not be overlooked. To block adversarial attacks, organizations should implement anomaly detection and filtering on input values and monitor model outputs to catch sudden distribution shifts or outliers. When necessary, adversarial training can be applied to build models resilient to such attacks.

At the platform level, security measures such as network segmentation, access control, and console activity logging for notebook and model development environments help prevent internal threats and accidental data leaks.

Finally, clear internal guidelines and training on AI models and data usage are essential to ensure regulatory compliance. AI ethics requirements—such as privacy, fairness, and explainability—should be applied from the model development stage onward, and legal and security teams should work together to run regular audits and risk assessment processes. Through this multilayered security approach, companies can secure the reliability and safety of their AI systems and build an AI operating framework that remains robust in the face of regulatory change.

10. AI Governance & Compliance

As AI adoption expands, organization-level AI governance, ethics, and regulatory compliance are emerging as essential components.

Source: IBM

AI governance refers to the framework of processes and standards that ensures AI systems and tools are used safely and ethically. Its purpose is to secure fairness, transparency, and accountability across the entire lifecycle—from AI development to deployment and use—while meeting legal requirements.

Governments and regulators around the world are rapidly introducing AI regulations, and without proactive internal governance, organizations risk being unable to respond to these rules, leading to legal sanctions and business disruption. For example, once the EU AI Act is implemented, companies will have to demonstrate the transparency, safety, and non-discrimination of their AI systems; violations may trigger fines of up to 7% of global revenue.

If responsibilities are not clearly defined in advance for when AI systems cause incidents, confusion within the organization will grow when problems occur, and external trust will be seriously compromised. Above all, as the social impact of AI technology increases, public and employee expectations regarding ethical responsibility are rising.

Source: IBM

In a survey by the IBM Institute for Business Value, 80% of business leaders cited explainability, ethics, bias, and trust issues as major obstacles to adopting generative AI, showing how sensitive stakeholders are to responsible AI use. In short, neglecting AI governance and ethical compliance can lead to legal risks, reputational damage, and internal confusion, ultimately harming business performance.

The risks of poorly managed AI are already visible in the real world. As mentioned earlier, the Italian data protection authority (Garante) temporarily banned ChatGPT over privacy concerns, due to a lack of proper legal basis for large-scale data collection. Lack of decision transparency is also a serious problem. In one case, the Apple Card’s credit limit algorithm was accused of discriminating by gender, triggering an investigation by New York financial authorities.

As seen in this example, black-box models with poor explainability are hard to defend when bias allegations arise, and can lead to large-scale investigations and sanctions. Internally, if departments adopt AI independently without a clear AI strategy and governance, it can cause duplicated investments, lack of technology standards, and data management issues, all of which reduce efficiency. Harvard Business Review (HBR) points out that in many organizations, AI is introduced in departmental silos, which actually weakens overall strategy execution.

Source: McKinsey

In McKinsey’s survey, 51% of respondents cited lack of AI-related knowledge and 40% cited regulatory uncertainty as major obstacles—an indication that many organizations, lacking governance, are “unsure what to do and how,” and therefore hesitate or stumble in execution.

In the worst case, ethical incidents can lead to customer and public distrust, directly causing revenue decline and stock price drops. A DataRobot study found that among organizations that experienced AI-bias-related incidents, 62% reported revenue loss and 61% reported customer churn. Technology executives cited loss of customer trust (56%) and deterioration of brand reputation (50%) as their greatest concerns around AI risk. Ultimately, neglecting governance exposes organizations to a triple threat of regulatory sanctions, reputational damage, and internal confusion, putting their overall AI strategy at risk.

On the other hand, by building strong AI governance and accountability frameworks, organizations can proactively manage risks while continuing to innovate with AI. Companies that operate trustworthy AI can expand their business without being shaken by regulatory changes, and by running AI in a transparent and responsible manner, they can boost brand trust and secure competitive advantage.

How, then, should organizations build AI governance?

It is most effective to make decisions and define the approach in a top-down manner. First, leadership should establish AI ethics principles and goals, and then form a company-wide AI governance committee or dedicated organization to lead the governance framework. This governance body should include stakeholders from fields such as data ethics, legal, security, and business units, who together define AI usage policies and responsibilities.

Many companies are already forming AI ethics boards and committees where legal, technical, and policy teams jointly review AI projects and check them against ethical standards. Internal AI usage guidelines should be created to clearly define what types of data and purposes are allowed for AI use, and which uses are prohibited.

