Build, deploy, and manage ML models at scale
Speed up your data science journey
with Gathr
Streamline the end-to-end ML lifecycle
Integrate data quickly and easily
Enrich both batch and streaming data
Build or import models effortlessly
Train on curated, enriched datasets
Operationalize models 10x faster
Manage versions and retrain with ease
Track model performance with drift detection
Enable advanced analytics use cases
Legacy Machine Learning platform failing you?
Upgrade your data engineering with next-gen unified data pipelining platform
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Drag-and-drop machine learning
Upgrade to a platform with enhanced Machine Learning Capabilities
Ready to deploy ML models- Highly scalable ML Ops
- Reduce risk of error
Ready to deploy ML models
Operationalize models at scale
Embrace MLOps across the enterprise
Enroll multiple teams into the MLOps paradigm and leverage automated and
reproducible workflows across use cases
Faster cycles for model deployment and monitoring
Central registry to store and track data, models, and metadata
Pre-built tools for fast-and-easy model engineering and training
Robust model & experiment versioning for flexibility of usage
Expert Opinion
Gathr is an end-to-end, unified data platform that handles ingestion, integration/ETL (extract, transform, load), streaming analytics, and machine learning. It offers strengths in usability, data connectors, tools, and extensibilty.
Customer Speak
Gathr helped us build “in-the-moment” actionable insights from massive volumes of complex operational data to effectively solve multiple use cases and improve the customer experience.
Meet Gathr.
The only all-in-one data pipeline platform
- One platform to do it all - ETL, ELT, ingestion, CDC, ML
- Self Service, zero-code, drag and drop interface
- Built-in DataOps, MLOps, and DevOps tools
- Cloud-agnostic and interoperable
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Integration -
Streaming
Analytics -
Data
Preparation -
Machine
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Why Gathr
TOP TEN
reasons to upgrade to Gathr
Up your ETL game today.
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Customer Stories
Powering breakthrough success
Leading auto insurance provider
Driver profiling and risk assessment
Built an end-to-end analytics application to analyze telematics data in real-time and offer customers dynamic, usage-based insurance plans.
Multi-national automaker
Pre-emptive fault detection in auto parts
Implemented a pre-emptive fault detection solution to help predict malfunction of auto parts, enable on-time maintainence, and ensure fault-free production.
Fortune 500 Bank
Real-time insider threat detection
Used predictive analytics and machine learning to automatically detect threat scenarios and raise alerts for preventing predicted breaches across sensitive applications.
Major US airline
Superior omni-channel customer experience
Delivered proactive insights to help contact center representatives present customers with relevant and personalized offers across multiple channels.
Fortune 500 mortgage lender
Real-time business activity monitoring
Enabled 10x faster data processing and efficient, near real-time KPI tracking with an end-to-end solution for data ingestion, transformation, enrichment, and analysis.
Cable TV and telecom provider
360-degree view of the customer
Enabled micro-segmentation and targeting, dynamic marketing campaigns, proactive error resolution, and contextualized customer service in real-time.
Communication analytics company
Processed 1.5 billion events per day
Modernized legacy ETL frameworks to process over 1.5 billion user interactions per day from multiple real-time feeds and reduce the overall release cycle time.
LEADING U.S. CALL CENTER
Real-time call monitoring solution
Improved performance metrics such as call abandonment rate, average speed of answer, and average call length by monitoring call activities in real-time.
TOP U.S. TELECOM PROVIDER
Real-time multi-lingual sentiment analysis
Enabled rapid and accurate real-time text categorization and multi-lingual sentiment analysis for massive volumes of data from diverse sources.
Leading Wireless & Telecom Service Provider
Call center agent monitoring solution
Reduced annual call center costs by $5M and improved agent productivity by tracking desktop activities of call center representatives in real-time.
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