Distributed ML Ops
Distributed Machine Learning Operations is very important for delivering Machine Learning Projects to our customers.
MLReef
MLReef
MLReef
Overview
What is MLReef?
MLReef is a Machine Learning development platform that aims to democratize ML innovation across the entire organization.
Distributed ML Development:
- up to 5X in ML development throughput
- up to 85% less dependency on internal data science capacity
-...
Read moreDistributed ML Development:
- up to 5X in ML development throughput
- up to 85% less dependency on internal data science capacity
-...
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Pricing
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What is MLReef?
MLReef is a Machine Learning development platform that aims to democratize ML innovation across the entire organization. Distributed ML Development: - up to 5X in ML development throughput - up to 85% less dependency on internal data science capacity - Distributed workload on…
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- Premium Consulting / Integration Services
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What is Hugging Face?
Hugging Face is an open-source provider of natural language processing (NLP) technologies.
What is Azure Machine Learning?
Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.
Product Details
- About
- Integrations
- Competitors
- Tech Details
- Downloadables
What is MLReef?
MLReef is a Machine Learning development platform that aims to democratize ML innovation across the entire organization.
Distributed ML Development:
- up to 5X in ML development throughput
- up to 85% less dependency on internal data science capacity
- Distributed workload on complex data tasks with seamless involvable domain experts
- Higher acceptance of deploye models ad development is a joint task
Q: What is Distributed ML Development?
Distributed Machine Learning development is the process by which the value-added chain is structurally distributed to different actors across the organization to drive efficiency, transparency, quality and to democratize the knowledge and capacity to create Machine Learning.
Distributed ML Development:
- up to 5X in ML development throughput
- up to 85% less dependency on internal data science capacity
- Distributed workload on complex data tasks with seamless involvable domain experts
- Higher acceptance of deploye models ad development is a joint task
Q: What is Distributed ML Development?
Distributed Machine Learning development is the process by which the value-added chain is structurally distributed to different actors across the organization to drive efficiency, transparency, quality and to democratize the knowledge and capacity to create Machine Learning.
MLReef Features
- Supported: AI Modules: Create AI Modules from fully flexible Git repositories
- Supported: ML Pipelines: Empower everyone to participate in the ML value chain with low/no code
- Supported: Data Management: Make data processing a collaborative and connected work
- Supported: Experiment tracking: Run experiments iteratively and with full reproducibility
- Supported: Nautilus: Host MLReef on any infrastructure - on the cloud or on-premises
MLReef Screenshots
MLReef Video
Fast prototyping with MLReef (Tutorial!)
MLReef Integrations
MLReef Competitors
- cnvrg.io
- DataIku
- Clarifai
MLReef Technical Details
| Deployment Types | On-premise, Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Windows, Linux |
| Mobile Application | No |
| Supported Countries | Global |
| Supported Languages | English |
MLReef Downloadables
MLReef Customer Size Distribution
| Consumers | 40% |
|---|---|
| Small Businesses (1-50 employees) | 8% |
| Mid-Size Companies (51-500 employees) | 24% |
| Enterprises (more than 500 employees) | 28% |
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Reviews and Ratings
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March 16, 2022
Distributed ML Ops
Score 10 out of 10
Vetted Review
Verified User
Distributed Machine Learning Operations is very important for delivering Machine Learning Projects to our customers.
- Helps us to take on more client projects
- Can be used by data analysts as well as casual users
- Out of the box support for major cloud vendors
- Allowing distributed learning
- Easy to learn
- We can handle 4 to 6 times more projects at the same time with our team
- We stay engaged with our customers well beyond the project duration