PinnedApplying a “Transform, Encode, KNN search” framework to match items with Amazon SageMaker and…A production-ready pattern to incorporate embedding building, indexing, and querying into your ML workflow in AWS.May 3, 2023May 3, 2023
Empowering Developers with a Gen AI Technical Support Agent: A Case Study of Canva SDKBuilding a technical support agent to enable developer self-serviceNov 26, 2023Nov 26, 2023
Optimizing Retrieval for RAG Applications: Enhancing Contextual Knowledge in LLMsA Closer Look at RAG RetrieversAug 15, 20231Aug 15, 20231
Exploring services for hosting your ML models on AWSChoose a service to run your ML models in AWSJun 14, 2023Jun 14, 2023
Summarising your meeting with ChatGPT and LangChainLLM might save you valuable time from manually summarising your meeting minutesJun 8, 20231Jun 8, 20231
Published inTowards Data ScienceContinuous Testing for Machine Learning SystemsValidate the correctness and performance of machine learning systems through the ML product lifecycle.Jul 25, 2021Jul 25, 2021
An elegant way to do feature engineering — feature engineering foundationsAfter repeating creating use case-specific and business logic coupled feature engineering code for a couple of years, I am thinking if it…Mar 9, 2021Mar 9, 2021
Rabbit holes in Machine Learning ProductionlizationMachine learning (ML) is the study of computer algorithms that improve automatically through experience. It has been an important part of…Sep 20, 2020Sep 20, 2020
Demystify Feature StoreIn this day and age, more and more organizations would like to have a uniform tool to deal with their tons of features. For a simple POC…Mar 25, 2020Mar 25, 2020
