HotBits Python API
MLflow
| HotBits Python API | MLflow | |
|---|---|---|
| - | 68 | |
| 2 | 18,965 | |
| - | 1.9% | |
| 0.0 | 9.9 | |
| almost 5 years ago | 7 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
HotBits Python API
We haven't tracked posts mentioning HotBits Python API yet.
Tracking mentions began in Dec 2020.
MLflow
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How to Use KitOps with MLflow
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.
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20 Open Source Tools I Recommend to Build, Share, and Run AI Projects
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.
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Top 10 MLOps Tools for 2025
6. MLflow
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Top 8 OpenSource Tools for AI Startups
Star on GitHub ⭐ - MLflow
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10 MLOps Tools That Comply With the EU AI Act
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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[Python] How do we lazyload a Python module? - analyzing LazyLoader from MLflow
One day I was hopping around a few popular ML libraries in Python, including MLflow. While glancing at its source code, one class attracted my interest, LazyLoader in __init__.py (well, this actually mirrors from the wandb project, but the original code has changed from what MLflow is using now, as you can see).
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Essential Deep Learning Checklist: Best Practices Unveiled
Tools: Implement logging using tools like MLFlow or Weights & Biases (W&B), which provide a structured way to track experiments, compare them visually, and share findings with your team. These tools integrate seamlessly with most machine learning frameworks, making it easier to adopt them in your existing workflows.
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Accelerating into AI: Lessons from AWS
CometML and mlMLFlow are popular development and experimentation tools, although some express concerns about their proprietary and weak data storage with its lack of tamper-proof guarantees.
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10 Open Source Tools for Building MLOps Pipelines
MLflow is an open source MLOps tool that allows users to manage the entire life cycle of machine learning models. It has four key components:
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A step-by-step guide to building an MLOps pipeline
Experiment tracking tools like MLflow, Weights and Biases, and Neptune.ai provide a pipeline that automatically tracks meta-data and artifacts generated from each experiment you run. Although they have varying features and functionalities, experiment tracking tools provide a systematic structure that handles the iterative model development approach.
What are some alternatives?
PyBrain
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
Surprise - A Python scikit for building and analyzing recommender systems
zenml - ZenML 🙏: The bridge between ML and Ops. https://zenml.io.
xeger - Library to generate random strings from regular expressions.
guildai - Experiment tracking, ML developer tools
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
dvc - 🦉 Data Versioning and ML Experiments
tensorflow - An Open Source Machine Learning Framework for Everyone
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.