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ML Tools

The document outlines various machine learning tools categorized into six main types: Cloud-Based ML Platforms, AutoML Platforms, MLOps & Experiment Tracking Tools, Model Deployment & Serving Tools, Data Labeling & Annotation Tools, and Reinforcement Learning Platforms. Each category includes notable platforms and services like Google Cloud AI, Amazon SageMaker, and OpenAI Gym, highlighting their specific functionalities. These tools provide comprehensive solutions for different stages of the machine learning lifecycle, from data preparation to model deployment.

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Rishit Goel
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47 views2 pages

ML Tools

The document outlines various machine learning tools categorized into six main types: Cloud-Based ML Platforms, AutoML Platforms, MLOps & Experiment Tracking Tools, Model Deployment & Serving Tools, Data Labeling & Annotation Tools, and Reinforcement Learning Platforms. Each category includes notable platforms and services like Google Cloud AI, Amazon SageMaker, and OpenAI Gym, highlighting their specific functionalities. These tools provide comprehensive solutions for different stages of the machine learning lifecycle, from data preparation to model deployment.

Uploaded by

Rishit Goel
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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machine learning tools that are not just libraries but full-fledged platforms, software, and

services:

1. Cloud-Based ML Platforms

​ •​ Google Cloud AI – ML platform offering AutoML, Vertex AI, and TensorFlow


support.

​ •​ Amazon SageMaker – End-to-end ML platform for training, deployment, and


monitoring.

​ •​ Microsoft Azure ML – Cloud-based ML platform with drag-and-drop model


building.

​ •​ IBM Watson ML – AI-powered cloud service for ML and deep learning.

2. AutoML Platforms

​ •​ Google AutoML – Automates ML model training with minimal user input.

​ •​ H2O.ai Driverless AI – AutoML tool for building models without manual tuning.

​ •​ DataRobot – Enterprise AutoML platform for automated feature engineering and


modeling.

3. MLOps & Experiment Tracking Tools

​ •​ MLflow – Open-source ML lifecycle management platform.

​ •​ Weights & Biases (W&B) – Tracks experiments, model performance, and


hyperparameters.

​ •​ Neptune.ai – Experiment tracking and model monitoring tool.

​ •​ DVC (Data Version Control) – Version control system for ML datasets and
experiments.

​ •​ Kubeflow – End-to-end ML workflow management tool on Kubernetes.

4. Model Deployment & Serving Tools

​ •​ TensorFlow Serving – Deploys trained models efficiently.

​ •​ ONNX (Open Neural Network Exchange) – Interoperability tool for different ML


frameworks.

​ •​ BentoML – Scalable model serving and deployment.


​ •​ Seldon Core – Open-source platform for deploying ML models at scale.

5. Data Labeling & Annotation Tools

​ •​ Labelbox – Data annotation platform for image, text, and video labeling.

​ •​ SuperAnnotate – AI-powered annotation platform for ML datasets.

​ •​ Amazon SageMaker Ground Truth – Data labeling service for ML training


datasets.

6. Reinforcement Learning (RL) Platforms

​ •​ OpenAI Gym – RL environment for training and testing agents.

​ •​ Unity ML-Agents – RL toolkit for game-based environments.

​ •​ Microsoft Project Bonsai – RL and control system training platform.

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