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Machine Learning: 7 Engineering Best Practices

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Machine learning (ML) is one of the modern innovations that enhanced many industrial and professional operations and processes, including daily human lives. Machine learning practices and applications are expected to expand that’ll push the bounds of the technological realm. Some real-life examples of ML applied are speech recognition, image recognition, medical diagnosis, predictive analytics, and statistical arbitrage. As they’ve become an integral part of many industries today, using machine learning comes with great responsibility.

With that in line, different problems arise in machine learning. Most of them are about engineering problems. Whether you’re an expert like software developers or a beginner starting in machine learning, knowing the fundamentals and best practices will help you tackle problems you’ll face in the field.

1. Identify The Problem Statement And Objective

When it comes to building a machine learning application, making the problem statement is the first step. Data scientists and engineers tend to overlook and de-prioritize this step, so spend time on the problem and think about the end goal you’re trying to achieve.

For instance, a problem in a certain business emerges, affecting the company’s profitability. Your job is to derive the objective, which will become the metric you should optimize. Although the objective might change over time as more data comes in, establishing an objective with the given dataset will provide you with a starting point in solving the problem.

2. Collect Existing Data From Past Systems

If you can’t develop a proper objective right away because the requirements are still unclear, you might want to delve into historical data from their old systems. This problem often arises when machine learning is first introduced to old systems. 

Before going into detail on what features your machine learning application will be included, gathering as much data as possible from their current system would be best. It’ll help you solve the task you have at hand and inform you about the indications where optimizations are needed to garner the best results.

3. Use A Simple Metric For Your First Objective

As mentioned, having an objective is the first step in solving any machine learning problem. It’d be best if you start by formulating a simple metric for your first objective so you can arrive at the end goal.

You’ll be presented with new data along the way that might make you revise the initial metric. But having a straightforward metric that’s attributable and observable initially will help you avoid many indirect effects from the beginning. These indirect effects might cost you a significant amount of resources later on. This is why you should start small first.

4. Establish A Testable Infrastructure

The element of uncertainty always accompanies machine learning. A complete infrastructure should be independent of the machine learning model. In a nutshell, you should develop an end-to-end solution wherein all aspects of the system can sustain themselves. You can maneuver and alter the rest of the system when needed.

To keep the infrastructure in check, you can do the following:

  • Employ a test that involves gathering data into the algorithm. Also, compare the statistics of the pipeline versus the statistics for the same data processed elsewhere.
  • Test the parts of the system by isolating each one such as the pre-processing of the data, training the model, testing the model, and serving the model. This way, you could mimic and change the parts of the system efficiently.

Features that are deemed to be not useful can be dropped out of the infrastructure because it’ll only cause technical debt.

5. Deploy Model Once It Passes Several Tests

Tests are essential because they serve as the barrier that separates the engineer from the issues in the system. If you want to deliver the best user experience to your machine learning app, you have to ensure that you employ several tests and sanity checks before launching your model. Check if the metrics in your model could provide good results. This can be done using standard metrics like recall, F1 score, and accuracy.

6. Ensure Data Quantity And Quality

If you want to have good pattern detection or good predictions, you need a considerable quantity of data. You have to ensure the system you’re building gathers enough data for you. If your data is insufficient, you could invest in the existing dataset, as mentioned, and then base the improvements of your model on that. Some engineers use short-circuiting of data if there’s a lack of it with transfer learning.

As for the quality, you could invest in feature engineering and data pre-processing as real-world data can be sparse, incomplete, or sometimes inconsistent. If properly executed, the data you get from the data gathering element can be subjected to transformations such as scaling, imputations, and others in the transformation element. The transformation element prepares the training data while using the same transformations on the newly gathered data from your system. Thus, it can generate features extracted from using the raw inputs only.

7. Use Checkpoints

In most cases, machine learning apps revolve around the power of machine learning models embedded behind infrastructure components. While this holds true to a certain degree, you can’t use the power of these infrastructure components without the help of a sufficient machine learning model that glues them all together.

Using checkpoints is one of the best practices you can employ in dealing with machine learning models. A checkpoint serves as the intermediate dump of a model’s internal state such as parameters and hyperparameters. You can resume any training from a certain point when you use effective machine learning frameworks. Also, it gives you the ability to train the ML model accumulatively. This provides you with more resilience to any cloud or hardware failures, too.

Final Words 

Machine learning is already making its mark in many industries today. It’s a subset of artificial intelligence that uses statistical techniques to create intelligent computer systems to learn and solve problems. The practices enumerated above could help up your skills in developing a sufficient machine learning application, which will be beneficial in your respective field.

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