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Found 11 papers, 7 papers with code
Operationalizing Machine Learning: An Interview Study
Organizations rely on machine learning engineers (MLEs) to operationalize ML, i. e., deploy and maintain ML pipelines in production.
New Directions in Cloud Programming
Nearly twenty years after the launch of AWS, it remains difficult for most developers to harness the enormous potential of the cloud.
Program Synthesis
Distributed, Parallel, and Cluster Computing
Databases
Operating Systems
Programming Languages
Scaling Replicated State Machines with Compartmentalization [Technical Report]
In this paper, we introduce compartmentalization, the first comprehensive technique to eliminate state machine replication bottlenecks.
Distributed, Parallel, and Cluster Computing
Deep Unsupervised Cardinality Estimation
To produce a truly usable estimator, we develop a Monte Carlo integration scheme on top of autoregressive models that can efficiently handle range queries with dozens of dimensions or more.
Keeping CALM: When Distributed Consistency is Easy
A key concern in modern distributed systems is to avoid the cost of coordination while maintaining consistent semantics.
Distributed, Parallel, and Cluster Computing Databases Programming Languages Software Engineering
Looking Back at Postgres
I got helpful input on this writeup from some of the more senior students on the project, but any errors or omissions are mine.
Databases
Serverless Computing: One Step Forward, Two Steps Back
Serverless computing offers the potential to program the cloud in an autoscaling, pay-as-you go manner.
Distributed, Parallel, and Cluster Computing Databases
Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms
Differential privacy is fast becoming the gold standard in enabling statistical analysis of data while protecting the privacy of individuals.
Cryptography and Security
A Berkeley View of Systems Challenges for AI
With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production.
Highly Available Transactions: Virtues and Limitations (Extended Version)
To minimize network latency and remain online during server failures and network partitions, many modern distributed data storage systems eschew transactional functionality, which provides strong semantic guarantees for groups of multiple operations over multiple data items.
Databases
GraphLab: A New Framework for Parallel Machine Learning
Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging.
