ml

Building Telemetry and Anomaly Detection Models for Cloud Native Storage

Building Telemetry and Anomaly Detection Models for Cloud Native Storage

March 4, 2020

Integrating with heterogeneous storage in the Cloud Native environment has always been a challenge. How to detect problems and fix them in a timely fashion is important for mission critical workloads. …
Machine Learning Made Easy on Kubernetes. DevOps for Data Scientists

Machine Learning Made Easy on Kubernetes. DevOps for Data Scientists

February 25, 2020

Though machine learning and AI are immensely powerful, these solutions are by no means easy. In many cases, there are many diverse components that are not designed to work together. Additionally, thes …
"Human error": How can we help people build models that do what they expect

"Human error": How can we help people build models that do what they expect

February 24, 2020

It's never been easier to train machine learning models. With excellent open source tooling, lower compute techniques, and incredible educational material online, really anybody can start to train their own models today. Yet, Anna Roth explains, when domain experts try to transfer their expertise to an ML model, the results can be unpredictable.
Accelerating ML at Twitter

Accelerating ML at Twitter

February 24, 2020

Twitter employs ML throughout its product to deliver value for its customers. Theodore Summe gives you a glimpse into ML at Twitter and explains how Cortex works to accelerate ML to better serve customer needs by partnering with TensorFlow.
Accelerating training, inference, and ML applications on NVIDIA GPUs

Accelerating training, inference, and ML applications on NVIDIA GPUs

February 24, 2020

Maggie Zhang, Nathan Luehr, Josh Romero, Pooya Davoodi, and Davide Onofrio give you a sneak peek at software components from NVIDIAs software stack so you can get the best out of your end-to-end AI applications on modern NVIDIA GPUs. They also examine features and tips and tricks to optimize your workloads right from data loading, processing, training, inference, and deployment.
Dont beat the market; beat the bots: Adversarial networks in finance

Dont beat the market; beat the bots: Adversarial networks in finance

February 24, 2020

Automated investing has brought an immense amount of stability to the market, but it has also brought predictability. Garrett Lander and Al Kari examine if an adversarial network can game the behavior of automated investors by learning the patterns in market activity to which they are most vulnerable.
Open Source Tools for ML Experiments Management

Open Source Tools for ML Experiments Management

February 23, 2020

The rise of new AI and ML requires new workflows and new tools: data versioning, ML pipeline versioning, experiments metrics visualization and others that have not been formalized and even named yet.T …
Production ML pipelines with TensorFlow Extended (TFX)

Production ML pipelines with TensorFlow Extended (TFX)

February 23, 2020

Aurlien Gron dives into creating production ML pipelines with TensorFlow Extended (TFX) and using TFX to move from ML coding to ML engineering. You'll walk through the basics and put your first pipeline together, then learn how to customize TFX components and perform deep analysis of model performance.
Anomaly detection using deep learning to measure the quality of large datasets

Anomaly detection using deep learning to measure the quality of large datasets

February 22, 2020

Any business, big or small, depends on analytics, whether the goal is revenue generation, churn reduction, or sales or marketing purposes. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. Sridhar Alla examines some techniques used to evaluate the quality of data and the means to detect the anomalies in the data.
Zero to ML hero with TensorFlow 2.0

Zero to ML hero with TensorFlow 2.0

February 22, 2020

Get a programmer's perspective on machine learning with Laurence Moroney, from the basics all the way up to building complex computer vision scenarios using convolutional neural networks and natural language processing with recurrent neural networks.
Anomaly detection in smart buildings using federated learning

Anomaly detection in smart buildings using federated learning

February 21, 2020

There's an exponential growth in the number of internet-enabled devices on modern smart buildings. IoT sensors measure temperature, lighting, IP camera, and more. Tuhin Sharma and Bargava Subramanian explain how they built anomaly-detection models using federated learningwhich is privacy preserving and doesn't require data to be moved to the cloudfor data quality and cybersecurity.
Containerized architectures for deep learning

Containerized architectures for deep learning

February 20, 2020

Container and cloud native technologies around Kubernetes have become the de facto standard in modern ML and AI application development. Antje Barth examines common architecture blueprints and popular technologies used to integrate AI into existing infrastructures and explains how you can build a production-ready containerized platform for deep learning.
Creating smaller, faster, production-worthy mobile machine learning models

Creating smaller, faster, production-worthy mobile machine learning models

February 20, 2020

Getting machine learning models ready for use on device is a major challenge. Drag-and-drop training tools can get you started, but the models they produce arent small enough or fast enough to ship. Jameson Toole walks you through optimization, pruning, and compression techniques to keep app sizes small and inference speeds high.
Deploying machine learning models on the edge

Deploying machine learning models on the edge

February 20, 2020

When IoT meets AI, a new round of innovations begins. Yan Zhang and Mathew Salvaris examine the methodology, practice, and tools around deploying machine learning models on the edge. They offer a step-by-step guide to creating an ML model using Python, packaging it in a Docker container, and deploying it as a local service on an edge device as well as deployment on GPU-enabled edge devices.
Executive Briefing: Unpacking AutoML

