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Rakuten - Recommendation Platform

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Building Intelligent Applications, Experimental ML with Uber’s Data Science W...

In this talk, we will explore how Uber enables rapid experimentation of machine learning models and optimization algorithms through the Uber’s Data Science Workbench (DSW). DSW covers a series of stages in data scientists’ workflow including data exploration, feature engineering, machine learning model training, testing and production deployment. DSW provides interactive notebooks for multiple languages with on-demand resource allocation and share their works through community features. It also has support for notebooks and intelligent applications backed by spark job servers. Deep learning applications based on TensorFlow and Torch can be brought into DSW smoothly where resources management is taken care of by the system. The environment in DSW is customizable where users can bring their own libraries and frameworks. Moreover, DSW provides support for Shiny and Python dashboards as well as many other in-house visualization and mapping tools. In the second part of this talk, we will explore the use cases where custom machine learning models developed in DSW are productionized within the platform. Uber applies Machine learning extensively to solve some hard problems. Some use cases include calculating the right prices for rides in over 600 cities and applying NLP technologies to customer feedbacks to offer safe rides and reduce support costs. We will look at various options evaluated for productionizing custom models (server based and serverless). We will also look at how DSW integrates into the larger Uber’s ML ecosystem, e.g. model/feature stores and other ML tools, to realize the vision of a complete ML platform for Uber.

Big Data- Automotive Industry Use Case

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Big Data has emerged as a powerful new technology paradigm. To manage the massive data generated by social media, online transactions, Weblogs, or sensors, Big Data incorporates innovative technologies in data management (unstructured, semi-structured and structured), processing, real-time analytics, and visualization. It is also useful for reporting in circumstances where a relational database approach is not effective or too costly. This Big Data project is to be primarily exposed to utilize the tools. Tool usage, programming, algorithms and application development are covered in relevant courses.

Societe Generale is one of the major banks in France and has many data science teams across the globe. After years of explorations and prototyping, it is time for the company to really deploy machine learning projects at scale to the production environment. To achieve that goal, we have been working hard to define a standard process of collaboration between data engineers and data scientists. And we also designed and deployed an infrastructure for productionizing machine learning. During this presentation, you will be looking at the following points of our adventure: 1. Difficulties that we had for putting ML applications into production, such as lack of model registry; hard to deploy ML libraries to our Hadoop cluster; collaboration between data scientists and data engineers etc. ? 2. How did we deploy MLflow as a key technical component to our production hadoop environment given different security constraints. 3. How did we build a CI/CD pipeline to deploy ML applications automatically. MLflow plays an important role in this piepline. 4. A first and concrete production project developed on top of this infrastructure with MLflow, Spark streaming, Sklearn and CI/CD. The key takeaways of this presentation would be: 1. To productionize machine learning in a big structure like Société Générale, a process of collaboration should be clearly defined. 2. A ML model registry is key to ML productionization. MLflow is the best solution we found. 3. A CI/CD pipeline is essential to the success of a machine learning application.

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Ai platform at scale

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"You don't need a bigger boat": serverless MLOps for reasonable companies

It is indeed a wonderful time to build machine learning systems, as the growing ecosystems of tools and shared best practices make even small teams incredibly productive at scale. In this talk, we present our philosophy for modern, no-nonsense data pipelines, highlighting the advantages of a (almost) pure serverless and open-source approach, and showing how the entire toolchain works - from raw data to model serving - on a real-world dataset. Finally, we argue that the crucial component for analyzing data pipelines is not the model per se, but the surrounding DAG, and present our proposal for producing automated "DAG cards" from Metaflow classes. Bio: Jacopo Tagliabue was co-founder and CTO of Tooso, an A.I. company in San Francisco acquired by Coveo in 2019. Jacopo is currently the Lead A.I. Scientist at Coveo. When not busy building A.I. products, he is exploring research topics at the intersection of language, reasoning and learning, with several publications at major conferences (e.g. WWW, SIGIR, RecSys, NAACL). In previous lives, he managed to get a Ph.D., do scienc-y things for a pro basketball team, and simulate a pre-Columbian civilization. Topics: MLOps, Metaflow, model cards.

