If you’re looking for a way to efficiently and accurately analyze your business data and models, using artificial intelligence (AI) and machine learning models (ML) is a great way to do that. Machine learning is all about the data and models and excludes metrics.

However, if you don’t follow the right processes, you won’t get the best results from ML. Model training is important to ensure that machine learning models dispense accurate data that is free from biases.

Today, we’ll explore what the most important aspects of AI/ML training is, what techniques you should follow, how to pick the right model for you, and how you can repurpose existing ML for new initiatives.

Let’s dive in!

Data Quality Importance in AI/ML Training

There’s no doubt that AI and ML can help you streamline processes, improve productivity, and increase efficiency in your business. However, these technologies can only generate reports and predict outcomes based on the data that’s available.

That’s why data quality is of the utmost importance when it comes to ML models and AI models. Simply put, bad data will breed a bad machine learning algorithm, which could spell trouble for your business.

First, let’s break down some of the most common data quality issues:

  • Not enough data
  • Non-representative data
  • Inaccurate data
  • Irrelevant data

Luckily, there are several things you can do to prevent data quality issues and they’re easier to execute than you think. Now, we’ll take a look at how to solve these issues:

  • Get creative about data collection (i.e. include social media insights, automated data collection)
  • Provide training to employees on how to properly pre-process data (we’ll talk more about this in the next section)
  • Create a culture of data science comprehension within your business

Essential Data Preprocessing Techniques

Did you know that data scientists generally spend about 80% of their time preprocessing data? This is because ensuring high-quality data means it must be evaluated for things like inconsistencies, duplicates, and missing values.

There are several ways that data scientists achieve this:

  1. Normalization: This process ensures that all features in a dataset are in a common scale. By completing normalization, you can ensure that machine learning algorithms perform better and are more accurate.
  2. Feature Extraction: This process involves examining raw data and extracting relevant features from it. The purpose of feature extraction is to break data down into less complex forms without degrading the relevant information it contains.
  3. Handling Missing Values: This process aims to eliminate machine learning bias and inaccuracy by eliminating missing values that exist in a data set.

Data preprocessing, while time consuming, is paramount to effective training validation within AI/ML. Without it, your ML models could be lacking in accuracy, be prone to bias, and more.

Choosing the Right Model for Your AI/ML Project

If you’re considering implementing AI/ML into your workflow, you’ll need to make sure you choose the right model for the project. Here is a quick rundown of the types of machine learning models out there:

  • Regression models
  • Classification models
  • Dimensionality reduction models
  • Neural network models
  • Clustering models
  • Dimensionality reduction models

When choosing the right option for you, there are three factors you should consider: complexity, training time, and data nature.

  1. Complexity: You’ll need to consider what level of accuracy, precision, and recall you need in ML models. More complex supervised models offer a higher level of these qualities, whereas simpler models can be more transparent, but often have a higher error rate.
  2. Training Time: It’s a good idea to think about how long it will take to train a new ML model and how that might affect your business goals. When you have an objective that has a short turnaround time, you’ll need to go with an ML option that is quick and easy to train.
  3. Nature of Data: All machine learning models are not created equal. Consequently, it’s important to consider the nature of your data when selecting the option that’s right for you. Unlabeled data, for example, is best handled by unsupervised ML algorithms.

The Role of Data Diversity and Domain Knowledge

If you want the best results when it comes to your ML algorithms, it’s important to prioritize data diversity and domain knowledge during the training stage. These items help models to generalize data more effectively, avoid biases, and perform better overall.

Let’s dive a little deeper.

Without data diversity, a ML model simply won’t be as robust as it could be. A lack of data diversity can lead to major issues such as poor overall performance, data bias, and even inaccurate information. You can combat this by implementing procedures into your data collection strategy that champions diverse data collection initiatives.

Domain knowledge is another vital part of a high-quality model learning algorithm. In the world of data science, domain knowledge refers to the expertise of an individual that facilitates machine learning. It’s vital to ensure that company staff who handle machine learning processes have a clear and profound understanding of the problems that machine learning aims to solve for you and how to apply that knowledge to the training process for the best results.

Best Practices for Data Splitting

Data splitting is the process of using different data sets for different stages of machine learning implementation. The strategy is simple: by using different groups of data for training, validation, and testing, you can avoid biased results and improve accuracy of results.

The first step is training. The data set that is used for this portion of machine learning is called the test set. This set is used to train the machine learning model, which allows it to find patterns within.

The next data set is called the validation set. This data is used to prevent overfitting, which occurs when an ML returns accurate results to the data set but can’t make generalizations based on new data.

The final data set is called the test set, and is used to test an ML after training is completed. This data set confirms that training has been successful and that a machine learning model performs well.

Reusing Pre-Trained Models and Preventing Overfitting

The ML training process is time consuming and can be costly. So, when a new, related task is required, it can be helpful to reuse pre-trained models. This enables businesses to take advantage of increased efficiency, improved accessibility, and better performance versus training a new ML.

Using pre-trained models comes with its own set of hazards. You’ll still need to use strategies to prevent overfitting. The two most common and helpful of these strategies are regularization and cross-validation. Regularization is used to calibrate the machine for the new task and cross-validation evaluates performance of a model by dividing data into subsets to train the model.


Implementing machine learning models into your business can lead to increased efficiency, higher revenue, and better customer satisfaction. For the best results, you’ll need to focus on data quality, preprocessing, model choice, and data handling in the training stage. Don’t forget to explore the linked posts throughout this article for a deeper understanding of the different models out there and the distinction between AI and ML.

Machine learning training is complicated – you don’t have to go through it alone. Get started with 3Cloud today!