CPA Trendlines’ Post

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/TDdMDD

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

Securing AI-generated code in an environment “driven by hype and tactical concerns rather than proceeding in a mature, strategic, sensible way”- by Chris Middleton in diginomica about research done by Venafi. Key concepts/ideas: 2/3rds of security professionals report it’s impossible to keep up with their AI-enabled DevOps teams. 78% believe adoption of AI developed code will lead to security teams losing control of safety within the enterprise. 47% don’t have policies in place ro ensure safe use of AI in DevOps. 63% have considered banning use of AI in coding because of security risks.

AI in coding and DevOps – research reveals a security black hole. As a new survey finds a familiar pattern in AI adoption – jumping in against one’s professional better judgement, Chris Middleton speaks to Venafi, the company behind the research. bit.ly/4eg7gBE #AIinCoding #AIinDevOps

A new post on my blog. Let's talk a bit about MLOps today: https://lnkd.in/ecn6TniT

Fascinating deep dive into CI/CD tools. Been exploring how different teams are optimizing their pipelines with tools like Jenkins, GitLab, GitHub. Love seeing how AI and security are reshaping modern CI/CD.  If you're evaluating tools for your team, this is super helpful: https://lnkd.in/e2cyyXXH

MLOps, an abbreviation for Machine Learning Operations, has become a buzzword in the world of AI. In simple terms, MLOps is the practice of managing and streamlining the lifecycle of machine learning models, from development and testing to deployment and monitoring in production. 🔎 Here's a deep dive into the main components of MLOps, its benefits, and how it differs from DevOps and AIOps: https://lnkd.in/g6asKfPt

Fascinating deep dive into CI/CD tools. Been exploring how different teams are optimizing their pipelines with tools like Jenkins, GitLab, GitHub. Love seeing how AI and security are reshaping modern CI/CD.  If you're evaluating tools for your team, this is super helpful: https://lnkd.in/ghZ2Jhxg

New to MLOps? Marcello Politi presents an accessible introduction to working with MLflow pipelines.

I've been asked to explain my local LLM workflow, so here it is! Day 0 on my introduction to RAG LLM devops, we are starting out simple by looking into what a "router query engine" is, why it is important, and how to use tools like LlamaIndex to easily configure your own: https://lnkd.in/g3MUKgs6