LLMOps is a considered, well structured response to the hurdles that come with building, managing and scaling apps reliant on large language models. From data preparation, through model fine tuning, to finding ways to improve model performance, here is an overview of the LLM lifecycle and LLMOps best practices.
An overview of stream processing: core concepts, use cases enabled, what challenges stream processing presents, and what the future looks like as AI starts playing a bigger role in how we process and analyze streaming data
Read about the fundamentals of event-driven programming (EDP): key concepts, advantages, and challenges. Discover EDP use cases and technologies, and learn about the relation between EDP and event-driven architecture (EDA).
What is real-time machine learning? How is it different from batch ML? What are common real-time ML use cases? What are the challenges of building real-time ML capabilities? All these questions and more are answered in this article.
Should data scientists know Java? Java and Scala underpin many real-time, ML-based applications—yet data scientists usually work in Python. Someone has to port the Python into Java or adapt it to use a Python wrapper. Neither of these options is ideal, so what are some better solutions?
Explore the fundamentals of time series analysis in this comprehensive article. Learn about key concepts, use cases, and types of time series analysis, and discover models, techniques, and methods to analyze time series data.
Gain a thorough understanding of telemetry data and how it works, learn about its benefits, challenges, and applications across different industries, and discover technologies you can use to operationalize telemetry.
Moving code from prototype to production can be tricky—especially for data scientists. There are many challenges in deploying code that needs to calculate features for ML models in real-time. I look at potential solutions to ease the friction.