Operating a model registry is also important. All AI models that are developed or deployed should be centrally managed, and a model card should be written for each model, documenting its intended use, data sources, performance metrics, bias test results, and responsible owner. This allows the organization to see at a glance who is using which AI model for what purpose, increasing transparency and enabling traceable accountability.

Risk management processes should be standardized and integrated. Models should be categorized by risk level, and high-risk models (e.g., those impacting life and safety or critical decisions involving discrimination-sensitive factors) should be subject to stricter validation and continuous monitoring.

Source: The Fed’s SR 11-7 guidance and AI governance

In the financial sector, for example, the US Federal Reserve’s SR 11-7 guidance requires an independent model validation team to verify models. Similarly, it is advisable to introduce third-party model validation or external audits for important AI models.

Monitoring regulatory changes is also essential. Organizations need to continuously track global AI regulatory developments and proactively update internal policies when new rules are announced. Brazil, China, the EU, Singapore, Korea, the US, and other regions are all advancing different AI regulatory approaches, but they share common principles such as transparency, human oversight, accountability, safety, non-discrimination, and privacy. Companies should align their internal guidelines with these trends and prepare for future legislation.

Education for all employees is equally important. Every staff member should understand the importance of AI ethics and security, and developers should be trained to use Responsible AI tools so that they can put ethical principles into practice in their daily work.

Finally, organizations should establish incident response plans in advance, defining how to communicate and respond to AI-related incidents for each scenario. For example, when harm is caused by an erroneous AI decision, the plan should clearly define who will be responsible for external apologies and remediation, and who will formulate internal measures to prevent recurrence, and when.

AI governance is not something that can be set up once and left as-is; it must be continuously improved. According to McKinsey’s global survey, the average organizational maturity in AI trust is only 2.0 out of 4. This indicates that many organizations still lack sufficient data quality guidelines and incident response plans. Governance levels should be regularly assessed, gaps identified, and deficiencies fixed, while continuously incorporating new technologies and industry standards to maintain an evolving governance framework.

By doing so, companies can build an environment in which AI can be used actively and safely, and position themselves as leading AI organizations that meet both external regulatory and internal ethical standards.

Conclusion

So far, we have looked at ten essential elements that companies must address when operating AI models. A holistic approach across data, infrastructure, processes, security, and governance is required. In summary, it is crucial to build a strong data pipeline and MLOps processes, optimize GPU infrastructure and maintain flexible GPU resource availability (GPUaaS), enable fast and reliable model deployment and scaling, optimize costs, strengthen security, continuously monitor and improve performance, and rigorously enforce governance and regulatory compliance. By faithfully implementing these principles, AI projects that once remained at the initial proof-of-concept (PoC) stage can advance into full-scale operations that generate real business value.

In particular, VESSL AI’s approach to GPU infrastructure management and operational automation offers valuable lessons. VESSL AI has adopted the concept of GPUOps to automatically optimize complex multi-cloud GPU environments and has established a GPUaaS model that allows customers to secure large-scale GPU resources whenever needed. As a result, VESSL AI’s customers can focus on AI model development and operation without the burden of infrastructure management. In practice, they have achieved up to a 4x acceleration in AI model development while reducing cloud costs by up to 80%. VESSL’s platform provides best practices across the ten factors described above through automated GPU infrastructure management, one-click model training and deployment, and pipeline management.

If you want to further streamline your AI model operations, consider adopting VESSL AI’s GPUOps- and GPUaaS-based solutions to experience a new level of operational efficiency and speed. By providing an environment where you can focus solely on AI R&D without worrying about complex infrastructure setup and management, VESSL AI will help accelerate your AI innovation journey. Experience cost-efficient, scalable AI operations firsthand with the VESSL AI platform and strengthen your company’s business competitiveness.

Customers

10 Things Enterprises Must Know When Operating AI Models

1. Data Pipeline Management

2. Building an MLOps (Machine Learning Operations) Process

So how do you make it happen?

3. Automating GPU Infrastructure (GPUOps)

4. Using GPUaaS (GPU as a Service)

5. Model Serving & Deployment

6. Scalability

7. Cost Optimization

8. Model Monitoring & Maintenance

9. Security

10. AI Governance & Compliance

Conclusion

Sources

Wayne Kim

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