MLflow VS H2O

AI Model Lifecycle Management:MLflow Documentation

MLflow provides developers with comprehensive tools for managing the entire ML lifecycle. Its four primary components—tracking, models, projects, and model registry—facilitate efficient, reproducible, and scalable ML pipeline building.

MLOps elevates DevOps principles to AI workloads. The new frontiers of DevOps are managing large model training jobs, handling GPUs, and automating ML pipelines. Python tools like MLflow and Kubeflow make this possible.

Model management: MLflow, Kubeflow are used for managing the deployment and lifecycle of models.

Popular tools for model development are TensorFlow, MLFlow, and PyTorch.

As artificial intelligence (AI) projects grow in complexity, managing dependencies, maintaining reproducibility, and deploying models efficiently become critical challenges. These processes require tools that can streamline development, tracking, and deployment. Tools like KitOps and MLflow simplify these workflows by automating key aspects of the machine learning (ML) project lifecycle. KitOps simplifies the AI project setup, while MLflow keeps track of and manages the machine learning experiments. With these tools, developers can create robust, scalable, and reproducible ML pipelines at scale.

MLflow is an open source platform for managing the machine learning project lifecycle, from model development to deployment and performance evaluation. It is beneficial for several reasons.

6. MLflow

Star on GitHub ⭐ - MLflow

MLflow is an open source platform for managing end-to-end machine learning lifecycle —including experimentation, reproducibility, and deployment. It supports strong governance by tracking data and validating the models. It allows the machine learning teams to log and manage experiments, including model metrics, parameters, and artifacts. This facilitates the reproducibility of results, which is crucial for transparency in AI systems.

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I would use H20 if I were you. You can try out LLMs with a nice GUI. Unless you have some familiarity with the tools needed to run these projects, it can be frustrating. https://h2o.ai/

H2O.ai

Thanks for the input. To clarify, I am more focused on choosing the modeling framework(s) that makes the most sense to use for future production. For example, is h2o.ai a good framework for training models for later deployment (through something like elastic beanstalk, Flask API's etc.)? I came across a number of mentions of Tensorflow, however it is focused on neural nets while I also want to use classic models such as random forests, etc.

I've also initialised an instance of H2O.ai, so I can parse into the server each product, by store, segmented. It can then train the models, determine which model is the most performant, and then save it. Because the variability of different product SKU, at different hospitals, is substantial.

H2O.ai H2O is a deep learning tool built in Java. It supports most widely used machine learning algorithms and is a fast, scalable machine learning application interface used for deep learning, elastic net, logistic regression, and gradient boosting.

H20