Cleora.ai

0.00

Cleora.AI creates high-quality embeddings for data science without expensive hardware.

Visit website

What is Cleora.ai?

Cleora.AI is a powerful machine learning tool designed to assist Data Science and Analytics teams in efficiently creating high-quality enterprise embeddings. It eliminates the need for expensive hardware, making the process accessible and scalable. Cleora embeds entities such as customers, products, and other business-related data into n-dimensional spherical spaces through a stable and iterative projection method. This results in embeddings that reflect an entity's history of behavior, represented as large-scale graphs. The technology allows organizations to develop advanced models for recommender systems, client segmentation, propensity prediction, churn prediction, and lifetime value modeling by simply extracting three columns from their databases. Cleora is known for its speed, efficiency, inductivity, and ability to combine embeddings across datasets. It offers automatic scaling, improved performance, and superior embedding quality in its PRO version for enterprises.

Key Features of Cleora.AI:

Efficiency: Cleora is faster than competing systems and capable of handling graphs with billions of edges without GPUs.
Ease of Use: Requires only three columns from the database with automatic graph detection.
Cross-dataset Compositionality: Stability in embeddings enables meaningful vector combinations from multiple datasets.
Inductive Capabilities: Can compute vectors for new entities based on interactions with other entities.
Automatic Scaling: Cleora PRO provides automatic scaling features, eliminating the need for expensive hardware.

Cleora.AI is up to 200x faster than DeepWalk and 4x-8x faster than Pytorch-BigGraph by Facebook, showcasing exceptional speed in generating embeddings. Additionally, Cleora can efficiently handle billion-scale graph data and process National Capital Region graph data in less than 5 minutes.

Who created Cleora.ai?

Cleora.ai was created by a lab named Sair, which is focused on behavioral modeling, recommendations, and large-scale data processing. The company behind Cleora.ai aims to provide a powerful machine learning tool for creating high-quality enterprise embeddings efficiently and cost-effectively. The platform is designed to eliminate the need for expensive hardware, making it accessible and scalable for Data Science and Analytics teams. Cleora.ai offers both a PRO version for enterprises and an Open Source version on Github, known for its speed, efficiency, and the ability to combine embeddings across datasets.

What is Cleora.ai used for?

Anomaly detection
Recommender systems
Client segmentation
Propensity prediction
Churn prediction
Lifetime value modeling
Automatic scaling
Efficiency in embedding large graphs
Cross-dataset compositionality
Inductive capabilities for computing vectors for new entities
Combining embeddings across datasets
Profile scoring
Profile matching
Hyper-segmentation
Time to event predictions
Lifetime value modeling
Uplift estimation

Who is Cleora.ai for?

Data scientists
Data Analysts
Machine learning engineers
AI researchers
Business analysts
Data & ML/AI Engineers
Analytics teams
Data engineers
ML/AI Engineers

How to use Cleora.ai?

To use Cleora.ai, follow these steps:

Efficiency: Cleora is notably faster than other systems, capable of embedding graphs with billions of edges without requiring GPUs.
Ease of Use: Simply extract three columns from your database, and Cleora automatically detects graphs in the data.
Cross-dataset Compositionality: Stable embeddings allow for meaningful vector combinations by averaging embeddings from multiple datasets.
Inductive Capabilities: Cleora can compute vectors for new entities based on their interactions with other entities.
Automatic Scaling: Cleora PRO provides automatic scaling capabilities, eliminating the need for costly hardware.
Creating Models: Build models for various purposes like recommender systems, client segmentation, churn prediction, etc., by extracting three columns from databases.

Cleora.AI offers additional benefits through its private beta for Cleora PRO, providing automatic scaling, improved performance, and superior embedding quality. The tool can handle billion-scale graph data efficiently and quickly, as demonstrated by processing graph data in the National Capital Region in under 5 minutes.

Moreover, Cleora PRO is significantly faster than DeepWalk and Pytorch-BigGraph by Facebook, making it an exceptional choice for embedding generation. It can efficiently process and represent entities such as clients, products, stores, and accounts with behavioral embeddings shaped as large graphs.

Overall, Cleora.AI streamlines data science and analytics tasks by simplifying the process of creating high-quality enterprise embeddings, making it accessible and scalable for various organizations and teams .

