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How to start and master machine learning

The buzz around AI, ML and DL terminology

There are lots of uncertainties about what machine learning is and actually can. So, what is machine learning? Put simply, machine learning describes computer algorithms trained with real-world data to build predictive models.

Thinking of the terminology of all buzzwords like artificial intelligence (AI), machine learning (ML) and deep learning (DL), artificial intelligence can be considered as the whole thing including machine learning and deep learning as a subset each. Meaning, deep learning is a subset of machine learning.

AI means that a computer is doing human things and within AI machine learning is one of the most rapidly growing techniques. ML is the subset of AI in which applications learn from data without being programmed explicitly. Considering the opportunities that ML brings, many companies are thinking of integrating ML in their business, but how to get this right from the start?

How to start with machine learning, but get it right

A lot of businesses want to incorporate machine learning to handle their data. But too often, machine learning projects end up in disappointment and do not get to a production state or fail once they are there. Based on numerous machine learning experiences from projects and training courses of both our partner Enjins and Stackfuel, we want to present four valuable lessons in how to realize lasting machine learning solutions.

1 Plan before you start building

To realize lasting machine learning you need to start with a plan and a business case. Assess the data quality and business potential of machine learning within your company. In addition, draft a roadmap and an infrastructure blueprint to have a valid business case before building a machine learning engine.

2 Involve your business experts

During development, testing, and usage, make sure you involve your internal business experts. Machine learning does not exist without learning. Therefore, include feedback loops that get all of the missing information out of the expert’s mind, greatly increasing model performance.

3 A model is not finished once live

A model is not finished once live, it only starts when going to production. Therefore, go to production with a model with limited complexity and start learning in a live environment. Only in the ‘real world’ you will get to know the value of your machine learning solution.

4 Skill your people

To kick-off or accelerate your ML practice the help of external parties might be useful. Only hiring one or two junior data scientists probably does not do the trick. In the end though, please make sure to build up your data science and machine learning knowledge inhouse. This will be a key asset of your company in the future.

Enjins – The Machine Learning Engineers

Enjins is a Dutch based company that builds use-case driven machine learning engines, with special focus on scale-ups and mid-corps. Having top notch scale-up clients like Wunderflats and Sendcloud, Enjins believes in opening the black box of machine learning models by providing tools to explain the models’ behavior. Increasing transparency leads to trust, a foundational pillar to create business value on top of machine learning. Learn more about Enjins on www.enjins.com.

Since August 2020, StackFuel corporates with Enjins as strategic partner to accelerate machine learning practices. While we are providing customers of Enjins with our upskilling online courses, Enjins helps with auditing, development, and operationalizing machine learning solutions. Together we offer an integrated machine learning portfolio with machine learning consulting, development, and training.

Julia Kazubek

Julia Kazubek

Julia is Director Marketing at StackFuel. For her, lifelong learning is part of everyday business in marketing. True to her motto "Knowing how to live life", she devours specialist literature on growth hacking, leadership and marketing in her free time or uses one of the many cultural offers in her hometown of Berlin.

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