Speed up your data science journey
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
Drag-and-drop machine learning
Use Gathr’s self-service, visual canvas to easily build, train, and deploy complex models. Select from the platform’s 300+ built-in operators to create models faster than ever before.
Import ML models from platforms like TensorFlow and Scikit-learn. Get complete support for multiple algorithms for classification, clustering, and regression analysis.
Connect to a variety of batch and streaming data sources, perform quality checks, and identify outliers. Get instant access to massive, enriched data sets for faster model building.
Train models on curated datasets and build streaming pipelines for model scoring in real-time. Optimize model hyper-parameters to maximize performance.
Operationalize models at scale
Easily distribute your models on a Spark cluster for linear scalability. You can also use Kubernetes to scale models that you want to expose as a REST endpoint.
Apply A/B testing for monitoring model performance in the production environment. Swap the best-performing models based on real-time performance or accuracy using ‘Champion Challenger and Hot Swap’ techniques.
Once built, migrate your stand-alone data science models to an application exposed as a REST service for use across teams and use cases.
Use models trained in Gathr’s in-built notebooks to score pipelines. Sync and manage all datasets, notebooks, models, workflows, tags, and versions of your work with GIT.
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
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.
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.
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
reasons to upgrade to Gathr
Up your ETL game today.
Take it for a spin or get a 1:1 personalized demo
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.
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.
Learning and Insights
Stay ahead of the curve
Q&A with Forrester
Building a modern data stack: What playbooks don’t tell you
4 common data integration pitfalls to avoid
Why modernizing ETL is imperative for massive scale, real-time data processing
Don’t just migrate. Modernize your legacy ETL.