When I first joined Cash Converters, the data science setup was pretty raw. There was no proper repository for training data or models. Just a few Python files and few pickled model. That was it. Reproducibility was non-existent. There was no way to ...
DatOps and MLOps are increasingly essential as organizations strive to gain true business value from their data and AI investments. Imagine Netflix, whose success hinges on delivering spot-on recommendations to millions nightly. Each time you log in ...
By Shubham SahuTags: Azure Data Factory, CI/CD, Azure DevOps, Data Engineering, YAML 🚀 Introduction Modern data engineering teams need a fast, reliable, and repeatable way to deploy Azure Data Factory (ADF) pipelines across multiple environments (de...
When teams talk about MLOps, the focus usually goes straight to model training, deployment, or monitoring. But there's a quiet hero in the ML pipeline that often gets overlooked: The Feature Store. You might ask - why all the fuss about storing featu...
[ Included: the architecture, YAMLs, and Python Code for the 2 microservices (Athena→SQS & SQS→SageMaker) to make this pipeline work. ] Secured Data Pipeline: Secure your (Athena, SQS) Credentials in Vault/Secret Manager for the Pods to Query Them P...
DataOps is revolutionizing the way businesses manage and deploy data workflows, ensuring error-free production and faster deployment cycles. BERGH et al. (2019) outlined seven key best practices to implement a robust DataOps architecture. These steps...
In today’s data-driven world, data pipelines are the backbone of efficient and scalable DataOps. These pipelines are vital for managing both data and code, automating complex workflows, and minimizing manual data handling. Data pipelines can be descr...
Data is a valuable asset for the most companies in the 21st century. Like other assets data needs to be managed over the whole lifecycle. Mismanagement of data can result in many risks like: data losses or breaches resulting in disclosure of private ...
DataOps, a fusion of "Data" and "Operations," addresses the challenges of developing data products by combining principles from Agile, DevOps, and Lean Manufacturing. It emphasizes collaboration, automation, and efficiency in handling data pipelines,...
The development of data products is an intricate process, blending the complexities of data and code. Unlike traditional software development, the data dimension adding additional unique challenges. Data must be available, understood, and accurate. T...
