This article discusses the steps and considerations for creating a machine learning use case to improve business processes. It explains the concept of machine learning and the importance of data quality and volume in creating accurate predictions. The article outlines the steps in creating an ML use case, including defining the problem, collecting and preparing data, defining product objectives and metrics, training and evaluating the model, and deploying the model. The article also discusses the types of ML problems and how to discover ML use cases in existing business processes. Overall, the article emphasizes the importance of understanding business problems and identifying opportunities to use ML to create enhanced solutions.

In today’s digital age, adopting AI solutions is crucial for businesses to gain a competitive advantage. However, many organizations lack the necessary data and machine learning (ML) skill set to create valuable AI solutions. This is where AI consultants play a key role, bridging the skill set gap and accelerating the adoption of AI across business functions. AI consultants help assess an organization’s maturity level and design a transformation approach that fits the client’s goals. They also promote the creation of collaborative, cross-functional teams with analytical and ML skills, and work on creating consistency in tools, techniques, and data management practices to enable successful AI adoption.

Feature engineering is a crucial aspect when it comes to designing machine learning models, and it plays a big role in creating top-notch AI systems. Features are attributes that represent the problem of the machine learning use case and contribute to the model’s prediction. The process of feature engineering involves creating relevant and useful features from raw data combined with existing features, adding more variables and signals to improve the model’s accuracy and performance. It starts manually and can be accelerated by adding automated feature engineering tools and techniques. Follow the steps of feature engineering to optimize your machine learning models and create innovative products.