Systems | Development | Analytics | API | Testing

Build/Buy in MLOPs for R&D Does "off-the-shelf" exist yet?

What kind of tools and infrastructure does a company need in order to build, train, validate and maintain data-based models as part of products? The straight answer is - “it depends.” The longer one is: “MLOps.” It is far too early to determine the “best” patterns and workflows for Data-Science, Machine- and Deep-Learning products. Yet, there are numerous examples of successful deployments from businesses both big and small.

ETL with Apache Airflow

Written in Python, Apache Airflow is an open-source workflow manager used to develop, schedule, and monitor workflows. Created by Airbnb, Apache Airflow is now being widely adopted by many large companies, including Google and Slack. Being a workflow management framework, Apache Airflow differs from other frameworks in that it does not require exact parent-child relationships. Instead, you only need to define parents between data flows, automatically organizing them into a DAG (directed acyclic graph).

Organizations Grapple with Skyrocketing Cloud Costs, Anodot Survey Finds

The pandemic upended business for many or at the very least cast a grim shade of uncertainty, so, as many took to working from home, they also were commissioned with cutting waste. Among the biggest sources of misspend in 2020 – cloud services. And remote work may have actually spurred the problem, as organizations migrate more applications to the cloud to support these workers.

Accelerating AI-based search in the cloud with ThoughtSpot for Snowpark

We’re all familiar with how Google Search revolutionized information processing for consumers. The ingenious combination of AI with a new way to organize content on the web created a user-friendly experience that forever changed how the world finds relevant information on the internet.