Machine Learning in Prod

Why do we need machine learning?

Machine learning algorithms have become increasingly important in production pricing forecasting for organizations. By analyzing and understanding large volumes of data, companies can make informed decisions about future pricing and adjust their strategies accordingly. These algorithms allow companies to predict market trends, maintain a competitive edge, and optimize costs, which has implications for revenue increase and cost optimization.

To generate optimal prices, machine learning algorithms learn from data on network topology, transportation costs, routing and transit times, and industry benchmarks. This allows organizations to price their products or services in a way that not only delights their customers but is also profitable for the business. By leveraging machine learning algorithms, companies can improve inventory management and demand forecasting, leading to more accurate predictions.

How to productionize machine learning models?

Productionizing a machine learning (ML) model refers to the process of deploying models onto productional systems, automation testing, and integrating it with business processes.

• Data preparation: In this phase, data is extracted from various sources, explored, cleaned, and transformed into formats that suit our ML algorithms.
• Model development: This phase involves selecting the appropriate ML algorithm, parameters tuning, training, and testing the model on labeled datasets.
• Model evaluation: This phase validates the model's accuracy by comparing its predictions with actual outcomes in the test dataset.
• Deployment: It involves integrating the model with a software system and automating the process for updating/maintaining the model's performance.
• Performance monitoring: During the deployment, we monitor the model's output, runtime issues, and errors. If something goes wrong, it is remedied in this phase.

In some cases, a hard-coded rule-based system may be able to capture most of the information in the data and produce satisfactory results, even though it may lack the sophistication of a machine learning algorithm. However, it should be kept in mind that hard-coded maps or rule-based systems have limitations over machine learning models as they are less flexible and less adaptive to changes in the data or the business problem. Therefore, it is always better to strive to complete the development of the machine learning model as soon as possible so that it can best leverage the power of machine learning algorithms.