Let's talk about the steps of the machine learning life cycle

If you’ve been thinking about machine learning over the past few years, you’re not the only one. This is big business and can have a significant impact on a company's performance, providing a much-needed competitive advantage.

The statistics prove this. For example, the global ML market is expected to be worth more than $115 billion by 2027, while advances in AI and ML will increase global GDP by 14% from 2019 to 2030, according to Markets and Markets. 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 discuss the seven steps of the ML lifecycle, let’s quickly review what exactly machine learning is.

What is machine learning?

Machine learning is a subset of artificial intelligence that aims to imitate the way humans learn by using data, algorithms and artificial intelligence, slowly improving accuracy over time.

For example, Netflix uses machine learning to power its recommendation algorithms, leveraging the vast amounts of viewing data it has access to and crunching those numbers to show people content that other similar users have enjoyed.

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 host of potential applications, from delivering personalized healthcare to powering self-driving cars and smart cities. Machine learning has applications in every industry, so the question isn’t 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. There are seven steps to this, and the first few steps are the most intense, so stick with it until the end.

Seven steps

1. Collect data

The first step in any ML activity 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 break down data collection into three further stages:

1. Identify the data source

Before you start collecting any data, you need to know where you want to get the data from. Depending on the type of model you are building, you may find yourself working with your own proprietary data, accessing public data (such as through social networking sites), or both. It's also worth considering whether you need explicit data (provided specifically by people) or implicit data (identified based on people's browsing habits and activities).

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 collect the right data from the right sources, which is where the previous step comes in. Don't worry about organizing the data because that will come later.

3. Integrate Data

The next step is to integrate the data you collect with your workflow and ultimately with your machine learning model. This might mean importing data into your proprietary database or using an API to set up automated data feeds from third-party sources.

2. Prepare the 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. There are four steps to this process:

1. Data Exploration

First, you need to look at the data you have so you understand how complete it is and how much work needs to be done to make it work for you use.

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. Data preprocessing

Preprocessing involves cleaning up any formatting that may be present and removing blank entries and other unusual elements from the data.

We're talking about operations you can perform on the entire data set to prepare it for further processing, rather than focusing on any single entry.

3. Data sorting

With these, you can process personal records. Data wrangling requires you to manually go through the data you have and update any data that needs to be updated so that your company can process it.

This is also where you can make any changes to the data to make it readable and easier to process for the model you build.

4. Analyze the Data

By now your data should be in very good shape, so the next step is for you to take a closer look at the data you have and analyze it to determine how you will Process it and build your model.

3. Select a model

Now that we have organized your data and taken a close look at what data you have, the next step is for you to select a model so that you can start processing that data and Work towards your ultimate 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 the developer who can provide the best advice for your needs.

4. Training the model

Now that you have chosen your model, the next step is to start developing it and feed it the data you have so you can start training it.

When we talk about training a model, 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 then train the model to draw its own conclusions.

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 that it better helps you achieve your goals.

6. Model Evaluation and Testing

Once your model has trained itself on the data you provided, you can start testing it and evaluating whether it achieves what you set for it Target.

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. Once the test is complete, you can proceed to the next step.

You can repeat steps five and six over and over again, one after the other, until you are ready to move on to the seventh and final step.

7. Model Deployment and Prediction

Now that you have completed evaluation, testing, and fine-tuning, your model is ready for real-time deployment.

Once you deploy it, you can start predicting and making predictions using the data you have access to, and you will be able to make decisions accordingly.

You can also go back and fine-tune more or add new data sources at any time, 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.

Conclusion

Now that you know how to get started with machine learning, you can take things to the next step by implementing machine learning in your company.

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