Executive Briefing: Unpacking AutoML

February 19, 2020

Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoMLtranslating from the hype into business concerns and practices in a vendor-neutral way.
How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE)

How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE)

February 19, 2020

Join Thomas Phelan to learn whether the combination of containers with large-scale distributed data analytics and machine learning applications is like combining oil and water or like peanut butter and chocolate.
Large-scale machine learning at Facebook: Implications of platform design on developer productivity

Large-scale machine learning at Facebook: Implications of platform design on developer productivity

February 19, 2020

AI plays a key role in achieving Facebook's mission of connecting people and building communities. Nearly every visible product is powered by machine learning algorithms at its core, from delivering relevant content to making the platform safe. Kim Hazelwood and Mohamed Fawzy explain how applied ML has continued to change the landscape of the platforms and infrastructure at Facebook.
Lightning Talk: Build a Reproducible ML Workflow with Kubeflow Pipelines

Lightning Talk: Build a Reproducible ML Workflow with Kubeflow Pipelines

February 18, 2020

Solving a data science problem is an iterative exercise. It requires running experiment after experiment trying new approaches with different parameters and lots of data. To manage this complexity, …
Machine learning challenges at LinkedIn: Spark, TensorFlow, and beyond

Machine learning challenges at LinkedIn: Spark, TensorFlow, and beyond

February 18, 2020

From people you may know (PYMK) to economic graph research, machine learning is the oxygen that powers how LinkedIn serves its 630M+ members. Zhe Zhang provides you with an architectural overview of LinkedIns typical machine learning pipelines complemented with key types of ML use cases.
TFX: Production ML pipelines with TensorFlow

TFX: Production ML pipelines with TensorFlow

February 18, 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 and Pedram Pejman explore Google's 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.
Architecting a data analytics service both in the public cloud and in the on-premise private cloud: ETL, BI, and machine learning (sponsored by SK Holdings)

Architecting a data analytics service both in the public cloud and in the on-premise private cloud: ETL, BI, and machine learning (sponsored by SK Holdings)

February 16, 2020

Jungwook Seo walks you through a data analytics platform in the cloud by the name of AccuInsight+ with eight data analytic services in the CloudZ (one of the biggest cloud service providers in Korea), which SK Holdings announced in January 2019.
Automating ML model training and deployments via metadata-driven data, infrastructure, feature engineering, and model management

Automating ML model training and deployments via metadata-driven data, infrastructure, feature engineering, and model management

February 16, 2020

Mumin Ransom gives an overview of the data management and privacy challenges around automating ML model (re)deployments and stream-based inferencing at scale.
Executive Briefing: What it takes to use machine learning in fast data pipelines

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

February 14, 2020

Dean Wampler dives into how (and why) to integrate ML into production streaming data pipelines and to serve results quickly; how to bridge data science and production environments with different tools, techniques, and requirements; how to build reliable and scalable long-running services; and how to update ML models without downtime.
Fair, privacy-preserving, and secure ML

Fair, privacy-preserving, and secure ML

February 14, 2020

With ML becoming more mainstream, the side effects of machine learning and AI on our lives become more visible. You have to take extra measures to make machine learning models fair and unbiased. And awareness for preserving the privacy in ML models is rapidly growing. Mikio Braun explores techniques and concepts around fairness, privacy, and security when it comes to machine learning models.
How machine learning meets optimization

How machine learning meets optimization

February 12, 2020

Machine learning and constraint-based optimization are both used to solve critical business problems. They come from distinct research communities and have traditionally been treated separately. But Jari Koister examines how they're similar, how they're different, and how they can be used to solve complex problems with amazing results.
How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE (BlueData))

How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE (BlueData))

February 12, 2020

Anant Chintamaneni and Matt Maccaux explore whether the combination of containers with large-scale distributed data analytics and machine learning applications is like combining oil and water or like peanut butter and chocolate.
Online machine learning in streaming applications

Online machine learning in streaming applications

February 11, 2020

Stavros Kontopoulos and Debasish Ghosh explore online machine learning algorithm choices for streaming applications, especially those with resource-constrained use cases like IoT and personalization. They dive into Hoeffding Adaptive Trees, classic sketch data structures, and drift detection algorithms from implementation to production deployment, describing the pros and cons of each of them.
Scalable anomaly detection with Spark and SOS

Scalable anomaly detection with Spark and SOS

February 10, 2020

Jeroen Janssens dives into stochastic outlier section (SOS), an unsupervised algorithm for detecting anomalies in large, high-dimensional data. SOS has been implemented in Python, R, and, most recently, Spark. He illustrates the idea and intuition behind SOS, demonstrates the implementation of SOS on top of Spark, and applies SOS to a real-world use case.