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Machine Learning

Build an AI App in Under 20 Minutes

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.

[MLOPS] From experiment management to model serving and back. A complete usecase, step-by-step!

The recording of our talk at the MLOps World summit. This talk covers a complete example, starting from experiment management and data versioning, building up into a pipeline and finally deploying using ClearML serving with drift monitoring. We then induce artifical drift to trigger the monitoring alerts and go back down the chain to quickly retrain a model and deploy it using canary deployment.

MLOps World Toronto: MLOps Beyond Training Simplifying and Automating the Operational Pipeline

Most data science teams start with building AI models and only think about operationalization later. But taking a production-first approach and automating components is the key to generating measurable ROI for the business. In this talk, Iguazio’s co-founder and CTO, Yaron Haviv, explains how to simplify and automate your production pipeline to bring data science to production faster and more efficiently. He displays real live use cases while going through all the different steps in the process.

How SightX Uses ClearML to Build AI Drone Models

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.

Top 27 Free Healthcare Datasets for Machine Learning

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.

Breaking AI Bottlenecks with NetApp + Iguazio + AWS FSx for NetApp ONTAP

Teams facing implementation challenges need a way to scale their operational pipelines, continuously roll out AI services faster, support real-time use cases and enable deployment in hybrid environments. The NetApp-AWS-Iguazio integrated FSx solution offers a one-stop-shop from storage to production, with full end-to-end MLOps capabilities—even at scale and in real-time.

[TALK] Model Serving Monitoring and Traceability: The Bigger Picture

The recording of our talk at the AI infrastructure alliance micro summit. This talk covers ClearML serving including monitoring and focuses on the importance of being able to trace the deployed model all the way back to the original experiment, code and data that were used to train it! One of the mayor advantages of a single tool end-to-end MLOps workflow.

Model Serving Monitoring and Traceability - The Bigger Picture - The AIIA Summit 2022

Watch our great evangelist Victor Sonck in the AIIA summit! How can you go in the bigger picture of model observability? Well, the short answer is complete traceability. And what does that mean? Find out for yourself in Victor's short and insightful talk. ClearML is an open source ML / DL experiment manager, versioning and ML-Ops full system solution.

Machine Learning Experiment Tracking from Zero to Hero in 2 Lines of Code

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.