Alex Lichtenberger’s Post

DevOps vs MLOps It can be confusing with so many terms popping up everyday. You may think you just learned what DevOps is and here we have a keyword popularized with rise of machine learning: MLOps If you want to understand the concept, Google has a page for that: https://buff.ly/3XN7ych

AI in coding and DevOps – research reveals a security black hole: A new survey finds a familiar pattern in AI adoption – jumping in against one’s professional better judgement. We speak to the company behind the research. http://dlvr.it/TDdMCy

Just finished the course “DevOps Foundations: Continuous Delivery/Continuous Integration” by Ernest Mueller and James Wickett! Check it out: https://lnkd.in/d5mC2dGA . . #devops #continuousintegrationandcontinuousdeliverycicd #google #cicd #skills #ml #ai

AI in coding and DevOps – research reveals a security black hole: A new survey finds a familiar pattern in AI adoption – jumping in against one’s professional better judgement. We speak to the company behind the research. http://dlvr.it/TDdMCq

Getting RAG Into Production With ML Ops Are you curious how MLOps can streamline the integration of RAG systems into production environments? Machine Learning Operations (MLOps) is a specialized branch within machine learning (ML) engineering that focuses on streamlining the deployment and maintenance of machine learning models in production environments. In today's data-driven landscape, the role of MLOps has become increasingly crucial as it addresses the complexities involved in the machine learning lifecycle, from development and training to deployment, monitoring, and governance. By integrating practices from DevOps, MLOps enables collaboration between data scientists, DevOps engineers, and IT professionals, thereby enhancing the efficiency, scalability, and reliability of machine learning solutions. Register for this online video webinar to learn about ML Ops and how to streamline RAG into production environments. https://lnkd.in/g2EqRv2b

Looking to streamline your deployments and make CI/CD a breeze? Check out our latest blog post where we share how we use PM2 and Github Actions to automate deployments and testing at Daily-Digest. We’ll walk you through setting up a lightweight CI/CD pipeline, complete with GitHub Actions and PM2 configurations. Curious? Read here: https://lnkd.in/e2rjfueU #dailydigest #cicd #devops #ai #whateverHashTag

5 must-have tools for large AI code repositories: 1. Git - to know when something changed. 2. Continuous Integration testing - to know if all tests are green, today. 3. Docker - to keep consistency between platforms. 4. Pytorch lightning - to write maintainable AI code. 5. Code formatting - to keep code syntax sane. The list of could-have tools is much longer.. Which ones do you see as essential?

Getting RAG Into Production With ML Ops Are you curious how MLOps can streamline the integration of RAG systems into production environments? Are you curious how MLOps can streamline the integration of RAG systems into production environments? Machine Learning Operations (MLOps) is a specialized branch within machine learning (ML) engineering that focuses on streamlining the deployment and maintenance of machine learning models in production environments. In today's data-driven landscape, the role of MLOps has become increasingly crucial as it addresses the complexities involved in the machine learning lifecycle, from development and training to deployment, monitoring, and governance. By integrating practices from DevOps, MLOps enables collaboration between data scientists, DevOps engineers, and IT professionals, thereby enhancing the efficiency, scalability, and reliability of machine learning solutions. Register for this online video webinar to learn about ML Ops and how to streamline RAG into production environments. https://lnkd.in/gAH6b3gf

Exploring Continuous Integration (CI) in Machine Learning reveals how it’s shaping model development. By integrating changes into a shared codebase frequently, CI helps catch issues early and ensures models evolve smoothly with new data or code updates. Tools like GitHub Actions and GitLab CI/CD are powerful allies for automating these workflows, reducing manual checks, and enhancing model reliability over time.

Rachel Stephens nails this analysis. Although coding assistants are where early AI investment has gone, the coding part isn’t the bottleneck preventing engineering organizations from having elite velocity and stability. It’s further down the SDLC chain. https://lnkd.in/gMTmawY3