Machine Learning has made a large impact in Financial Services by addressing a wide range of applications from fraud detection to improved customer service and robo investing tools. Learn how Pachyderm’s Automated Data Versioning and Data-Driven Pipelines help data science teams in Financial Service organizations to automate and scale their machine learning lifecycle while guaranteeing reproducibility.
Automated end-to-end pipelines engineered to scale From development to deployment, Pachyderm combines automation, data versioning, and parallel processing to transform expensive and unpredictable projects into streamlined enterprise-grade AI/ML production workflows.
Always know exactly what data was used to create any model across time. Have confidence at every step because you’ve built on the top of a rock solid data science foundation backed by true data lineage.
Pachyderm is cost-effective at scale and enables data engineering teams to automate complex pipelines with sophisticated data transformations
Automatically trigger pipelines when data changes.
Platform enables autoscale and parallel processing with no code.
Deduplicating file system that overlays standard object stores.
Automatic data version and Immutable data lineage.
Increase team collaboration via git-like structure of commits.
Track any result all the way back to its raw input
Runs on your existing cloud on on-premises infrastructure.
Language agnostic - use any language to process data
Data agnostic - unstructured, structured, batch, and streaming
Financial institutions have a long history with AI. Statisticians used hand-coded heuristics and expert systems to detect money laundering schemes and execute high frequency trades. But those older systems are brittle and don’t adapt well to black swan events and fast changing circumstances. That’s why financial leaders everywhere are turning to AI/ML to stop fraud dead in its tracks, upgrade their trading platforms and get their customers help before they ever need to talk to a support representative. Machine learning is highly flexible and it can find fraud patterns that old heuristic systems miss, teasing out the hidden relationships among transactions. It can deliver better, more human-like customer support and it can create trading systems that can respond to sudden shifts in the market faster.
What if there was an open data science platform that tracked every change in your data, models, code and did everything with the same discipline that banks track their investment?
That’s where Pachyderm comes into the picture. Our powerful machine learning platform lets anyone transform ad-hoc model creation into automated repeatable processes regardless of the format. Pachyderm pipelines enable teams to collaborate more effectively and it’s robust data transformation engine delivers the data foundation you need to build your machine learning pipelines on.
Financial Institutions face a complex and myriad set of regulations and compliance frameworks. Often those compliance standards overlap and conflict. Machine learning offers unprecedented promise and possibility but it also brings new compliance challenges.
Older heuristic and hand coded rules are easier to debug. But with machine learning, your models learn from the data itself. If you don’t know where that data came from, who touched it and when, you could easily find yourself in regulatory hot water. At every step of the journey, from data ingestion to putting your model in production, you need to know the steps it took to get there. You need to be able to roll backwards and forwards in time to recreate any step or answer any question from a regulatory agency.
Pachyderm can reduce the time it takes for auditors) to understand that journey from data to model, by providing documentation of every step along the way. With simple command `pachctl inspect` you can trace the entire journey of how your data became a model and prove every step in between. Whether it’s for debugging purposes, sharing data science workflows across business units, or satisfying data compliance requirements, everyone needs to know, with confidence, that any model, workflow, or result can be traced back to its original source with fully reproducible steps.
Try out this end-to-end Market Sentiment analysis example using NLP on Pachyderm for FREE. Included are step-by-step instructions on building a fully automated end-to-end machine learning pipeline from raw data to a deployed model with complete data lineage. Along the way, you’ll learn how to incorporate data labeling, transfer learning, model monitoring, how to handle new data automatically, and more.
Model risk should be managed like any other type of risk, model risk increases with greater model complexity, higher uncertainty about inputs and assumptions, broader use, and larger potential impact.
Banks should identify the sources of risk and assess the impact across a number of different fairness, ethicases to reduce this threat as much as possible.
Banks should consider risk from individual models and in the aggregate. Aggregate model risk is affected by interaction and dependencies among models; reliance on common assumptions, data, or methodologies. Pachyderm was engineered to help resolve this problem by letting you see every transformation your data, code and models went through across the machine learning lifecycle.
Pachyderm delivers the strong data foundation you need to create and maintain the right governance, policies, and controls over your data. With Pachyderm you can build end-to-end pipelines where everything is tracked and versioned, which makes supporting your auditing and compliance teams and internal audit and compliance functions that much easier.
Watch a short demo which outlines the product in action