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Trademarks: This software listing is packaged by Data Science Dojo. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

About the offer:

ZenML is an open-source framework designed to simplify and accelerate the development of end-to-end machine learning pipelines. It provides a high-level abstraction layer that allows users to focus on defining their ML workflows without worrying about the underlying infrastructure, data management, or processing logic. ZenML supports a wide range of data sources, feature engineering techniques, model architectures, and deployment targets. Additionally, ZenML integrates with popular cloud platforms and data stores, enabling users to scale their ML workflows seamlessly.

Who benefits from this offer:

Data scientists
Machine learning engineers
Software developers
Data engineers

What is included in this offer:

ZenML is a comprehensive machine learning framework that provides various features to simplify and accelerate the development of end-to-end ML pipelines. The tool and its pre-requistes essential for its installation covered by us are:

OS: Ubuntu Server 20.04 LTS
Environment: Docker version 23.0.2, build 569dd73
Interpreter: Python 3.8.16
Tool: zenml, version 0.37.0

and other necessary customization and settings for compatibility.

Technical Specifications:

Minimum memory: 4 GB
Minimum vCPU: 2 vCPUs
Operating System: Operating System: Ubuntu 18.04 LTS or newer, Debian 9 or newer, CentOS 7 or newer, or Fedora 30 or newer.
Storage: 10 GB

Why ZenML?

Data Versioning: ZenML tracks changes to data to ensure reproducibility and traceability.
Data Preprocessing: ZenML provides built-in preprocessing techniques to help users clean and transform their data.
Data Pipelines: ZenML enables users to define complex data pipelines with ease.
Data Integration: ZenML integrates with popular data stores and cloud platforms to make data management seamless.
Experiment Tracking: ZenML allows users to track experiments and compare model performance.
Hyperparameter Tuning: ZenML provides tools for hyperparameter tuning to optimize model performance.
Model Versioning: ZenML tracks changes to model configurations to ensure reproducibility and traceability.
Cloud Support: ZenML can leverage cloud resources to accelerate and scale machine learning workflows.
Kubernetes Integration: ZenML integrates with Kubernetes for container orchestration and deployment.
Pipeline Orchestration: ZenML allows users to define and orchestrate complex ML workflows.
Reproducibility: ZenML ensures reproducibility by tracking changes to data, models, and configurations.
Automation: ZenML automates repetitive tasks, such as data preprocessing and model evaluation.
Plugin System: ZenML provides a plugin system for users to extend its functionality and integrate with third-party tools.
Integration with Third-party Tools: ZenML integrates with popular ML libraries, such as TensorFlow and PyTorch.
Community Support: ZenML has an active community of users and contributors who provide support and share best practices.

Getting Started:

To access ZenML's deployed web interface follow the steps:

SSH into VM(Virtual Machine)
Enter this command: sudo docker start $(sudo docker ps -aqf "status=exited")
Wait for about a minute, then Navigate to http://your_ip:8080
By default the username is "default" and password field will remain empty. You can later change the credentials after logging in.

ZenMLData Science Dojo

ZenML

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ZenML

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ZenML
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