Why you should use Continuous Integration and Continuous Deployment in your Machine Learning projects

CI-CD - Continuous Integration/ Continuous Delivery

Continuous integration (CI), continuous delivery (CD) and continuous testing (CT) are at the core of Machine Learning Operation (MLOps) principles. If you’re a data scientist or machine learning engineer or an IT business leader investing in data science teams, and willing to extend their ML capabilities. MLOps might be the next step that delivers significant value to your business, speeding up development and implementation phases for any machine learning project.

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What’s new about neptune.new


Over time we got a tremendous amount of feedback from you. You asked how to use Neptune with spot instances and pipelines, how to organize things in a more hierarchical way, and many more and we wanted to do that. But adding those improvements to the current product in small increments was getting harder (and slower). Don’t get me wrong we love to iterate, but sometimes you need to take a deep deep deep breath, take a step back and rebuild the foundations. 

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8 Best Data Science and Machine Learning Platforms for MLOps in 2021

MLOps platforms

From gathering a team and preparing data to deployment, monitoring, and management of machine learning models, MLOps takes a lot of time, resources, and time. It’s a complex network of processes and practices that require a certain type of technology. Statistics show that AI technology adoption is still low in businesses as only 14.6% of firms have invested in AI capabilities in production.

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