In this article, we will walk you through steps to run a Jenkins server in docker and deploy the MLRun project using Jenkins pipeline. Before we dive into the actual set up, let’s have a brief background on the MLRun and Jenkins.
It says something about a company and its people when they drop the process of formulaic job interviews and just let you pitch ideas for the job you want. That’s what happened when I applied to Continual as a Technical Marketing Manager. Five weeks in, I’m pleased to say I’m working on those same ideas, which I’ll detail in a couple minutes.
We’re so proud to share that Iguazio has been named a sample vendor in eight Gartner Hype Cycles in 2022: Iguazio was mentioned in the following categories: MLOps, Logical Feature Store, Adaptive ML, Data-Centric AI, AI Engineering, AI TRiSM, Operational AI Systems, ModelOps, AI Engineering in HCLS and Continuous Intelligence. We are delighted to have been mentioned alongside global industry leaders like AWS, IBM, Microsoft, Google, Databricks and Dataiku.
Fully automatic retraining loop using ClearML Data Right, so you want to create a fully automatic retraining loop, that you can set up once and then pretty much forget about. Where do we even start?!
Machine learning is more accessible than ever, with datasets available online and Jupyter notebooks providing an easy way to explore and train models. In building a model, we often forget that it will be incorporated into an application that will provide value to the user. Therefore, we wanted to demonstrate how we can "use" the models we build in an application.
With the rise of drone usage, it’s easier to take aerial footage than ever before. The resulting data can trigger quick, effective action; removing guesswork and increasing aerial awareness, which can have profound implications on growing profits and trimming expenses. And as drone use rises, so does the usage of AI, to navigate, detect, identify, and track meaningful artifacts and objects.
Machine Learning is revolutionizing the world of healthcare. ML models can help predict patient deterioration, optimize logistics, assist with real-time surgery and even determine drug dosage. As a result, medical personnel are able to work more efficiently, serve patients better and provide higher quality healthcare.
In your machine learning projects, have you ever wondered “why is model Y is performing better than Z, which dataset was model Y trained on, what are the training parameters I used for model Y, and what are the model performance metrics I used to select model Y?” Does this sound familiar to you? Have you wondered if there is a simple way to answer the questions above? Data science experiments can get complex, which is why you need a system to simplify tracking.
Sometimes the need for processing power you or your team requires is very high one day and very low another. Especially in machine learning environments, this is a common problem. One day a team might be training their models and the need for compute will be sky high, but other days they’ll be doing research and figuring out how to solve a specific problem, with only the need for a web browser and some coffee.
