Machine Learning: Key barriers to success and strategies to overcome them

Limited resources and lack of technical talent

One of the biggest challenges in implementing machine learning is the shortage of skilled technical talent.

Designing models, training them with relevant data, and deploying them into production is no simple task. It requires multidisciplinary teams and expertise that many organizations have not yet developed in-house.

Adding to this, a robust infrastructure is essential to train complex models, which raises the barrier to entry. In this context, leveraging cloud-based preconfigured platforms and specialized technology partners can reduce development times and accelerate adoption.

Disconnected tools and fragmented processes

In many cases, data scientists use tools that differ from those used by developers or business analysts.

This disconnect creates information silos, slows down iteration cycles, and limits cross-functional collaboration.

The solution lies in unifying workflows within collaborative environments where model development, validation, and monitoring can be handled in an integrated way. This not only boosts efficiency but also enables scalable and controlled solution deployment.

Lack of governance and responsible AI strategies

The pressure to deliver quick results can lead organizations to skip fundamental AI governance practices.

Who oversees the training data? How is the model’s behavior audited? What mechanisms are in place to prevent bias or ensure explainability? A lack of clear answers to these questions can result in reputational, legal, or ethical risks.

That’s why building responsible AI strategies is essential. These should include model traceability, explainability, continuous auditing, and regulatory compliance. Incorporating these pillars from the design phase is key to ensuring long-term sustainability.

High infrastructure costs

ML models require significant processing and storage capacity—especially deep learning and foundation models.

Attempting to scale these solutions using traditional data centers is often inefficient and expensive. Public cloud emerges as a powerful alternative, offering on-demand access to optimized instances for training and inference, along with preconfigured models that save time and reduce total cost.

This elasticity is particularly valuable in experimental environments, where quick and flexible cycles are essential.

Lack of repeatable workflows

Without a clear MLOps (Machine Learning + Operations) practice—based on DevOps principles—many models remain trapped in notebooks and never reach production.

The absence of continuous integration, automated validation, version control, or monitoring can degrade model performance over time.

Implementing a strong MLOps culture transforms isolated experiments into sustainable enterprise solutions. This includes automated pipelines for training, testing, deployment, and real-time monitoring, supported by metrics that enable rapid iteration and agile improvement cycles.

How to overcome these barriers

The good news is that there are concrete ways to overcome these challenges and bring ML to production successfully:

• Adopt platforms that integrate development, training, debugging, and governance tools in a single visual environment.

• Implement MLOps practices, including workflow automation, CI/CD, and full model traceability.

• Scale flexibly in the cloud with ML-optimized instances to reduce costs and improve performance.

• Promote responsible ML use, with built-in bias detection, data protection, explainability, and compliance mechanisms.

• Leverage ready-to-use pre-trained models (FMs) like those available on Amazon Bedrock to accelerate implementation without starting from scratch.

Conclusion

At Nubiral, we help organizations overcome these challenges with end-to-end solutions that combine technology, strategy, and applied AI expertise.

Because for ML to move beyond a promise and become a competitive advantage, these barriers must be addressed from the very beginning. With a clear strategy, the right tools, and a scalable mindset, companies can turn data initiatives into tangible and sustainable results.

Want your ML projects to go from idea to real business value? Let’s talk—book your meeting today!

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