Determined AI Description
With Determined, you can engage in distributed training without needing to modify your model code, as it efficiently manages the provisioning of machines, networking, data loading, and fault tolerance. Our open-source deep learning platform significantly reduces training times to mere hours or minutes, eliminating the lengthy process of days or weeks. Gone are the days of tedious tasks like manual hyperparameter tuning, re-running failed jobs, and the constant concern over hardware resources. Our advanced distributed training solution not only surpasses industry benchmarks but also requires no adjustments to your existing code and seamlessly integrates with our cutting-edge training platform. Additionally, Determined features built-in experiment tracking and visualization that automatically logs metrics, making your machine learning projects reproducible and fostering greater collaboration within your team. This enables researchers to build upon each other's work and drive innovation in their respective fields, freeing them from the stress of managing errors and infrastructure. Ultimately, this streamlined approach empowers teams to focus on what they do best—creating and refining their models.
Determined AI Alternatives
Vertex AI
Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case.
Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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OORT DataHub
Our decentralized platform streamlines AI data collection and labeling through a worldwide contributor network. By combining crowdsourcing with blockchain technology, we deliver high-quality, traceable datasets.
Platform Highlights:
Worldwide Collection: Tap into global contributors for comprehensive data gathering
Blockchain Security: Every contribution tracked and verified on-chain
Quality Focus: Expert validation ensures exceptional data standards
Platform Benefits:
Rapid scaling of data collection
Complete data providence tracking
Validated datasets ready for AI use
Cost-efficient global operations
Flexible contributor network
How It Works:
Define Your Needs: Create your data collection task
Community Activation: Global contributors notified and start gathering data
Quality Control: Human verification layer validates all contributions
Sample Review: Get dataset sample for approval
Full Delivery: Complete dataset delivered once approved
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DagsHub
DagsHub serves as a collaborative platform tailored for data scientists and machine learning practitioners to effectively oversee and optimize their projects. By merging code, datasets, experiments, and models within a cohesive workspace, it promotes enhanced project management and teamwork among users. Its standout features comprise dataset oversight, experiment tracking, a model registry, and the lineage of both data and models, all offered through an intuitive user interface. Furthermore, DagsHub allows for smooth integration with widely-used MLOps tools, which enables users to incorporate their established workflows seamlessly. By acting as a centralized repository for all project elements, DagsHub fosters greater transparency, reproducibility, and efficiency throughout the machine learning development lifecycle. This platform is particularly beneficial for AI and ML developers who need to manage and collaborate on various aspects of their projects, including data, models, and experiments, alongside their coding efforts. Notably, DagsHub is specifically designed to handle unstructured data types, such as text, images, audio, medical imaging, and binary files, making it a versatile tool for diverse applications. In summary, DagsHub is an all-encompassing solution that not only simplifies the management of projects but also enhances collaboration among team members working across different domains.
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ClearML
ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.
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Integrations
Company Details
Company:
Determined AI
Headquarters:
United States
Website:
www.determined.ai/
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support
Determined AI Features and Options
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