Your new company For our Client, new IT Global Hub with location in Katowice, we are currently looking for Machine Learning Engineer. Your new role In this role you will work in a team of data scientists and data engineers on bringing Advanced Analytics models You will develop optimized, scalable, and maintainable Python code for preparing, delivering, and deploying ML models as well as organizing large amount of data You will provide advice to and share your knowledge with data analysts in different business units and in our community of data enthusiasts You will use state-of-the-art cloud technology and continuously extend your knowledge and skills What you'll need to succeed You have at least 3 years practical experience in software engineering You have a proven track record in designing software architecture and developing high quality code and can develop CI/CD and ML pipelines You have expertise in OOP concept and at least one relevant programming language (Python, Java, Scala) Your technological toolbox includes GitHub, CI/CD with GitHub Actions, MLflow, Kubernetes, Jira and Confluence You have an understanding of Data Science and Machine Learning and experiences with MLOps concepts Familiarity with big data technologies such as Apache Spark is a big plus Ideally you are familiar with cloud services and Data Science related components, preferably in MS Azure You bring ability to work in a team and sharing knowledge with team members, combined with a high degree of curiosity, initiative and the motivation to work in an agile and interdisciplinary environment What you'll get in return The company offers unique opportunity of professional development, stable work position in recognized company, additional benefits: private medical care, multi-sport card. The company is located in the center of Katowice's city. What you need to do now If you're interested in this role, click'apply now' to forward an up-to-date copy of your CV, or call us now. Mandatory legal footer to be added at the bottom of job description Hays Poland sp.

This article was published as a part of the Data Science Blogathon. In a typical software development process, the deployment comes at the end of the software development life cycle. First, you build software, test it for possible faults, and finally deploy it for the end user's accessibility. The same can be applied to machine learning as well. In a previous article, I described how we could build a model, wrap it with a Rest API, containerize it, and finally deploy it on cloud services.

In speaking with many machine learning teams, we've found that implementing a model registry has become a priority for AI-first organizations in solving visibility and governance concerns. A model registry is a centralized model store to collaboratively manage the full lifecycle of ML models. This includes model lineage and versioning, moving models between stages from development to staging to production, and model annotations and discovery (i.e., timestamps, descriptions, labels, etc.). ML teams implement a model registry solution to get centralized visibility and management of their models. But there are challenges to adopting a model registry, making it hard to build an up-to-date model registry that contains everything an organization needs.

Continuous integration (CI) is the process of all software developers merging their code changes in a central repository many times throughout the day. A fully automated software release process is called continuous delivery, abbreviated as CD. Although the two terms are not interchangeable, CI/CD is a DevOps methodology and fits in that category. A continuous integration/continuous delivery (CI/CD) pipeline is a system that automates the software delivery process. CI/CD pipelines generate code, run tests, and deliver new product versions when software is changed.

Amazon SageMaker enables developers to create, train, and deploy machine-learning (ML) models and to deploy ML models on embedded systems and edge …

In modern software development teams, continuous integration (CI) and continuous deployment (CD) are standard practices. CI is about how the project should be built and tested in various runtimes, automatically and continuously. CD is needed so that every new bit of code that passes automated testing can be released into production with no extra effort. Adopting CI/CD tools can be very beneficial in ML projects. These tools help you find errors and contradictions in code quickly and, in the long run, reduce the costs of downtime.

Software developers and engineers continue to write or run scripts to glue together components into workflows, even though it is a time-consuming task. However, adopting machine learning or other new technologies that could replace these tried-and-tested scripts can prove to be a challenge for many. In other words, it can be difficult to convince engineers to change how they work. In this edition of The New Stack Makers podcast, Tiffany Jachja, evangelist for software delivery platform provider Harness and Rajsi Rana, senior product manager, Oracle Cloud, discuss scripting and how machine learning, CI/CD and other processes can help guide a shift in engineering culture to make the most of time and resources. Alex Williams, founder and publisher of The New Stack, hosted this episode.

What are the trends in software development over the second half of 2020? This is like no other year. The prevailing issue has turned the world upside down, pushing companies to take on new technology's challenges and analyze their digital strategies. Digital has become the principal (and, in some cases, only) channel of customer interaction and engagement. Enterprises with the digital projects designed to be implemented within the next one to three years need to speed up their initiatives.

We live in an unbelievably rich ecosystem for creating present day web applications. The tools for delivering applications to production, monitoring performances, and deploying in real-time are countless. These tools are so indispensable that modern web application development would be almost impossible without them. By contrast, modern Machine Learning doesn't yet have that same ecosystem. This anomaly arises due to a number of reasons: standardized practices are yet to be established, constant evolution of development tools, and modern Deep learning has been around only for a really miniscule amount of time in the grand scheme of things.

Rosenbaum: This is the video of a machine-learning simulation learning to walk and facing obstacles, and it's there only because I like it. Also, it's a kind of metaphor for me trying to build the CI/CD pipeline. I'm going to be talking about CI/CD for machine learning, which is also being called MLOps. The words are hard, we don't have to really define these things, but we do have to define some other things and we're going to talk about definitions a lot actually. I'm going to start by introducing myself. I'm on the left, this picture is from DevOpsDays Chicago, our mascot is a DevOps Yak. You can come check out the conference. I work for Microsoft on the Azure DevOps team. I come from a developer background, and then, I did a lot of things with DevOps CI/CD and such. I'm not a data scientist, I did some classes on machine learning just so I can get context on this, but I'm coming to this primarily from a developer perspective. I also run another conference, this is a shameless plug, it's DeliveryConf, it's the first year it's happening, it's going to be in Seattle, Washington, on January 21 and 22. You should register for it right now because it's going to be awesome. The first thing I want to do is I want to set an agenda.