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4 Common Machine Learning Pitfalls and How To Avoid Them

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Published on September 28th, 2022

Table of Contents

Introduction

In the realm of IT, machine learning (ML) is sweeping the industry. In fact, Arthur Samuel invented one of the most popular words of the twenty-first century in 1959, defining machine learning (ML) as “the field that offers computers the ability to learn without being explicitly programmed.” Although the idea of machine learning (ML) first gained popularity in the 1950s and 1960s, especially in academia, at the time, only a few applications could benefit from such advanced technology.

Soon after 2000, ML had a rebirth. As the price of storage and computing power started to fall, ML all of a sudden became a viable and scalable solution. Several important causes have contributed to the current growth of ML:

Access to cloud computing
Open source tools that evolve with technology
Increased demand for improved product development, customer management, and process automation

Since its debut, ML has advanced significantly, and every company considering ML must grasp how to create effective models. It is crucial to understand what the current ML alternatives are. It’s even more important to understand the common myths associated with ML and how to avoid them to make the most of and benefit from its capabilities.

Machine Learning

Today, ML is complemented by several open source workbenches and utilities, including Python, R, TensorFlow, sci-kit-learn, and many others. It is also supported by the current state of cloud computing and scalable data and data capacities. The algorithms in a framework like Keras for TensorFlow are available to anyone interested in learning more about Machine Learning and are housed in prepackaged software libraries.

While there are countless reasons why ML pilots never take off, the most pressing problems can be traced back to four main pitfalls:

Lack of business alignment.
Poor machine learning training
Data quality issues.
The complexity of deployment.

Let’s explore each of them and suggest some solutions for data teams and organizations to avoid them.

1. Lack Of Business Alignment

The original sin of machine learning is how most of these projects are born.

Too often, a group of data scientists come up with machine learning projects and think, This data is interesting; wouldn’t it be great if&.

And it is this way of thinking that turns ML projects into scientific experiments.

In this kind of project, it might still be possible for a model to generate something useful, but if the project doesn’t solve a pressing issue, business partners won’t give it the time or attention it requires. Or worse, it might resemble blockchain more: a significant technology looking for a challenge. (Read more about blockchain technology here.)

Instead of starting with clean data and then looking for an issue, they can address, machine learning projects should first look at the most urgent business priorities and then determine what resources are needed to tackle them.

Before beginning a machine learning project, consider these questions:

Is this issue urgent? According to the WHO?
Why is machine learning the right solution to this problem?
How will we define success?

2. Poor Machine Learning Training

You must adjust each hyperparameter in order to produce the most effective ML algorithms for your use case. The number of trees in a random forest or the design of a neural network is two examples of hyperparameters. In order to attain performance, these hyperparameters should be customized to your unique dataset by referring to analogous use cases or previous research.

However, it should be highlighted that rather than experimenting and testing various configurations to see how they perform, we need to approach hyperparameter optimization deliberately. The following are a few of the most well-known optimization techniques:

Grid Search: Also referred to as parameter sweep, grid search is a thorough search of a manually chosen subset of the hyperparameter space of the learning algorithm. Typically, some performance statistic is what motivates it.

Random Search: In contrast to grid search, random search chooses combinations at random rather than exhausting all conceivable configurations.

Bayesian optimization: Bayesian optimization creates a probabilistic model mapping feature from hyperparameter values ??to a target evaluated on the validation set. The goal of Bayesian optimization is to assess a promising configuration of hyperparameters based on the current model and then update it to collect observations revealing as much information as possible about this function and especially about the location of the optimum.

Evolutionary optimization: This method explores the hyperparameter space for a particular program using evolutionary algorithms. It starts with a population of random solutions, evaluates hyperparameter tuples to determine their fitness function, ranks hyperparameter tuples according to their relative fitness, and then, through crossover and mutation, replaces the worst-performing hyperparameter tuples with newly generated hyperparameter tuples.

Population-based training: This approach eliminates manual hyper-tuning by having multiple learning processes operate independently using different hyperparameters. Poorly performing models are replaced by models that take the modified hyperparameter values of the better-performing ones.

It is worth noting that hyperparameter optimization and feature selection should be part of model training, not an activity performed before model training. A widespread mistake is to perform feature selection on the entire dataset before starting to train the model, but this will leak information from the test set into the training process.

Our Learners Also Read: Python Vs C#: Comparison, Benefits, Differences, and Use Cases

3. Data Quality Issues

Whether training or deployment, it’s impossible to have an effective machine learning model with insufficient data. As they say, garbage in, garbage out.

The difficulty is that machine learning models require a lot of data. More data is always desiredas long as it is trustworthy.

However, insufficient data can be introduced into good data channels in infinite ways. Sometimes it can be a noisy anomaly where the error is picked up quickly; other times, it may be a gradual case of data drift that reduces the accuracy of your model over time. Either way, it’s terrible.

That’s because you’ve built this model to automate or inform a painful business problemso when accuracy drops, does trust, and the consequences are dire. For example, one of my colleagues talked to a financial company that used a machine learning model to buy bonds that met specific criteria. Insufficient data took it offline; it took weeks before it was trusted to go back into production. (Also read: The Future of Fintech: AI and Digital Assets in Financial Institutions.)

The data infrastructure supporting machine learning models must be constantly tested and monitored ideally scaled and automated.

4. Deployment Complexities

It turns out that deploying and maintaining machine learning in production requires a lot of resources. Who knows?

Well, Gartner did. Due to the development of the AI business and the resulting tenfold rise in processing requirements, it is predicted that by 2025, AI will be the main category influencing infrastructure decisions.

Because of how much support from stakeholders is needed, business alignment is crucial. For instance, Atul Gupte, a former manager of Uber’s data products, oversaw a project to enhance the company’s data science workbench, which data scientists used to interact more effectively.

Data Scientists have currently automated the process of verifying and verifying work documents that were required when applying to join the Uber platform. It was an excellent project for machine learning and deep learning, but the problem was that data scientists routinely hit the limits of available computing.

Gupte explored several solutions and identified a possible explanation for virtual GPUs (an emerging technology). While there was a high price, Gupte justified the expenditure by management. The project was not only to save the company millions but also to support a critical competitive difference.

Another example is how Netflix never took its award-winning recommendation algorithm to production, opting for a more straightforward solution that was easier to integrate. (Also read: How artificial intelligence is personalizing entertainment.)

How To Avoid These Pitfalls

Don’t allow these difficulties to stop you from starting your machine learning project.

Mitigate these risk factors:

Getting stakeholder buy-in early and leveling often.
Iterate the DevOps way.
Ensure you have the proper training data and monitor quality before and after production.
Keep in mind production resource limitations.

Conclusion

Creating robust models requires that key considerations be considered throughout the process. Modern ML is full of promise. It is a dependable engine that spurs creativity, develops new products, improves user experiences, and automates laborious jobs, but early adopters of ML must be cautious of frequent dangers.