Systems | Development | Analytics | API | Testing

MLOps for Generative AI with MLRun

The influx of new tools like ChatGPT spark the imagination and highlight the importance of Generative AI and foundation models as the basis for modern AI applications. However, the rise of generative AI also brings a new set of MLOps challenges. Challenges like handling massive amounts of data, large scale computation and memory, complex pipelines, transfer learning, extensive testing, monitoring, and so on. In this 9 minute demo video, we share MLOps orchestration best practices and explore open source technologies available to help tackle these challenges.

Cloud Data Warehouse: A Comprehensive Guide

With the advent of modern-day cloud infrastructure, many business-critical applications like databases, ERPs, and Marketing applications have all moved to the cloud. With this, most of the business-critical data now resides in the cloud. Now that all the business data resides on the cloud, companies need a data warehouse that can seamlessly store the data from all the different cloud-based applications. This is where Cloud Data Warehouse comes into the picture.

Exporting data from Countly through DB Viewer

DB Viewer is a plugin that provides a UI to browse databases. But it is also a great option to access Database data through REST API, for example, to export data. In this article, we will explain how to navigate the data scheme and find all the needed information to export events and their granular data from Countly. ‍ Let's say you have some other database, and you want to populate it with data from Countly. Or you just want to prepare some kind of report through a third-party application.

The Modern Data Ecosystem: Choose the Right Instance

There are several ways to optimize cloud storage, depending on your specific needs and circumstances. Here are some general tips that can help: Overall, optimizing cloud storage requires careful planning, monitoring, and management. By following these tips, you can reduce your storage costs, improve your data management, and get the most out of your cloud storage investment.

Conceptual vs logical vs physical data models

Data modeling is not about creating diagrams for documentation sake. It’s about creating a shared understanding between the business and the data teams, building trust, and delivering value with data. It’s also an investment. An investment in your data systems' stability, reliability, and future adaptability. Like all valuable initiatives, it will require some additional effort upfront.