How to implement an ML architecture without failing in the attempt

📌 Are you interested in automation, AI, etc? You are in the right place. At SIXE we are going to tell you how to set up a ML architecture avoiding the most common mistakes.

Machine Learning (ML) is no longer the future, it is the present. Companies from all sectors are betting on artificial intelligence to improve processes, automate tasks and make smarter decisions.

But here comes a reality check that you may not want to hear.

Most ML project models fail

🔴 80% of ML models never make it to production.
🔴 6% of companies are investing in training their team in AI.
🔴 Many infrastructures are not ready to scale ML projects.

And therein lies the problem. It’s not enough to have powerful AI models if the infrastructure they run on is a shambles. If your architecture is not scalable, secure and efficient, your ML project is doomed to failure.

Here’s how to avoid these mistakes and design a Machine Learning infrastructure that really works.

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Stop reinventing the wheel: use what you already have

One of the most common mistakes is to think that you need a completely new infrastructure to implement ML. False.

Many companies already have underutilized resources that they can leverage for Machine Learning:

✅ GPUs with spare capacity (often only used for graphics tasks).
✅ Underutilized servers that can be assigned to ML workloads.
✅ Access to public clouds that could be better optimized.

📌 Exclusive advice from SIXE: Big companies will sell you that you need to buy and buy. A efore spending on more hardware or hiring more, analyze what you can optimize within what you already have. If you don’t know how, we can do it for you.. We perform audits to make your infrastructure greener and make the most of your resources. Spend less, produce more.

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GPUs: Are you taking advantage of them?

Here’s a bombshell: More than 50% of GPUs in enterprises are underutilized.

Yes, they bought powerful hardware, but they are not using it efficiently. Why?

❌ They do not have GPU management tools.
❌ GPUs are assigned to projects that don’t even need them.
❌ Capacity is wasted due to lack of planning.

📌 Solutions you can apply TODAY:

✅ Implements a job manager and GPU scheduler.
✅ Use Kubernetes to orchestrate ML models efficiently.
✅ Adopt a workload scheduler.

If you are thinking of buying more GPUs because “there is not enough capacity”, do an audit first. It is quite possible that you can free up resources and delay purchases. In many cases, it is possible to free up resources and delay purchases by optimizing existing infrastructure. Systems such as AIX, Linux, IBM i, RHEL, SUSE may have untapped capacity that can be reallocated with technical adjustments. At SIXE we audit all these systems to identify opportunities for improvement without the need to change hardware, prioritizing efficiency over investment.

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If you do not automate you are living in the past.

The lack of standardization in ML is a serious problem. Each team uses different tools, processes are not replicable and everything becomes chaotic.

This is where MLOps comes in.

MLOps is not just a term bandied about lately, it is a necessity for ML models to move from the experimentation phase to production without headaches.

📌 Benefits of MLOps:

✅ Automates repetitive tasks (validation, deployment, security).
✅ Reduces human errors in model configuration and execution.
✅ Improves reproducibility of experiments.

If you don’t have a clear MLOps strategy, your team will end up doing the same work over and over again. We recommend you train your team on MLOps to stop wasting time on repetitive tasks. At SIXE, we understand the challenge of ML and we offer a MLOps course with Ubuntu Linux designed to help you implement efficient and scalable workflows.

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Hybrid cloud: The perfect balance between cost and flexibility

The eternal debate between public and private cloud has generated more than one headache in companies. Should you opt for the agility of the public cloud or prioritize the control and security of a private cloud? The good news is that you don’t have to choose. There is an in-between solution that combines the best of both worlds: the hybrid cloud.

❌ Public cloud only: Can be costly and raises security concerns.
❌ Private cloud only: Requires investment in hardware and maintenance.

🔹Use the public cloud for quick experiments and initial testing.
🔹Migrate models to private cloud when you need more control and security.
🔹Make sure your infrastructure is portable to move between clouds, avoiding environment incompatibility.

Thanks to the ability to seamlessly interconnect between environments, the hybrid cloud eliminates vendor lock-in and optimizes operational costs. A hybrid architecture gives you the best of both worlds: agility to innovate and stability to scale.

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ML Security: Don’t wait until it’s too late

Many people think about security when it is already too late. An attack on your ML models or a data breach can have disastrous consequences.

Best practices to protect your ML infrastructure:

✅ Perform at least one annual security audit of your infrastructure.
✅ Implement strong authentication and identity management.
✅ Encrypt data before using it in ML models.

Remember: Security is never enough. The more “layers” of security you have, the less likely you are to be in the news for a data breach ;)

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Training: Without a trained team, how will you manage your infrastructure?

AI and ML are constantly evolving. If your equipment is not upgraded, it will be left behind.

🔹 Training in MLOps workshops.
🔹 Internal learning. Foster a culture of continuous learning within your organization through mentoring, collaborative documentation and practical sessions.

💡 At SIXE we offer MLOps training to help companies build scalable and efficient architectures. If your team needs to get up to speed, we can adapt to your company’s specific needs.

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Don’t waste hours chasing an error

If your ML infrastructure fails and you don’t have monitoring, you ‘re going to spend hours (or days) trying to figure out what happened.

📊 Essential tools for observability in ML:

✅ Real-time dashboards for model and hardware monitoring.
✅ Automatic alerts to detect problems before they become critical.
✅ Detailed logs for process traceability and error resolution.

If you don’t have full visibility over your infrastructure, sooner or later you will have problems.

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Conclusion

Building a scalable and efficient architecture for Machine Learning is not just a technical challenge, but a change of mindset. Leverage your current resources, optimize the use of GPUs and adopt MLOps to automate key processes.

Do you want to design an ML architecture that really works? We can help you.

👉 Contact us and we will help you create a scalable, secure and AI-optimized infrastructure.