Top 10 things to consider when starting a Machine Learning project

Starting a machine learning project can be both exciting and daunting. However, with a bit of planning, preparation, and following best practices you can set yourself and your project up for success. In this blog post, we’ll go through the top 10 things to consider when starting a Machine Learning project. These steps are applicable to both beginners as well as seasoned professionals.

Define your problem: Before you start working on your project, it’s important to clearly define the problem you’re trying to solve. After all, if you’re not solving a real problem then why are you doing the project at all?! Are you trying to predict future sales? Classify images? Understand customer behavior? Having a clear problem statement will help guide your project, understand what parts of the project require machine learning versus other approaches, and will ensure that you stay on track.

Gather your data: Once you’ve defined your problem, it’s time to gather the data you’ll need to train your model. This means understanding your data sources, what type of data you have, and how much data you have. Make sure you have enough data to accurately train your model. Make sure your data is representative of the problem you’re trying to solve. Also, you should check your data quality including checking for missing values, outliers, and any other issues that may impact the performance of your model.

Prepare your data: Once you have your data, you next need to prepare it for training. This may include cleaning the data, normalizing it, and splitting it into training and test sets. For supervised learning in particular, this may also mean labeling your data. While this may seem like a tedious task, do not skip this step.

Choose the right algorithm: There are many different machine learning algorithms to choose from, and the right one will depend on the problem you’re trying to solve. For example, if you’re trying to classify images, a convolutional neural network (CNN) may be a good choice. While if you’re trying to predict sales, a linear regression model may be more appropriate.

Listen to our podcast on the Seven Patterns of AI to see what type of AI and Machine Learning project you’re looking to run.

Train your model: Once you have determined what problem you’re solving, gathered your data, prepared your data, and selected the right algorithm, you’re now ready to build and train your model. This is where the machine learning magic happens! Make sure to monitor the performance of your model as it trains to ensure it’s learning as expected.

Evaluate your model: Once your model is trained, it’s important to evaluate the results to see how well it’s performing. This may include using metrics like accuracy, precision, recall, and F1 score to evaluate the performance of your model.

Check out our glossary entry to learn more about Accuracy

Check out our glossary entry to learn more about Precision.

Check out our glossary entry to learn more about F1-Score.

Fine-tune your model: Based on the evaluation results, you may need to fine-tune your model. In fact, more than likely you will absolutely need to do this. This can include adjusting the parameters of your algorithm, or trying different algorithms altogether. Remember, ML Models are constantly degrading and constant evaluation and ongoing re-training will need to  be worked into your training and retraining pipelines.

Operationalize your model: Once you’re happy with your model’s performance, it’s time to put the model into the real world. In the machine learning world this is called operationalizing your model. You can place your model anywhere that the model needs to provide predictions including in a mobile app, on a server, in the cloud, on the desktop, in a web browser Javascript, or on an “edge device”. So, “deployment” really is just putting the model into places where it can be operational, making inferences on “real” data.

Monitor and maintain your model: Even after your model is deployed, it’s important to monitor its performance and make updates as necessary. Remember when building a model it’s never a set it and forget it type thing. This may include retraining the model as needed if the data changes, or making adjustments if the model’s performance starts to degrade. You will have model drift over time, so if you’re not monitoring and maintaining your model it may become unusable.

Follow best practices: Most importantly, follow best practices and learn from others. We are big advocates of following CPMAI, the Cognitive Project Management for AI. It’s a vendor-neutral best-practice methodology for AI, machine learning, advanced data analytics, intelligent automation and cognitive projects of any size.

Learn more about the Cognitive Project Management for AI (CPMAI) Methodology.

Starting a machine learning project can seem daunting. But by keeping these 10 considerations in mind, you can follow a step by step approach for project success. Remember to clearly define your problem, gather and prepare your data, choose the right algorithm, train your model, evaluate your model’s performance, and follow best practices.

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