Understanding the Data Side of Machine Learning

One of the primary goals of artificial intelligence (AI) and machine learning (ML) is to enable machines to learn and reason and, ultimately, to automate tasks or decision-making. However, while solution builders focus on decreasing the need for human intervention in certain processes, it’s essential to remember the human element that is the cornerstone of ML.   As a data scientist or machine learning solution builder, you need to recognize that your role is more than designing an ML system. It’s also optimizing the data side of machine learning with quality data that results in the most favorable outputs. 

What is machine learning?

At a high level, machine learning uses mathematical models to enable a computer to learn on its own. While AI performs tasks that have typically required human intelligence, ML is a subset of AI that solves problems or completes tasks by learning from data, identifying patterns, and making predictions.

The primary types of machine learning fall into three categories:

  1. Supervised learning,
  2. Unsupervised learning
  3. Reinforcement learning

All require high-quality input data and, in the case of supervised learning, annotated training data to produce favorable outputs.

Why data and data quality are essential to machine learning

If you choose the wrong type of fuel for a race car, you won’t see the performance its engine was designed to deliver. The right kind of fuel is necessary for the top performance of any machine – and it’s also true when you’re fueling machine learning with data. Machines can’t distinguish between good and bad data on their own – humans must make that call.

ML enables machines to learn independently, but a machine can’t distinguish between good and bad data. They need to learn from a training dataset or input data, and the quality of that data is directly proportional to an ML model’s success. So, if a machine learning model delivers inaccurate or irrelevant results, the first step in troubleshooting is evaluating data quality. Specifically, data must reflect:

  • Consistency: Data must be represented the same way throughout the dataset.
  • Accuracy: Data inputs must be correct and precise.
  • Completeness: Data should represent all aspects of operating conditions in a balanced way.
  • Relevance: Data should be contextual and necessary to the process.

Subpar data quality is more than a problem you see (or overlook) in the sandbox. Poor data quality can create data cascades that cause adverse downstream effects, for example, errors that jeopardize production, worker safety, and decision-making. Data quality is vital to ML performance and to the value it provides to the operation where it’s deployed.

How data is used in machine learning

Among the different types of machine learning, supervised learning is the most common. ML solution builders train these algorithms by using annotated training data. Once the model has identified patterns and relationships in the initial dataset, solution builders evaluate outputs and make corrections to input data, if needed, so the ML model delivers favorable results.

Granted, annotating data is time-consuming and can lengthen the timeframe for implementing a solution. However, the urgency to launch a new ML solution must be balanced with accuracy. The ML model needs to work as intended, which is far more critical than launching sooner and getting harmful negative data cascades.

Fortunately, practical solutions decrease the tension between speed and accuracy. Active learning technology allows the ML algorithm to take an active role in its training. With active learning, the training dataset is smaller than in traditional training phases of development. The algorithm helps select the data that will maximize performance, rather than blindly using all data available, and can ask humans for more input to perform better.

Outsourcing may be a more straightforward solution for handling data annotation, particularly if the problem is a lack of time. Data annotation often comprises about 80% of the resources on an AI project. Internal teams could spend their time on higher-value tasks. Moreover, companies annotating data internally often don’t have the right tools to efficiently perform the task and quality assessment. Outsourcing often makes sense in these situations if you have a strategy that includes several data annotator providers to reduce risk. It’s also imperative to choose a provider committed to the high data quality you need to ensure a model that gives favorable results.

The primary types of machine learning

There are three categories of machine learning; supervised learning, unsupervised learning, and reinforcement learning. Each type of machine learning has distinct advantages for different applications

Supervised learning

In supervised learning, data annotators add metadata to help the algorithm learn the types of data inputs it will receive when it’s fully deployed and the problem it must solve. The supervised learning algorithm finds the relationships necessary to create a model based on inputs.

One of the main advantages of supervised learning is greater control over how the model works and the outputs it produces. . Data scientists can train the algorithm to produce outputs based on data from real-world experiences. A downside is that data annotation and training require a high degree of human intervention. However, you have the option to minimize this time by outsourcing to an experienced, reliable data annotation provider. 

Unsupervised learning

Unsupervised machine learning doesn’t require data annotation. Instead, the machine learning algorithm finds patterns in data to create hidden structures that cluster data based on the relationships it discovers. Hidden structures are often relationships that humans don’t have the time or ability to identify, which could lead to revelations and innovation.

Another advantage of unsupervised learning algorithms is their flexibility. Because these algorithms require no human intervention to label data or interpret results, it quickly adapts to solve new problems and create new hidden structures. However, there’s a trade-off between supervised and unsupervised learning: Unsupervised learning may eliminate data annotation, but it tends to be less accurate than supervised learning.

Reinforcement learning

Reinforcement learning borrows from humans’ “trial-and-error” approach, allowing the machine learning algorithm to learn through a system of reinforcing accurate outputs and discouraging inaccurate or irrelevant outputs. An interpreter evaluates outputs and rewards the solution if the output is favorable, often with a score representing its effectiveness. If the result is not favorable, it forces the algorithm to return to the problem and find a better output. The solution is designed to seek the highest possible reward and, in turn, the best possible outputs.

Reinforcement learning can solve complex problems, and once the algorithm makes an error, it’s unlikely that it will make it again. However, that is also one of the downsides of this type of machine learning – too much reinforcement can negatively impact results.

Machine learning applications

Finding the right combination of quality data and type of machine learning model is key to meeting the growing demand for machine learning. Fortune Business Insights predicts phenomenal machine learning market growth of 38.8% CAGR from 2021 to 2029 to reach $209.91 billion. In-demand solutions driving growth include:

  • Intelligent data analysis in healthcare – Machine learning can facilitate diagnoses, interpret medical imaging, and alert caregivers to priorities, based on current data and trends that point to likely outcomes. Machine learning can also automate repetitive administrative tasks, like data entry or inventory management, to allow caregivers to spend more time with patients.
  • Fraud detection in finance – Machine learning can detect the differences between a legitimate transaction and a fraudulent one so it can issue alerts and decrease financial institutions’ losses.
  • Email monitoring – Phishing is a social engineering attack that resulted in more than $44 million in losses in 2021. Machine learning, specifically natural language processing (NLP), can analyze email content and detect signs that it’s a phishing attempt rather than authentic communication.
  • E-commerce recommendation engines – E-commerce adoption has skyrocketed. Machine learning can improve experiences with recommendations based on past purchases or items that are similar or complementary to the merchandise customers have viewed. The solution can also increase conversion and revenues for e-commerce merchants.

Want to learn more about the role of data quality in machine learning?

Sigma understands how much is riding on the effectiveness of your machine-learning model and the role that data plays. For more than three decades, we’ve provided annotation to companies worldwide, delivering accurate annotations and allowing data science teams to focus on other aspects of machine learning model development.

Sigma offers a risk-free pilot with no obligation to experience firsthand the advantages of a partnership with a reliable data annotator committed to data quality. Contact us.


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