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Senatus AI: Software Development Re-invented

A state-of-the-art toolkit of A.I.-augmented capabilities which aim to "shift left" in the Software Development Lifecycle.

Sandhya Sridharan, Managing Director, Head of Core Development Platform and Engineering | November 2021

Driving Enterprise Software Delivery at Scale at JP Morgan Chase

Sandhya Sridharan, Head of Core Development Platform and Engineering, shares her perspectives on how JPMorgan Chase is navigating the challenges of developing and operating its developer platform ecosystem at scale and what elite performing really means to our company.

At JPMorgan Chase, we operate at a tremendous scale. Our technology organization is more than 50,000 people strong, with 35,000 developers and our teams are performing north of 120,000 builds daily, with over 70,000 deployments. To make things more interesting, we also have tremendous complexity and variance in our software development ecosystem. With all that in mind, delivering value without sacrificing security, quality and regulatory requirements across our application suite is much easier said than done

Sudeepto Roy, Software Engineer, GTI

Protecting web applications via Envoy OAuth2 filter

At JPMorgan Chase, we operate at a tremendous scale. Our technology organization is more than 50,000 people strong, with 35,000 developers and our teams are performing north of 120,000 builds daily, with over 70,000 deployments. To make things more interesting, we also have tremendous complexity and variance in our software development ecosystem. With all that in mind, delivering value without sacrificing security, quality and regulatory requirements across our application suite is much easier said than done.

AI Research | September 2021

Synthetic Data for Real Insights

J.P. Morgan AI Research generates synthetic datasets to accelerate research and model development in financial services.

ANU J., PAUL C., ARUP N., CHIEF TECHNOLOGY OFFICE | APR 2021

Evolution of Data Mesh Architecture Can Drive Significant Value in Modern Enterprise

Most modern organizations recognize that their data benefits their entire enterprise. Data has value to the individual business process that produces it, but data’s additional potential can be realized when it’s combined with other data assets.

LAWRENCE H., AI RESEARCH INTERN | SEP 2020

Searching for Patterns in Daily Stock Data: First Steps Towards Data-Driven Technical Analysis

Chart patterns are a commonly-used tool in the analysis of financial data. Analysts use chart patterns as indicators to predict future price movements. The patterns and their interpretations, however, are subjective and may lead to inconsistent inference and biased interpretation.

AMY V., FENGLIN Y., MING C., JOHN B., PRASHANT D. | SEP 2020
ARTIFICIAL INTELLIGENCE & MACHINE LEARNING TECHNOLOGY, CHIEF TECHNOLOGY OFFICE

Snorkeling: Label Data With Less Labor

Check out how Snorkeling can complement active learning and help partially automate the process of data label creation.

GUANGYU W., MACHINE LEARNING CENTER OF EXCELLENCE | AUG 2020

How to Build a FAQ Bot With Pre-Trained BERT and Elasticsearch

In this tutorial, we will demonstrate a simple way to create a FAQ bot by matching user questions to pre-defined FAQs using Sentence-BERT and Dense Vector Search in ElasticSearch with concrete code example. The solution is fast, accurate and scalable in production level environment.

MING C., ERICAMARIE K, FENGLIN YIN, HUANG Z., JOHN B., PRASHANT D. | JULY 2020
ARTIFICIAL INTELLIGENCE & MACHINE LEARNING TECHNOLOGY, CHIEF TECHNOLOGY OFFICE

Use ML to Improve Customer Experience Without Data and Privacy Compromise

Read on to find out how you can use machine learning to redact personally identifiable information to generate customer insights while ensuring data and privacy protection.

APPLIED AI & ML TEAM | JUNE 2020

Enter EVA (Email Virtualization Automation)

How the firm’s in-house machine learning solution for emails helped teams process the email surge in Q1.

AUSTIN G., DIGITAL ADVANCED COMPUTING | MAY 2019

Quantum Computing From a Computer Science Perspective

When approaching quantum computing from a computer science perspective, it may seem intuitive to begin by comparing quantum computers directly with their classical counterpart. However, many who attempt to learn this way (myself included) end up more confused than informed, especially after encountering the complex mathematical and physics notation present in popular literature.

PRASHANT D., CHIEF TECHNOLOGY OFFICE | APRIL 2020

Learning More From Less Data With Active Learning

How JPMC is combining the power of machine learning and human intelligence to create high-performance models in less time and at less cost.