Codeless automation testing is performing automation tests without having to write any code. It can be instrumental in executing continuous testing as most automation scripts fail due to the deficiency of proper coding standards. It will also enable us to concentrate more on test creation and analysis instead of fearing getting the code to work, possessing it, and scaling it when required. So, if we are relatively codeless in automation testing, we will find this blog helpful.

The Python ValueError is an exception that occurs when a function receives an argument of the correct data type but an inappropriate value. This error usually occurs in mathematical operations that require a certain kind of value.

Deploying models is becoming easier every day, especially thanks to excellent tutorials like Transformers-Deploy. It talks about how to convert and optimize a Huggingface model and deploy it on the Nvidia Triton inference engine. Nvidia Triton is an exceptionally fast and solid tool and should be very high on the list when searching for ways to deploy a model. Our developers know this, of course, so ClearML Serving uses Nvidia Triton on the backend if a model needs GPU acceleration.

This guide will take you through the steps to build and host an API using Linx. It will cover building a straightforward API to retrieve product data. You will be provided with the data, instructions for what tools to use, relevant scripts and all steps to get the API live. It will take about 20 to 30 minutes to complete all steps.

The data landscape is exploding with tools. As data professionals we have at our fingertips specialized tools for anything: from specialized databases (graph, geo, you name it) to tools for SQL-driven transformations (looking at you, dbt). Yet, a lot of data work is about provisioning, selecting, administering, and just maintaining those tools. Which is just a pain. As Pavel Dolezal, CEO and co-founder of Keboola said: The answer is in how the Modern Data Architecture is built.

Codemagic has recently decreased its prices thanks to Apple M1 machines. How is it possible for us to provide Apple M1 VMs to everybody, including those on a Free plan, and lower the prices at the same time? Codemagic’s CTO Mikhail Tokarev took some time to share the details, including the technical aspects behind our recent changes.

Data integration is the data engineering process of combining data across all the different sources in a company (CRM, SaaS apps like Salesforce, APIs, …) into a single unified view. The data integration process includes data extraction, data cleansing, data ingestion, data validation, modeling, and exposing ready-to-be-consumed data sets to other users and applications for business intelligence or data-driven activities.

Starting a company that works in any field is an exciting journey. We start with probably ten people and almost no user base in the initial days. But if our services are user-centric and our product solves a good problem, we will grow in both these dimensions. When our user base grows, and we expand our services, we generally ask our engineers to make our systems even better for them.

GitHub Copilot has been the subject of some controversy since Microsoft announced it in the Summer of 2021. Whatever your feelings about the matter, Copilot is likely here to stay. So that got me thinking — perhaps there are more important questions to ask about Copilot. If developers are going to use an AI-assisted code generation tool, it would be more productive to think about how to improve it rather than contemplating its right to exist.

This is a guest post with exclusive content by Bill Inmon, Mary Levins, and Georgia Burleson. Bill “is an American computer scientist recognized by many as the father of the data warehouse. Inmon wrote the first book, held the first conference, wrote the first column in a magazine, and was the first to offer classes in data warehousing.” -Wikipedia.