Pros

Stable embeddings: Cleora's stability ensures consistent results on similar datasets.
Significantly faster than DeepWalk and Pytorch-BigGraph by Facebook.
Available as both a PRO version for enterprises and an Open Source version on Github.
Enables the creation of sophisticated models for recommender systems, client segmentation, propensity prediction, churn prediction, and lifetime value modeling.
Eliminates the need for GPUs when embedding graphs with billions of edges.
Facilitates the creation of high-quality enterprise embeddings without requiring costly hardware.
Can process graph data in the National Capital Region in less than 5 minutes.
Can handle billion-scale graph data efficiently.
Ability to combine embeddings across datasets.
Speed: Noted for speed, efficiency, and inductive capabilities.
Private beta for Cleora PRO offers additional benefits like automatic scaling, improved performance, and superior embedding quality.
Private beta for Cleora PRO: Additional benefits like automatic scaling and improved performance are available in the private beta version.
Efficient deployment: Cleora offers simple, secure, and quick deployment with zero overhead.
Efficiency: Cleora is significantly faster than competing systems, capable of embedding graphs with billions of edges without the need for GPUs.
Dim-wise independence: Each dimension in Cleora embeddings is independent, allowing for efficient multi-view embedding combinations.

Cons

Data bias due to information richness in raw data
Difficulty in guaranteeing fundamental correctness, leading to data leakage
Spaghetti code that is hard to maintain and scale
Complex and difficult-to-maintain spaghetti code
Entrenchment in a 'data science' silo with arcane knowledge
Employees becoming complacent and defending outdated methods
Requires manual labor and lacks time for analysis
Dependent on expert knowledge for evolving data schemas
Human limitations may lead to loss of important behavioral cues
Complicated data pipelines creation and maintenance
Each model requires different features and/or input formats
Entrenchment in a 'data science' silo with little results to show
Spaghetti code that is hard to grow and maintain
Entrenchment in a 'data science' silo with arcane knowledge and little results to show
Employees becoming complacent and defending their 'arcane ways'

Pros

Cons

Stable embeddings: Cleora's stability ensures consistent results on similar datasets.
Significantly faster than DeepWalk and Pytorch-BigGraph by Facebook.
Available as both a PRO version for enterprises and an Open Source version on Github.
Enables the creation of sophisticated models for recommender systems, client segmentation, propensity prediction, churn prediction, and lifetime value modeling.
Eliminates the need for GPUs when embedding graphs with billions of edges.
Facilitates the creation of high-quality enterprise embeddings without requiring costly hardware.
Can process graph data in the National Capital Region in less than 5 minutes.
Can handle billion-scale graph data efficiently.
Ability to combine embeddings across datasets.
Speed: Noted for speed, efficiency, and inductive capabilities.
Private beta for Cleora PRO offers additional benefits like automatic scaling, improved performance, and superior embedding quality.
Private beta for Cleora PRO: Additional benefits like automatic scaling and improved performance are available in the private beta version.
Efficient deployment: Cleora offers simple, secure, and quick deployment with zero overhead.
Efficiency: Cleora is significantly faster than competing systems, capable of embedding graphs with billions of edges without the need for GPUs.
Dim-wise independence: Each dimension in Cleora embeddings is independent, allowing for efficient multi-view embedding combinations.

Data bias due to information richness in raw data
Difficulty in guaranteeing fundamental correctness, leading to data leakage
Spaghetti code that is hard to maintain and scale
Complex and difficult-to-maintain spaghetti code
Entrenchment in a 'data science' silo with arcane knowledge
Employees becoming complacent and defending outdated methods
Requires manual labor and lacks time for analysis
Dependent on expert knowledge for evolving data schemas
Human limitations may lead to loss of important behavioral cues
Complicated data pipelines creation and maintenance
Each model requires different features and/or input formats
Entrenchment in a 'data science' silo with little results to show
Spaghetti code that is hard to grow and maintain
Entrenchment in a 'data science' silo with arcane knowledge and little results to show
Employees becoming complacent and defending their 'arcane ways'

Cleora.ai FAQs

What is Cleora PRO and how does it benefit Data Science teams?: Cleora PRO helps teams create high-quality embeddings without expensive hardware, providing automatic scaling, ease of use, performance optimizations, and support for item attributes.

What can you build with Cleora Embeddings?: Cleora embeddings can be utilized to build systems for recommender systems, client segmentation, propensity prediction, lifetime value modeling, and churn prediction, among other applications.

What is the difference between Cleora PRO and Cleora Open Source?: Cleora PRO is available as a self-service option for selected customers, while the Cleora Open Source version can be accessed on Github and is used by many industry leaders.

How fast is Cleora compared to other graph embedding systems?: Cleora PRO is up to 200x faster than DeepWalk and 4x-8x faster than Pytorch-BigGraph by Facebook, offering exceptional speed in embedding generation.

Can Cleora handle billion-scale graph data?: Yes, Cleora can automatically scale and handle billion-scale graph data efficiently, as demonstrated by its performance processing graph data in the National Capital Region in less than 5 minutes.