Pojd’me si to ujasnit hned na začátku: nesnáším, když si někdo dělá srandu z mé češtiny. Proč? Je to velmi jednoduché: je miliarda důvodů, proč zrovna neříkám určité slovo nebo frázi správně. Je určitě časté, že nevím, jak se slovo píše nebo vyslovuje správně. Ale je taky možné, že mi…

A Wake-Up Call for Everyone in the Data Science Industry

Whether we are talking about self-driving cars, QR code readers, personalized feed on YouTube, or approvals of loan requests — ML models are more and more often driving automation and “smart” decisions behind. Indeed, building such solutions is a complex but extremely exciting task. …

How we set up automatic tracing, versioning, and synchronization of project environment

Reproducibility and traceability belong to the crucial requirements of a reliable system. No continuous delivery nor efficient collaboration is possible without these two features. In fact, any kind of delivery is hardly imaginable without a precise specification of the environment.

Fortunately, various tools and methods exist, such as package managers…

Model hosting using SageMaker

AWS SageMaker provides deployment options for batch and real-time predictions. We already discussed batch processing in the first post of the series. In this post, let’s take a look at SageMaker hosting service, which is tightly coupled with Sagemaker jobs. …

Photo by Mr TT on Unsplash

“[DataSentics mission is to] make data science and machine learning have a real impact on organizations across the world — demystify the hype and black magic surrounding AI/ML and bring to life transparent production-level data science solutions and products delivering tangible impact and innovation.”

You can recognize the values of…

We all have to give SageMaker service a credit — it enhances capabilities and releases new features steadily. One of them is SageMaker jobs. They provide a way to process data, train, and evaluate models using algorithms provided by SageMaker or custom ones.

AWS SageMaker always uses Docker containers when…

Part Two: How a Data Science Project Can Benefit from Docker?

photo from https://www.pexels.com/@cottonbro

The post suggests how you can benefit from Docker on different stages of a data science project from research to deploying to data and application monitoring in production. In particular, I discuss advantages as well as aspects to consider when applying Docker technology to

  • Examine an existing solution
  • Prepare development…

Part One: Critical Q&A for better understanding of Docker and Container Orchestration

photo from https://www.pexels.com/@suzyhazelwood

Let's take a look at some important whys and hows on virtualization, containerization, Docker, and container orchestration. The post is not intended to explain the concepts itself, but to answer some of the key questions required for a better understanding of the underlying technologies and to introduce practical use cases.

From https://giphy.com/

The 1st of January. It is about zero celsius. Extremely sunny, no wind, no rain, no snow. Here it is — my self-organized one-person first-in-life marathon. I am running. Why? To prove I can do it, to make something I had never done before. I already did a similar thing…

“As ironic as it seems, the challenge of a tester is to test as little as possible. Test less, but test smarter.” — Federico Toledo

There is a widely held opinion that having any intelligence written in SQL is a bad, bad idea — it is hard to maintain, release…

Anastasia Lebedeva

Developer, Runner, Adventure Lover

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