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Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

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Cloud Native Night July 2019, Munich: Talk by Steffen Srohsschmiedt (@grohsschmiedt, Head of Cloud at LogicalClocks) === Please download slides if blurred! === Abstract: Machine Learning (ML) pipelines are the fundamental building block for productionizing ML code. Building such pipelines with Big Data is a complex process. The different stages in ML pipelines also need to be orchestrated, from data ingestion and data transformation, to feature engineering, to model training, serving and monitoring. Hopsworks is an open-source data platform that can be used to both develop and operate horizontally scalable machine learning (ML) pipelines. A key part of our pipelines is the world's first open-source Feature Store, that acts as a data warehouse for features, providing a natural API between data engineers - who write feature engineering code - and Data Scientists, who select features from the feature store to generate training/test data for models. Join us next time: https://www.meetup.com/Cloud-Native-muc/events

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Modern machine learning systems may be very complex and may fall into many pitfalls. It's very easy to unintendedly introduce technical debt into such a complex structure. One of the approaches solving some of anti-patterns is a feature store. Feature store is a missing piece filling a gap between raw data and machine learning models. Not only it will help you to handle technical debt, but even more importantly speeds up time to develop new model.

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In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. This talks explores recent advances in this area in both research and practice. I will explain how deep learning can be applied to recommendation settings, architectures for handling contextual data, side information, and time-based models, and compare deep learning approaches to other cutting-edge contextual recommendation models, and finally explore scalability issues and model serving challenges.

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This talk is about building Audi's big data platform from a first Hadoop PoC to a multi-tenant enterprise platform. Why a big data platform at all? We explain the requirements that drove the development of this platform and explain the decisions we had to make during this journey. During the process of setting up our big data infrastructure, we often had to find the right balance between going for enterprise integration versus speed. For instance, whether to use the existing Active Directory for both LDAP and KDC versus setting up our own KDC. Using a shared enterprise service like Active Directory requires to follow certain naming conventions and restricted access, where running our own KDC brings much more flexibility but also adds another component to maintain to our platform. We show the advantages and disadvantages and explain why we've decided to choose a certain approach. For data ingestion of both batch and streaming data, we use Apache Kafka. We explain why we installed a separate Kafka cluster from our Hadoop platform. We discuss the pros and cons of using the Kafka binary protocol and the HTTP REST protocol not only from a technical perspective but also from the organisational perspective as the source systems are required to push data into Kafka. We give an overview of our current architecture including how some use cases are implemented on it. Some of them run exclusively on our new big data stack, while others use it in conjunction with our data warehouse. The use cases cover all different kinds of data from sensory data of robots in our plants to click streams from web applications. Building an enterprise platform does not only consist of technical tasks but also of organizational tasks: data ownership, authorization to access certain data sets, or more financial ones like internal pricing and SLAs. Although we have already achieved quite a lot, our journey has not yet ended. There are still some open topics to address, like providing a unified logging solution for applications spanning multiple platforms. Or finally offering a notebook-like Zeppelin to our analysts. Or addressing legal issues like GDPR. We will conclude our talk with a short glimpse into our ongoing extension of our on-premises platform into a hybrid cloud platform. Speakers Carsten Herbe, Big Data Architect, Audi AG Matthias Graunitz, Big Data Architect, Audi AG

CWIN17 Toulouse / Opc ua, the de facto interoperability standard for industry...

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QAware GmbH

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"You don't need a bigger boat": serverless MLOps for reasonable companies

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Hopsworks - The Platform for Data-Intensive AI

A view of graph data usage by Cerved

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Berlin buzzwords 2020-feature-store-dowling

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