Welcome to "The Art of Model Training: From Beginner to Pro"! In this blog, we will be delving into the world of machine learning and exploring the process of training models. Model training is a crucial step in the machine learning process. It is the process of using a set of input data, known as the training set, to adjust the parameters of a model so that it can make accurate predictions on new, unseen data. This allows the model to learn from the data and improve its performance over time.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Artificial Intelligence (AI) promises to transform almost every business on the planet. That's why most business leaders are asking themselves what they need to do to successfully deploy AI into production. Many get stuck deciphering which applications are realistic for the business; which will hold up over time as the business changes; and which will put the least strain on their teams.

However, we only ever hear of using AI to understand big data sets. This is because small data sets are usually easily understood by people, and applying AI to analyze and interpret them isn't necessary. These days, many businesses and manufacturers integrate AI into the production line, slowly creating data scarcity. And unlike big companies, many setups cannot collect massive training sets due to risk, time, and budget limitations. As most companies don't know how to benefit from AI application on small data sets correctly, they blindly apply it to make future predictions based on previous files.

Machine learning has been, and will continue to be, one of the biggest topics in data for the foreseeable future. And while we in the data community are all still riding the high of discovering and tuning predictive algorithms that can tell us whether a picture shows a dog or a blueberry muffin, we're also beginning to realize that ML isn't just a magic wand you can wave at a pile of data to quickly get insightful, reliable results. Instead, we are starting to treat ML like other software engineering disciplines that require processes and tooling to ensure seamless workflows and reliable outputs. "Poor data quality is Enemy #1 to the widespread, profitable use of machine learning, and for this reason, the growth of machine learning increases the importance of data cleansing and preparation. The quality demands of machine learning are steep, and bad data can backfire twice -- first when training predictive models and second in the new data used by that model to inform future decisions."