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Articles March 11th 2022

4 Tips For Streamlining Your Machine Learning Process

Machine Learning

Machine learning (ML) is a process that employs algorithms to make machines more intelligent. This involves feeding datasets into the machine and getting it to learn from them. It’s a component of artificial intelligence (AI) and is commonplace in business models as it helps to improve operations and productivity by mimicking human intelligence but reducing errors. While the machine will learn to develop algorithms and perform functions on its own over time, you have to set a baseline for the learning process.

The best way to get the machine to learn to perform more accurately is by streamlining the process. This makes it easy for your artificial intelligence systems to pick patterns and standardize features.

Here are some essential tips for streamlining your machine learning process:

 

1. Outsource the Process

One of the most convenient ways of streamlining your ML process is by letting the experts handle it. It’s no surprise that machine learning is even being outsourced. This is because the process involves managing data, training algorithms, testing, and deploying it to an AI ecosystem. One wrong step in the process could mess up the whole thing, and you may be forced to spend time correcting it.

Therefore, it may be more convenient to let experts handle the whole process, especially if you don’t have the technological know-how or resources. As many companies realize that an expert can handle this process more efficiently, that’s the step you should also take. Additionally, outsourcing may be more cost-effective. You can look over at this website to see how an outsourcing company will train your models to work for you in any infrastructure you’re using.

 

2. Understand the Data

A model is only as good as the data it’s based on. Thus, make sure your data is up to scratch. Start by finding sources of relevant data, making sure it’s consistent. If it’s not, try cleaning it up or implementing new collecting processes. Then look for trends or connections in the data. If you notice anything intriguing, think about how that could be applied to the business objectives you’re trying to achieve with ML.

Understanding your data also involves knowing what data is available, where it comes from, and how it can be used. You may need to get additional data sources or combine information from multiple sources such as internal databases and third-party applications programming interfaces (APIs). Once you understand your data and know how accurate it is, how big the distribution is, and how it can be stored, you’ll have a better foundation for building your models. Remember that data is the backbone of any ML process, so focus more on it to streamline the process.

 

3. Choose An ML Framework

It’s extremely tempting to jump from one framework to another, especially when you’re searching fruitlessly for something that’ll work better than what you have. However, this is a mistake. Every framework has its syntax and quirks, so jumping around will only slow you down. Instead, pick something and stick with it. This will help you become familiar with the framework and learn how to maximize its potential benefits.

Moreover, as the team developing an algorithm or a model increases with time, you should focus on maintaining consistency. Consistency will help you build an effective AI ecosystem that’ll perform as required. In contrast, using different ML frameworks brings chaos and makes it difficult to document or track performance.

 

4. Set Up a Standard Data Pipeline

A pipeline refers to a set of linked processes in a sequence. Pipelines are useful for handling data because they can be used to standardize outputs and prevent errors. For example, you can use pipelines to:

  • Clean the training data. Fill in missing values and format the data to be processed by your model.
  • Convert categorical variables into numerical ones if needed.
  • Split the data into training and test sets so that the ML model doesn’t overfit the data.
  • Transform your data using polynomial features or other feature engineering techniques.


By using a standard pipeline, you can quickly start iterating on it as you discover what works and doesn’t work in your data cleaning and modeling process.


Conclusion

Developing a working model in machine learning can be a tedious task. The algorithms may not always perform in the first trial, so you need to test and train the models you’re building. To achieve the desired results in your machine learning process, you should ensure that it’s streamlined through the four tips discussed in this article.