Eduardo Ordax

The reality is no qualification is needed to start jumping into the AI space, however I would recommend some key aspects that will help to succeed: Software Development & engineering skills, DevOps, Data Science Foundations, Analytics, platform architecture and could, lot of Cloud.

In my personal opinion, the most successful folks in this field are the ones with a technical background and deep understanding of the business. In the tech side you need to know the foundations of ML, some statistics and preferably how to code in python. On the business side, you need to understand how you can leverage AI to improve the current processes.

In Summary, AI is just maths…. A bit more elaborated answer would say AI is the art of solving business problems by using data and a little bit of “magic”.

To develop business skills in your AI career, focus on understanding the business problems AI aims to solve by engaging with stakeholders and analyzing the strategic goals behind these problems. Study successful AI use cases to learn how AI creates value and efficiencies. This understanding will help you align AI solutions with business objectives and enhance your professional value.

To scale your AI solution, ensure its architecture supports scalability, possibly through cloud services. Optimize algorithms and expand data handling capacities to manage larger datasets. Automate model retraining and updates for sustained accuracy. Address integration with existing systems and plan a phased rollout to minimize operational disruptions. Invest in team training for effective management of the scaled solution. Scaling involves both technological enhancements and strategic resource planning to align with ongoing business goals.

To test your AI solution, begin with unit testing to assess individual components, followed by integration testing to ensure they work together effectively. Utilize real-world data scenarios to evaluate practical performance and conduct user acceptance testing for feedback on usability. Monitor for biases and ethical concerns. Refine your solution based on these tests and feedback to improve reliability and user satisfaction ahead of full deployment.