A new area is emerging at the intersection of machine learning (ML) and systems design. This birth is driven by the explosive growth of diverse applications of ML in production, the continued growth in data volume, and the complexity of large-scale learning systems. Addressing the challenges in this intersection demands a combination of the right abstractions -- for algorithms, data structures, and interfaces -- as well as scalable systems capable of addressing real world learning problems.

Designing systems for machine learning presents new challenges and opportunities over the design of traditional data processing systems. For example, what is the right abstraction for data consistency in the context of parallel, stochastic learning algorithms? What guarantees of fault tolerance are needed during distributed learning? The statistical nature of machine learning offers an opportunity for more efficient systems but requires revisiting many of the challenges addressed by the systems and database communities over the past few decades. Machine learning focused developments in distributed learning platforms, programming languages, data structures, general purpose GPU programming, and a wide variety of other domains have had and will continue to have a large impact in both academia and industry.

As the relationship between the machine learning and systems communities has grown stronger, new research in using machine learning tools to solve classic systems challenges has also grown. Specifically, as we develop larger and more complex systems and networks for storing, analyzing, serving, and interacting with data, machine learning offers promise for modeling system dynamics, detecting issues, and making intelligent, data-driven decisions within our systems. Machine learning techniques have begun to play critical roles in scheduling, system tuning, and network analysis. Through working with systems and databases researchers to solve systems challenges, machine learning researchers can both improve their own learning systems as well impact the systems community and infrastructure at large.

The goal of this workshop is to bring together experts working at the crossroads of ML, system design and software engineering to explore the challenges faced when building practical large-scale machine learning systems. In particular, we aim to elicit new connections among these diverse fields, identify tools, best practices and design principles. The workshop will cover ML and AI platforms and algorithm toolkits (Caffe, Torch, TensorFlow, MXNet and parameter server, Theano, etc), as well as dive into the reality of applying ML and AI in industry with challenges of data and organization scale (with invited speakers from companies like Google, Microsoft, Facebook, Amazon, Netflix, Uber and Twitter).

The workshop will have a mix of invited speakers and reviewed papers with talks, posters and panel discussions to facilitate the flow of new ideas as well as best practices which can benefit those looking to implement large ML systems in academia or industry.

Focal points for discussions and solicited submissions include but are not limited to:
- Systems for online and batch learning algorithms
- Systems for out-of-core machine learning
- Implementation studies of large-scale distributed learning algorithms --- challenges faced and lessons learned
- Database systems for Big Learning --- models and algorithms implemented, properties (fault tolerance, consistency, scalability, etc.), strengths and limitations
- Programming languages for machine learning
- Data driven systems --- learning for job scheduling, configuration tuning, straggler mitigation, network configuration, and security
- Systems for interactive machine learning
- Systems for serving machine learning models at scale