A new generation of data processing systems, including web search, Google’s Knowledge Graph, IBM’s Watson, and several different recommendation systems, combine rich databases with software driven by machine learning. The spectacular successes of these trained systems have been among the most notable in all of computing and have generated excitement in health care, finance, energy, and general business. But building them can be challenging, even for computer scientists with PhD-level training. If these systems are to have a truly broad impact, building them must become easier. We explore one crucial pain point in the construction of trained systems: feature engineering. Given the sheer size of modern datasets, feature developers must (1) write code with few effective clues about how their code will interact with the data and (2) repeatedly endure long system waits even though their code typically changes little from run to run. We propose brainwash, a vision for a feature engineering data system that could dramatically ease the ExploreExtract-Evaluate interaction loop that characterizes many trained system projects.
This paper describes a new approach, based on linear programming, for computing nonnegative matrix factorizations (NMFs). The key idea is a data-driven model for the factorization where the most salient features in the data are used to express the remaining features. More precisely, given a data matrix X, the algorithm identifies a matrix C that satisfies X ≈ CX and some linear constraints. The constraints are chosen to ensure that the matrix C selects features; these features can then be used to find a low-rank NMF of X. A theoretical analysis demonstrates that this approach has guarantees similar to those of the recent NMF algorithm of Arora et al. (2012). In contrast with this earlier work, the proposed method extends to more general noise models and leads to efficient, scalable algorithms. Experiments with synthetic and real datasets provide evidence that the new approach is also superior in practice. An optimized C++ implementation can factor a multigigabyte matrix in a matter of minutes.
We describe an asynchronous parallel stochastic coordinate descent algorithm for minimizing smooth unconstrained or separably constrained functions. The method achieves a linear convergence rate on functions that satisfy an essential strong convexity property and a sublinear rate (1/K) on general convex functions. Near-linear speedup on a multicore system can be expected if the number of processors is O(n1/2) in unconstrained optimization and O(n1/4) in the separable-constrained case, where n is the number of variables. We describe results from implementation on 40-core processors.
We describe an asynchronous parallel stochastic coordinate descent algorithm for minimizing smooth unconstrained or separably constrained functions. The method achieves a linear convergence rate on functions that satisfy an essential strong convexity property and a sublinear rate ($1/K$) on general convex functions. Near-linear speedup on a multicore system can be expected if the number of processors is $O(n^{1/2})$ in unconstrained optimization and $O(n^{1/4})$ in the separable-constrained case, where $n$ is the number of variables. We describe results from implementation on 40-core processors.
Impala von Cloudera ist ein modernes, massiv paralleles Datenbanksystem, welches von Grund auf für die Bedürfnisse und Anforderungen einer Big Data Umgebung wie Hadoop entworfen wurde. Das Ziel von Impala ist es, klassische SQL-Abfragen mit geringer Latenz und Laufzeit auszuführen, so wie man es von typischen BI/DW Lösungen gewohnt ist. Gleichzeitig sollen dabei sehr große Quelldaten in Hadoop gelesen werden, ohne dass ein weiterer Extraktionsprozess in zusätzliche Systemlandschaften notwendig ist. Dieses Kapitel soll einen Überblick über Impala aus der Benutzerperspektive geben und detaillierter auf die Hauptkomponenten und deren Entwurfsentscheidungen eingehen. Zusätzlich werden wir einen Geschwindigkeitsvergleich mit anderen bekannten SQL-auf-Hadoop Lösungen vorstellen, der den besonderen Ansatz von Impala unterstreicht.
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. To meet this need, we are building MedPerf, an open framework for benchmarking machine learning in the medical domain. MedPerf will enable federated evaluation in which models are securely distributed to different facilities for evaluation, thereby empowering healthcare organizations to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status, and our roadmap. We call for researchers and organizations to join us in creating the MedPerf open benchmarking platform.
Many problems in machine learning can be solved by rounding the solution of an appropriate linear program (LP). This paper shows that we can recover solutions of comparable quality by rounding an approximate LP solution instead of the ex- act one. These approximate LP solutions can be computed efficiently by applying a parallel stochastic-coordinate-descent method to a quadratic-penalty formulation of the LP. We derive worst-case runtime and solution quality guarantees of this scheme using novel perturbation and convergence analysis. Our experiments demonstrate that on such combinatorial problems as vertex cover, independent set and multiway-cut, our approximate rounding scheme is up to an order of magnitude faster than Cplex (a commercial LP solver) while producing solutions of similar quality.
Cloudera Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Apache Hive. This paper presents Impala from a user’s perspective, gives an overview of its architecture and main components and briefly demonstrates its superior performance compared against other popular SQL-on-Hadoop systems.
