If you’ve been thinking about machine learning over the past few years, you’re not the only one. It’s big business and can have a significant impact on a company’s performance, providing a much-needed competitive edge.
The statistics bear it out. For example, according to Markets and Markets, the global ML market is expected to be worth more than $115 billion by 2027, and advances in AI and ML will increase global GDP by 14% from 2019 to 2030. Additionally, Netflix says it has been able to save $1 billion by using machine learning. Now that we know why ML is essential; before moving on to the seven steps of the ML lifecycle, let’s quickly review what machine learning really is.
What is machine learning?
Machine learning is a subset of artificial intelligence that aims to slowly improve accuracy over time by using data, algorithms, and artificial intelligence to mimic how humans learn.
Netflix, for example, uses machine learning to power its recommendation algorithms, taking the vast amounts of viewing data it has access to and crunching those numbers to show people what other similar users like.
For machine learning to work, you need a powerful model and access to large amounts of data. Most ML algorithms also have access to a floodgate of input information, and they can do better as more data is fed in.
Machine learning has a plethora of potential applications, from providing personalized healthcare to powering self-driving cars and smart cities. Machine learning has applications in every industry, so the question is not whether your company can benefit from it, but whether it can be the first to do so in your niche.
Now, it’s time for us to take a look at the life cycle of machine learning. This one has seven steps, and the first few steps are the most intense, so stick to the end.
1. Data collection
The first step in any ML campaign is to start collecting data. After all, if you don’t have any data, your machine-learning model won’t be able to process anything. We can divide data collection into three further phases:
1.1 Determine the data source
Before you start collecting any data, you need to know where you’re going to get it. Depending on the type of model you’re building, you may find yourself using your own proprietary data, accessing public data (for example, through social networking sites), or both. It’s also worth considering whether you want explicit data (provided specifically by people) or implicit data (identified based on people’s browsing habits and activity).
1.2 Collect data
Now that you know what your data source is and the type of data you want to capture, the next step is to start collecting data.
You need to make sure you’re collecting the right data from the right sources, which is where the previous step comes in. Don’t worry about collating the data as that will come later.
1.3 Integrate data
The next step is to integrate the data you collect with your workflows and eventually with your machine learning models. This could mean importing data into your proprietary database or using an API to set up automated data sourcing from third-party sources.
2. Prepare data
Now that you have identified your data sources, collected them and integrated them into your system, the next step is to prepare it so that the model is ready to start using it. This process has four steps:
2.1 Data Exploration
First, you need to look at the data you have so you can see how complete it is and how much work needs to be done to make it suitable for your purposes.
This is also where you determine the approach you will take in the next two steps to make sure you have everything ready for the algorithm.
2.2 Data preprocessing
Preprocessing involves cleaning up any formatting that may be present, and removing blank entries and other outlier elements from the data.
We’re talking about operations you can perform on the entire dataset to prepare it for further processing, rather than focusing on any single entry.
2.3 Data curation
With these, you can work on your personal records. Data wrangling requires you to manually go through the data you have and update any that needs updating so your company can process it.
This is also where you can make any changes to the data to make it readable and tractable for the models you build.
2.4 Analyze data
Your data should be in pretty good shape by now, so the next step is for you to take a close look at the data you have and analyze it to determine what you’re going to do with it and build your model.
2.5 Select the model
Now that we’ve cleaned up your data and taken a close look at what you have, the next step is for you to choose a model so you can start working on that data and working towards your end goal.
There are many different options when it comes to choosing a model, so your best bet is to research what’s available and find a developer who can give you the best advice for your needs.
2.4 Training model
Now that you have chosen your model, the next step is to start developing it and feeding it the data you have so you can start training it.
When we talk about training models, that’s because machine learning algorithms work by teaching themselves.
Instead of telling them what dogs and cats look like, you feed them a bunch of labeled data about dogs and cats, and train the model to draw its own conclusions.
2.5 Model parameter tuning
Through testing and evaluation, you should now have a clear idea of what changes you need to make to your model to fine-tune it and ensure it better helps you achieve your goals.
2.6 Model evaluation and testing
Once your model has trained itself on the data you provided, you can start testing it and assessing whether it achieves the goals you set for it.
Testing and evaluation go hand in hand, as testing will be a key part of your evaluation and will help you determine if things are working. After the test is complete, you can proceed to the next step.
You can repeat steps five and six over and over, one after the other, until you are ready to move on to the seventh and final step.
2.7Model Deployment and Prediction
Now that you have evaluated, tested, and fine-tuned, your model is ready for live deployment.
Once you’ve deployed it, you can start forecasting and forecasting with the data you have access to, and you’ll be able to make decisions accordingly.
You can also always go back and do more fine-tuning or add new data sources, so don’t think the build is over and done just because it’s live.
If there’s one thing machine learning has shown us, it’s that there’s always room for improvement.