Explore LLMOps fundamentals for generative AI applications. Learn how effective management and operations transition prototypes to real-world use cases with Krasamo’s specialized services.

Machine Learning is an application in which machines can learn automatically from their experiences or train data to make predictions detecting patterns and creating its own rules.

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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.


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MLOps are the capabilities, culture, and practices (similar to DevOps) where Machine Learning development and operations teams work together across its lifecycle

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Krasamo is a Texas-based software developer with extensive experience gained through many implementations with Apache Airflow.

Data monetization is a process that uses data to create monetary value in the organization. Data products are the source of data that comes in raw or refined forms such as data sets, reports, analysis results, applications, etc