Model Training

Model training is a critical component of tapping into digital transformation via machine learning. Through model training, teams feed data to a machine learning algorithm so that the model can learn through processing large volumes of data, identifying patterns, and finding anomalies.

Training a machine learning model goes far deeper than simply dropping a bunch of data into rows and columns, then pushing a button. It requires consecutive iterations of delivering data to an algorithm, then allowing that algorithm to train the model by identifying the underlying patterns. In doing so, the model is able to extract correlations and conditions that would be difficult for a human to do quickly or effectively.

Why Tap Into Machine Learning Model Training

Model training happens across four crucial steps.

First, the data engineers split the data sets into smaller sections, ensuring that the data sets do not intersect. In doing so, teams are better able to conduct unbiased performance estimates.

Next, the team determines which algorithm to use. Typically a simpler algorithm is selected to compare against the final trained model’s performance. Some modeling techniques include linear regression, random forest, boosted trees, or neural networks.

With the algorithm selected, data engineers then tune the hyperparameters to the subset of data that’s most critical to the desired outcome. Likewise, teams choose a validation set to evaluate the model’s success after training.

Finally, the model training begins and the data is fitted into the algorithm, so that the machine learning model can get to work.

Model Training is Crucial for Optimal Outcomes

Model training isn’t just a cog in the system of tapping into modern machine learning. It’s the process in which a mathematical representation is built between raw data and a specified outcome or target.

The better the model is trained to a specific target, the more accurately teams can understand its performance and rely on the insights delivered. In using time and resources to tap into these insights via machine learning, teams can build revenues, lower costs, and improve user and employee experiences in the process.

How Model Training Works

Machine learning model training isn’t a one-and-done initiative. Instead, there are three ongoing purposes of training models effectively and for optimal outcomes.

First, the team must identify why they’re training the model in the first place. What are the use cases for the model? As we discuss often in our StoryVesting framework, that why must be aligned with the organizational and consumer why to stay in harmony with the market’s needs.

Next, the model must be developed and trained. This is the most important step as it’s where data scientists and engineers transform raw data into context and meaning.

Finally, the model will be deployed to the organization. In this step, it’s crucial that it continues to be monitored to ensure accuracy and relevancy to the organization.

Model Training

Model training isn’t just for the data department. Well-trained models benefit everyone internally and externally alike.

Improved Customer Experience

In training your machine learning models with the right data for your organization, you’re better equipped to pinpoint patterns in your customer’s experience (CX) across the customer journey. With that insight, you’re able to make faster, more strategic adjustments to improve CX.

Improve Employee Experience

Your employee experience (EX) matters just as much as your customer’s, if not more so. By using your platforms to refine your processes and support your people, you improve each of your 3 Ps simultaneously. That improvement boosts employee morale and impacts EX.

North Star Metric Alignment

Today’s business world moves at breakneck speed. With model training, you can dump vast amounts of data into a model and quickly get insight as to how to continue moving up the S Curve of Growth. These insights shape decision making, keeping teams on the same page and moving in sync toward the organization’s North Star Metric.

Stay Relevant

Organizations that leverage model training effectively are better equipped to spot patterns in consumer behavior and make critical changes to align their products with the market needs. In doing so, they’re able to stay relevant and continue moving up the S Curve of Growth.

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Model Training

Why Tap Into Machine Learning Model Training

Model Training is Crucial for Optimal Outcomes

How Model Training Works