[TALK]@Telematika

pipeline

Deep learning coming to the tire industry: Warehouse staffing with RNN-LSTMs and pricing optimizations with DNNs

February 6, 2020

Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Alex Liang details two solutions where deep learning is used: a warehouse staffing solution where LSTM RNNs are used for staffing level forecasting and a pricing recommendation solution where DNNs were used for data clustering and demand modeling.

From inception to insight: Accelerating AI productivity with GPUs (sponsored by Dell Technologies)

February 5, 2020

Data scientists and machine learning engineers need the flexibility to work in multiple environments without wasting precious time configuring hardware and software and modifying code. Ramesh Radhakrishnan and John Zedlewski walk you through deploying a simple set of technologies for executing end-to-end pipelines entirely on GPUs.

Machine Learning Models and Datasets Versioning Practices and Tools

February 5, 2020

The rise of AI and ML changes development workflow and requires new development tools: data versioning, ML pipeline versioning, experiments metrics tracking and others that have not been formalized an …

V4L2: A Status Update

February 5, 2020

Since the beginning of 2018 a lot of work has been put into improving the V4L2 subsystem. The main addition was the Request API, which is required for stateless hardware codecs, and will help improve …

Marios Adventures in Tekton Land

February 4, 2020

Tekton is a Kubernetes-native, lightweight, easy to manage CI/CD pipelines engine. Pipeline building blocks can be reused, version controlled and curated in a catalogue that embeds best practices. Tek …

Kubernetes 201: Production tooling

February 2, 2020

Going to production with Kubernetes means new considerations that come with many acronyms. Kubernetes is configurable to meet your needs while open source tooling such as Helm, Brigade, and Duffle enable better ongoing operability. Bridget Kromhout and Aaron Wislang walk you through role-based access control, custom resource definitions, and pod disruption budgets.

TFX: Production ML pipelines with TensorFlow

February 2, 2020

Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe explores Google's open source community TensorFlow Extended (TFX), an open source version of the tools and libraries that Google uses internally, made using its years of experience in developing production ML pipelines.

AI pipelines powered by Jupyter notebooks

February 1, 2020

The Jupyter Notebook has become the de facto platform for data scientists and AI engineers to build interactive applications and develop AI/ML models. Luciano Resende details how to schedule related notebooks that correspond to different phases of the model lifecycle into notebook-based AI pipelines and walks you through scenarios that demonstrate how to reuse notebooks via parameterization.

Building a deployment pipeline with Jenkins 2

January 31, 2020

Brent Laster offers a brief, practical introduction to Jenkins as well as a guide to leveraging its automation and integration with other open source technologies to create a simple, working build and deployment pipeline that implements principles of continuous integration and continuous delivery.

Building machine learning inference pipelines at scale

January 31, 2020

Real-life ML workloads require more than training and predicting: data often needs to be preprocessed and postprocessed. Developers and data scientists have to train and deploy a sequence of algorithms that collaborate in delivering predictions from raw data. Julien Simon outlines how to build machine learning inference pipelines using open source libraries and how to scale them on AWS.

Kubernetes for machine learning: Productivity over primitives

January 28, 2020

Sophie Watson and William Benton demonstrate high-level open source tools that build on Kubernetes to solve machine learning workflow pain points. They explain why Kubernetes is great for ML and present tools that effortlessly provision custom research environments, publish reproducible notebooks, operationalize models and pipelines as services, and detect data drift automatically.

Model as a Service for Real-time Decisioning

January 27, 2020

Imagine a stream processing platform that leverages ML models and requires real-time decisions. While most solutions provide tightly coupled ML models in the use case, these may not offer the most eff …

Model as a service for real-time decisioning

January 27, 2020

Hosting models and productionizing them is a pain point. ML models used for real-time processing require data scientists to have a defined workflow giving them the agility to do self-service seamless deployments to production. Niraj Tank and Sumit Daryani detail open source technologies for building a generic service-based approach for servicing ML decisioning and achieving operational excellence.

Observability for data pipelines: Monitoring, alerting, and tracing lineage

January 27, 2020

Data-intensive applications, with many layers of transformations and movement from different data sources, can often be challenging to maintain and iterate even after they are initially built and validated. Jiaqi Liu explores how to factor in monitoring, alerting, and tracing data lineage when building data applications that move and transform data across multiple dependencies.

