Machine Learning

Context Engineering with Adi Polak

Exploring the evolution from prompt engineering to context engineering for building stateful AI systems. Covers managing AI memory, event-driven architectures with Kafka and Flink, and creating reusable skills for multi-agent workflows.

QCon London 2025: Achieving AI Precision through Intelligent Data Retrieval

Overcoming AI precision challenges through agentic RAG systems. How intelligent agents can decompose complex tasks to deliver more accurate data retrieval for production AI applications.

Agentic AI: The Top Challenges and How to Overcome Them

Key obstacles enterprises face when deploying agentic AI systems, including model logic, reliability, data privacy, and data quality, with practical solutions for building trustworthy autonomous AI agents.

Machine Learning in Production - Concepts you should know

Are you interested in learning about the Machine Learning side of data? Hurry 🎉 , you have reached the right place to start learning about it.

Here is a list of concepts for you to get started:

ML Algorithm

ML algorithm is a procedure that runs on data and produces a machine learning model. Some of the popular ones are Decision trees, Naive Bayes, and Linear Regression.

ML Model

ML model is the ML algorithm process outcome; It often contains a statistical representation of the data ingested into the algorithm. ML model input is data, and the output is either a prediction, decision, or classification.