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YOU CAN’T BUY AI
Machine Learning is a Process and not a Product Follow this order
There is a great deal of anxiety around the introduction of ML and AI technology across the world. Understandably so! Let’s take a moment to turn back time and think of the age of the printing press, the steam engine, electricity, the transportation age, the first computers. Those were all major shifts in how we interacted with the world and each other. We have now reached the age of data and cloud infrastructure. Each age brought about masses of innovations that fundamentally changed how we operate. Following COVID, the way we work is like none we have known before. On top of all this, we also live in the age of connection. Digital Transformation is not optional anymore. Organisations worldwide are looking at ways to foster innovation, generate new revenue streams, and adapt quickly to market changes and customer needs. Digital transformation is the enabler to these outcomes. This brings us to our headline: Machine Learning is a Process, not a product. Here are our thoughts: You can't buy machine learning Machine learning is not a product that you can buy off the shelf. It is a process that requires careful planning, execution, and maintenance. Data is key Machine learning models need data to learn. You cannot simply dive headlong into the deployment of AI technologies. You need to have a deep understanding and developed process for data collection. This data is used to gain insights into how your business operates. You build a machine learning practice and process This is not a one size fits all kind of thing. You need to build out what it is that you are trying to achieve on your data. Your data is unique to you. Models need to be updated regularly Data changes over time, so your models need to be updated to reflect those changes. You need to have domain experts Machine learning is a complex field, so you need to have experts who understand the domain and can help you build and maintain your models. Models can drift Over time, models can start to make inaccurate predictions. You need to monitor your models and take steps to address any drift that occurs. By following these tips, you can build a successful machine learning practice that will help you improve your productivity and achieve your business goals.
56 SYNAPSE | SHOW EDITION 2023
To be successful in adopting, adapting and benefiting from machine learning, you have to have a data engineering aspect. At Opennetworks we have a wealth of experience in building, maintaining and evolving data landscapes in cloud and across business needs. Our data engineers are on hand to help architect and achieve your next big data objective. Let us help you sort out your data so that you can plug in the ML models and gain the AI insights you are looking for.
