12,841 research outputs found
Machine Learning at Microsoft with ML .NET
Machine Learning is transitioning from an art and science into a technology
available to every developer. In the near future, every application on every
platform will incorporate trained models to encode data-based decisions that
would be impossible for developers to author. This presents a significant
engineering challenge, since currently data science and modeling are largely
decoupled from standard software development processes. This separation makes
incorporating machine learning capabilities inside applications unnecessarily
costly and difficult, and furthermore discourage developers from embracing ML
in first place. In this paper we present ML .NET, a framework developed at
Microsoft over the last decade in response to the challenge of making it easy
to ship machine learning models in large software applications. We present its
architecture, and illuminate the application demands that shaped it.
Specifically, we introduce DataView, the core data abstraction of ML .NET which
allows it to capture full predictive pipelines efficiently and consistently
across training and inference lifecycles. We close the paper with a
surprisingly favorable performance study of ML .NET compared to more recent
entrants, and a discussion of some lessons learned
A Cost-based Optimizer for Gradient Descent Optimization
As the use of machine learning (ML) permeates into diverse application
domains, there is an urgent need to support a declarative framework for ML.
Ideally, a user will specify an ML task in a high-level and easy-to-use
language and the framework will invoke the appropriate algorithms and system
configurations to execute it. An important observation towards designing such a
framework is that many ML tasks can be expressed as mathematical optimization
problems, which take a specific form. Furthermore, these optimization problems
can be efficiently solved using variations of the gradient descent (GD)
algorithm. Thus, to decouple a user specification of an ML task from its
execution, a key component is a GD optimizer. We propose a cost-based GD
optimizer that selects the best GD plan for a given ML task. To build our
optimizer, we introduce a set of abstract operators for expressing GD
algorithms and propose a novel approach to estimate the number of iterations a
GD algorithm requires to converge. Extensive experiments on real and synthetic
datasets show that our optimizer not only chooses the best GD plan but also
allows for optimizations that achieve orders of magnitude performance speed-up.Comment: Accepted at SIGMOD 201
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