18 Machine Learning Best Practices

18 Machine Learning Best Practices

We explore 18 machine learning practices that that you can apply when building your machine learning application. Machine Learning and Deep Learning (AI, in general) are no longer just buzzwords. Because of all these things building machine learning-based applications is not an easy task. There are several areas where data scientists, software developers and DevOps engineers need to work together in order to make a high-quality product.

Machine Learning and Deep Learning (AI, in general) are no longer just buzzwords. They became an integral part of our businesses and startups. This affects software development too, in fact, it goes even further. We can’t observe machine learning components just as another part of the ecosystem, because they are part of the system that makes decisions. These components also are shifting our focus to data, which brings a different mindset when it comes to the infrastructure. Because of all these things building machine learning-based applications is not an easy task. There are several areas where data scientists, software developers and DevOps engineers need to work together in order to make a high-quality product.

In this article, we cover 18 Machine Learning practices that we think will help you achieve that. These practices are divided into 5 sections. Each section is composed of several tips and tricks that may help you build awesome machine learning applications. Here is what is covered in this article:

  • Objective and Metric Best Practices
  • Infrastructure Best Practices
  • Data Best Practices
  • Model Best Practices
  • Code Best Practices

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