Using Kubeflow Pipelines for Building Machine Learning Pipelines

January 24, 2020

Kubeflow is an open-source project dedicated to making deployments of machine learning workflows on Kubernetes simple, portable and scalable. This session will focus on Kubeflow Pipelines, a platform …

(Unifying analytics and AI on big data for faster insights at scale)

January 22, 2020

Ziya Ma walks you through Intels scalable data insights strategy and related big data analytics and AI technologies such as Analytics Zooan end-to-end analytics and AI pipeline for developing full solutions with Apache Spark on Intel Xeon and Intel Optane DC Persistent Memory at scale. She highlights customers use cases and collaboration with industry leaders throughout.

Containers can actually improve your security story

January 21, 2020

Maya Kaczorowski explores how containers offer a fundamentally different, possibly better, security model than you're used to. They enable you to patch your environment more easily, identify when you're affected by a new vulnerability, and enforce governance for what is deployed in your environment.

Integrating Hardware-accelerated Video Decoding with the Display Stack

January 21, 2020

In February 2018, Bootlin launched a crowdfunding campaign to fund thedevelopment of upstream Linux kernel support for the hardware-acceleratedvideo decoder (VPU) found on Allwinner platforms. This wo …

Open Source Graphics 101: Getting Started

January 19, 2020

GPUs are complex beasts, especially when you come from the CPU world and don't know about all those GPU-specific concepts, or what massively parallel and deeply pipelined computing implies.Throughout …

Architecting for testing and continuous delivery

January 17, 2020

Most organizations want faster, more-incremental delivery of their applications, but fragile tests and complex continuous delivery pipelines often make this difficult. What if the problem isnt the pipeline but the architecture of the system? Ken Mugrage details the architectural choices that will help you enable stable tests and faster pipelines.

Continuous delivery in an ephemeral world

January 17, 2020

With systems like Travis CI, Circle CI, and CodeBuild, we're never more than a few lines of YAML away from a complete continuous delivery pipeline. However, ephemeral build systems constantly recreate the world from scratch, increasing build time and lengthening the CD feedback loop. John Chapin addresses those challenges and shares a reference pipeline using AWS CodePipeline and CodeBuild.

Hands-on introduction to Kubernetes and OpenShift

January 16, 2020

Join Christian Hernandez to learn Kubernetes basics using curl, kubectl, oc, and other command-line tools. You'll discover how to model portable, scaleable, and highly available solutions using open source tools for distributed computing.

DevOps for Network Engineering: Be the Cool NetDevOps Kid

January 15, 2020

DevOps principles are not exclusive to software development. We can apply many of its concepts to other environments, like network configuration management. Based on the idea of treating configuration …

Build your own data lake with AWS Glue and Amazon Athena (sponsored by Amazon Web Services)

January 14, 2020

Damon Cortesi demonstrates how to use AWS Glue and Amazon Athena to implement an end-to-end pipeline.

Executive Briefing: What it takes to use machine learning in fast data pipelines

January 11, 2020

Your team is building machine learning capabilities. Dean Wampler demonstrates how to integrate these capabilities in streaming data pipelines so you can leverage the results quickly and update them as needed and covers challenges such as how to build long-running services that are very reliable and scalable and how to combine a spectrum of very different tools, from data science to operations.

From BI to big data; Or, There and back again

January 10, 2020

Francesco Mucio shares the basic tools he and his team had to learn (or relearn) moving from the coziness of their database to the big world of Spark, cloud, distributed systems, and continuous applications. It was an unexpected journey that ended exactly where it started: with an SQL query.

From legacy to cloud: An end-to-end data integration journey

January 10, 2020

Max Schultze details Zalondo's end-to-end data integration platform to serve analytical use cases and machine learning throughout the company, covering raw data collection, standardized data preparation (binary conversion, partitioning, etc.), user-driven analytics, and machine learning.

LSTM-based time series anomaly detection using Analytics Zoo for Spark and BigDL

January 8, 2020

Collecting and processing massive time series data (e.g., logs, sensor readings, etc.) and detecting the anomalies in real time is critical for many emerging smart systems, such as industrial, manufacturing, AIOps, and the IoT. Guoqiong Song explains how to detect anomalies in time series data using Analytics Zoo and BigDL at scale on a standard Spark cluster.