AI-READY DATA PIPELINES
Accelerate AI development with data readiness workflows
Ensure that your web data is suitable for training and deploying AI models effectively.
AI-ready data = Effective AI models
Data readiness is a critical aspect of AI development, ensuring that data is suitable for training and deploying AI models effectively. Poor data quality can lead to biased models, inaccurate predictions, and inefficient AI systems.
AI-readiness Pipelines, running on Datastreamer can transform raw, unstructured data—including social media, news, blogs, PDFs, and other files into high-quality, structured datasets—empowering the feeding and training of AI models at scale.
Data Readiness: An assessment framework
“Garbage in, Garbage out” is a phrase best applied to AI model development. Lacking the proper AI-ready data is a critical failure point if not properly solved.
Is the required data available?
With unlimited integration possibilities, Pipelines on Datastreamer are able to integrate and unify any web data.
Is the data clean and complete?
Implement data filtering, routing, detection enrichments within your Pipelines to be able to validate the completeness of the data and deduplicate and filter out invalid data.
Does the data align with AI goals?
AI goals change. With a fully flexible data pipeline you can adapt the data sources to reflect real-world changes with streaming internet data
Is there sufficient data available?
With the transformation capabilities present in the Datastreamer platform, you can unify and join many different providers to create expansive data sets.
Is the data stored securely?
With integration to databases, data warehouses, and even Datastreamer’s own Searchable Storage; ingress and egress of data to secure locations is simplified.
Is the data properly accessible?
Utilize ingress capabilities and connectors in Datastreamer to no-code adapt your pipelines to handle and ingest from anywhere!
Meet Sharvari - Data scientist extraordinaire
Sharvari is a senior data scientist at Datastreamer, and created this page. The team at Datastreamer is built from industry veterans, and Sharvari is an expert in using pipelines running Datastreamer to power our in-house enrichment and transformation feature development.
Why should your AI pipelines run on Datastreamer?
Your AI projects should be leverage the cutting-edge data streaming and enrichment platform capabilities that are powered by Datastreamer. Our motto is to accelerate how you work with web data. Accelerate your AI innovation by using an automated pipeline for getting centralized, high-quality, and intelligently processed data.
Automated data ingestion
Seamlessly collect data from social media, news, PDFs, databases, APIs, and cloud storage.
No-code Integration
Build AI-ready data pipelines without complex engineering. Remove the operational bottlenecks and distractions.
Get With The "Real" Times
Leverage real-time data processing. Extract, structure, and enrich data from multiple sources instantly.
Unstructured Data Structuring
Automatically transform unstructured data, and incomplete semi-structured data into AI-ready structured data.
Scalable and Secure
Designed for enterprise-scale AI workflows with built-in compliance and security.
Automated Infrastructure
Leverage the powerful underlying Datastreamer platform to automate the scale, health, and versioning of your Pipelines.
FREEDOM from pipeline chores
Build smarter pipelines
With simple no-code pipelines, AI-powered enrichment capabilities, fully managed and automated infrastrucutre, it just makes sense to build Pipelines that run on Datastreamer.