How to Kickoff a Machine Learning Project in Your Company — A Lightweight Approach

Alex Bierhaus
Mar 9 · 3 min read

Today, machine learning is possible for all companies, no matter what size and industry. Thanks to easily accessible technology and an abundance of growing data. But the key to a successful Machine Learning project is to take a sensible — and thus lightweight approach - from the start. A business-centric rather than technology-centric approach allows you to identify and align the right elements (talent, data, and execution) appropriately. This article will bring us closer to how to start an ML project.

During our last Machine Learning projects, we have come up with the following step model for successful integration of machine learning projects:

Stages for a successful Machine Learning Project
  1. Prepare problems and identify your business use cases and the business requirements.

Rather than investing heavily in infrastructure and expensive resources that ultimately don’t fit your needs, take a top-down approach and start with one particular business challenges you want to address. Use your business roadmap as a guide. Be guided by your business needs, not technology needs. During this step, it makes a lot of sense to get the buy-in from one of the top business stakeholders with a good feeling about technology and opportunities for machine learning.

2. Summarize data and identify the data sets you need

Ask yourself: What data do you need to solve this problem? Do we have the data sources and attributes that could answer your question? If not: How can we this information in the long run, and how can we start with a subset of the required information?

3. Prepare data and take care of data quality

Data cleaning is one of the most important parts of each data science project and often accounts for more than 80% of the work. In this step, delete outliers, insert missing values, work out formatting, and look for compliance and privacy, e.g., should part of the dataset be anonymized?

4. Evaluate Algorithms and create a model

Many algorithms can be used in your machine learning model to solve a particular business case with quite different accuracy levels. Simple models are usually easier to implement, scale, and maintain. Don’t try to overengineer your model from the beginning. If you can choose between a very fancy model with very high accuracy and a much simpler (and less accurate) model, you should rather choose the latter if it addresses your business needs more efficiently.

5. Improve Accuracy and test your model

This is the most exciting phase within the projects. After picking up the right data and implementing your algorithms, it is time to test the model. In this phase, I have had a good experience with a rather classic process model:
Build, Maintain & Monitor and Deploy, which complement each other. By intelligently dovetailing these three components, you can achieve a uniform structure that is constantly improving and enables real-time predictions. For example, model generation based on input data is subject to both a unified structure and flexibility through a dynamics option. The system regulates itself, and on this basis, a model adaptation to the data structure is carried out regularly.

Life Cycle of models

6. Finalize model and rollout

Of course, a machine learning model is never really finished. However, to show results, the model should be finalized in a version and introduced into productive operation. Here, it would be best if you did not wait too long because otherwise, you will miss important time to get direct feedback from experts, who will later work with the results of the model regularly.

In this article, we learned how to kick off a new machine learning project in your company. We looked at the different steps that are most promising, especially for new projects. We also looked at how concrete machine learning models should be built. However, after the technical successful implementation, it is imperative to make the models and the knowledge gained with them also tangible for the end-user. This requires a continuous exchange with the respective departments. This will help you get the most out of machine learning investments and increase the chances of achieving your goals.

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