AUTOMATION
Machine Learning success starts with these 10 steps “Machine Learning (ML) can take an organisation’s digital transformation to new heights” — It’s a statement we hear time and time again, but in practice, it doesn’t achieve that warm and fuzzy turn-key transformation feeling the statement asserts. BY SANTIAGO GIRALDO, DIRECTOR OF PRODUCT MARKETING AT CLOUDERA THE TRUTH is that the promise of ML can be difficult to attain, but it is ultimately there for the taking – for those ready to embrace a new way of thinking. While some deem ML as a pie-in-the-sky assertion that’s too good to be true, others are grabbing it by the horns and witnessing the true value it can bring to a business. In fact, according to Forbes research, the global machine learning market was valued at $1.58B in 2017 and is expected to reach $20.83B in 2024.
The thing is, ML is not always easy to implement. We often see teams running into the most issues when bridging the gap from simply dipping their toes in the ML waters to getting to grips with full scale ML production. Luckily for them, these barriers are easily overcome.
There’s no denying that in order to see the benefits of ML, businesses have to embark on a new kind of data journey – one that may seem difficult, or even uncomfortable. But once an organisation has full scale ML models in production, the benefits are endless. It can help to increase revenue, decrease costs, and even help teams work smarter and do things faster. It’s also sustainable, if a company is willing to work at it.
Taking a holistic approach When it comes to embracing ML models in all their glory, leaders have to adopt the right mindset and take a holistic approach. Before it can become a driver for change, ML must first be treated as an integral part of an organisation’s data strategy and be baked in from the very beginning. By integrating ML from the start of a project and running it alongside existing IT environments, processes, applications and workflows, organisations can drive better business results. This is because the ML will be continuously learning and developing from the very beginning, ensuring they are working to the best of their ability from the get go.
In order to achieve enterprise ML success, there are ten proven steps for an organisation to follow:
Evolve the organisation to embrace ML For businesses that have already dabbled in ML, they will have noticed that there’s a wall between experimentation and large-scale adoption. This wall is there because an organisation may lack the knowledge and skills needed to weave ML development, production and maintenance into their existing processes, workflows, architecture and culture. That’s why embracing ML requires flexibility in the structure of a company and their approaches to how they manage their data. Data scientists and engineers should work closely with leaders, to lead them on the right path when it comes to managing
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ISSUE VII 2021
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