It’s helping us to advance breakthroughs in new drugs and vaccines, and it will continue to do so for future advances.
But there are unique challenges to developing ML models for healthcare and life sciences: How can you automate your pipeline, version your data, and scale up as you need without breaking the bank?
This solution brief presents what data engineering teams need to automate complex data pipelines, and some of the top use cases being implemented with healthcare and life sciences data.
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Data is unstructured: Most healthcare data isn’t stored in a database or files but in physical charts, EMRs, X-Rays, MRIs, audio files, and even DNA sequences.
Data is disparate and heterogenous: Data is in different formats (text, images, video and audio) and spread across different systems from providers to payors.
Data sets are large: Most use cases have petabytes of data and millions of records that need to be continually processed to derive accurate results.
Reproducibility is key: Organizations need to reproduce any outcome by identifying what data was used and what models were to used to produce what results.
Data changes more frequently than the ML model: The ML model is relatively static in comparison to the volumes of data being changed and updated.
Not enough experimentation: The time required for running and re-running projects can be prohibitive, which means less experimentation and smaller datasets.
Pachyderm is cost-effective at scale and enables data engineering teams to automate complex pipelines with sophisticated data transformations
Deliver reliable results faster maximizes dev efficiency.
Automated diff-based data-driven pipelines.
Deduplication of data saves infrastructure costs.
Immutable data lineage ensures compliance.
Data versioning of all data types and metadata.
Familiar git-like structure of commits, branches, & repos.
Leverage existing infrastructure investment.
Language agnostic - use any language to process data
Data agnostic - unstructured, structured, batch, & streaming
Automatic deduplicating file system that overlays your object store or database. Track every change to your data and code automatically as you work. "Diff-based" storage eliminates unnecessary data copies.
Autoscale up and down based on demand using a Kubernetes backend. Automatic data sharding allows pipelines to processes large data sets in parallel. Full process visibility and monitoring using Kubernetes native tools
Automatic triggering of pipeline execution based on versioned data and/or code changes. Intelligent processing of only modified data and dependencies. This “diff-based” automation allows for a data-centric approach and faster time to results.
