machine learning

The OS for AI: How serverless computing enables the next gen of machine learning

February 2, 2020

ML has been advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Jonathan Peck explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML.

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.

Digital discrimination: Cognitive bias in machine learning

January 30, 2020

With increasing regularity we see stories in the news about machine learning algorithms causing real-world harm to people's lives and livelihoods. Maureen McElaney examines how bias can take root in machine learning algorithms and ways to overcome it.

The Observatorium: Combining Machine Learning and Observability to Improve Incident Response

January 29, 2020

At DigitalOcean, a global hosting company predicated on providing building blocks for developers, the proliferation of microservices necessary to support a worldwide cloud creates a unique-yet-univers …

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.

Machine learning vital signs: Metrics and monitoring of AI in production

January 27, 2020

Production artificial intelligence systems are interacting with the real world, and it's terrifying that oftentimes nobody has any idea how they're performing on live data. Donald Miner details why you should track your models in production over time, explains how you can implement proper logging and metrics for models, and details metrics you should probably be capturing.

Managing machines

January 27, 2020

Machine learning (ML) drove massive growth at consumer internet companies over the last decade, enabled by open software, datasets, and AI research. For many problems, ML will produce better, faster, and more repeatable decisions at scale. Unfortunately, building and maintaining these systems is difficult and expensive. Pete Skomoroch explores what you need to produce better ML results.

Open source streaming analytics with the Kafka, Flink, Cassandra (KFC) stack

January 27, 2020

Streaming analytics is a popular subject in enterprise organizations because customers want real-time experiences, such as notifications and advice based on online behavior and other users actions. Bas Geerdink details an open source reference solution for streaming analytics that covers many use cases that follow a "pipes and filters" pattern, built with Scala, Flink, Kafka, and Cassandra.

Optimizing analytical queries on Cassandra by 100x

January 26, 2020

Cassandra is one of the most popular datastores in big data and ML applications. Data analysis at scale with fast query response is critical for business needs, and while Cassandra with Spark integration allows running an analytical workload, it can be slow. Shradha Ambekar dives into the challenges faced at Intuit and the solutions her team implemented to improve performance by 100x.

Overview of Data Governance

January 26, 2020

Paco Nathan offers an overview of its history, themes, tools, process, standards, and morepartly based on interviewing experts in this field about issues and best practices. Join in to learn what impact machine learning has on data governance and vice versa, along with an overview of open source projects and open standards in this space.

Removing unfair bias in machine learning using open source (sponsored by IBM)

January 25, 2020

ML models are increasingly used to make decisions that impact lives. Ana Echeverri and Trisha Mahoney walk you through how to use the open source Python package AI Fairness 360, developed by IBM researchers, a comprehensive open source toolkit empowering users with metrics to check for unwanted bias in datasets and machine learning models and state-of-the-art algorithms to mitigate such bias.

The OS for AI: How serverless computing enables the next gen of machine learning

January 24, 2020

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 …

Unlocking your serverless functions with OpenFaaS for AI chatbot projects

January 23, 2020

Sergio Mendez examines critical challenges when implementing AI chatbots and explains how Movistar designed an open source serverless architecture using OpenFaaS on top of Kubernetes and other complementary technologies like NoSQL, brokers to deploy Telegram AI chatbots. Sergio then compares these technologies to "vendor lock-in" services offered by major cloud providers.

What's your machine learning score?

January 23, 2020

ML in production is different than ML in an R&D environment. Tania Allard dives deep into a number of techniques to test your ML quality and decay in your R&D and production environments appropriately. You'll see examples of issues commonly encountered in the ML area and how to test and monitor your data, model development, and infrastructure.

Architecting a data platform to support analytic workflows for scientific data

January 14, 2020

In upstream oil and gas, a vast amount of the data requested for analytics projects is scientific data: physical measurements about the real world. Historically, this data has been managed library style, but a new system was needed to best provide this data. Sun Maria Lehmann and Jane McConnell share architectural best practices learned from their work with subsurface data.

Continuous intelligence: Keeping your AI application in production

January 14, 2020

Machine learning can be challenging to deploy and maintain. Any delays in moving models from research to production mean leaving your data scientists' best work on the table. Arif Wider and Emily Gorcenski explore continuous delivery (CD) for AI/ML along with case studies for applying CD principles to data science workflows.

Continuous intelligence: Moving machine learning into production reliably

January 13, 2020

Danilo Sato and Christoph Windheuser walk you through applying continuous delivery (CD), pioneered by ThoughtWorks, to data science and machine learning. Join in to learn how to make changes to your models while safely integrating and deploying them into production, using testing and automation techniques to release reliably at any time and with a high frequency.

Data science transformation: Transforming a traditional wealth manager to a cutting-edge data-driven company

January 13, 2020

Charlotte Werger outlines the components necessary to transform a traditional wealth manager into a data-driven business, paying special attention to devising and executing a transformation strategy by identifying key business subunits where automation and improved predictive modeling can result in significant gains and synergies.

Data-driven digital transformation and jobs: The new software hierarchy and ML

January 13, 2020

Robert Cohen discusses the skills that employers are seeking from employees in digital jobs, linked to the new software hierarchy driving digital transformation. Robert describes this software hierarchy as one that ranges from DevOps, CI/CD, and microservices to Kubernetes and Istio. This hierarchy is used to define the jobs that are central to data-driven digital transformation.

Dealing with data scarcity in natural language processing

January 13, 2020

In this age of big data, NLP professionals are all too often faced with a lack of data: written language is abundant, but labeled text is much harder to come by. Yves Peirsman outlines the most effective ways of addressing this challenge, from the semiautomatic construction of labeled training data to transfer learning approaches that reduce the need for labeled training examples.

Deep learning for recommender systems

January 12, 2020

The success of deep learning has reached the realm of structured data in the past few years, where neural networks have been shown to improve the effectiveness and predictability of recommendation engines. Oliver Gindele offers a brief overview of such deep recommender systems and explains how they can be implemented in TensorFlow.

Deep learning for speech synthesis: The good news, the bad news, and the fake news

January 12, 2020

Modern deep learning systems allow us to build speech synthesis systems with the naturalness of a human speaker. While there are myriad benevolent applications, this also ushers in a new era of fake news. Scott Stevenson explores the danger of such systems and details how deep learning can also be used to build countermeasures to protect against political disinformation.

Deep learning with TensorFlow and Spark using GPUs and Docker containers

January 12, 2020

Organizations need to keep ahead of their competition by using the latest AI, ML, and DL technologies such as Spark, TensorFlow, and H2O. The challenge is in how to deploy these tools and keep them running in a consistent manner while maximizing the use of scarce hardware resources, such as GPUs. Thomas Phelan discusses the effective deployment of such applications in a container environment.

Executive Briefing: Overview of data governance

January 11, 2020

Effective data governance is foundational for AI adoption in enterprise, but it's an almost overwhelming topic. Paco Nathan offers an overview of its history, themes, tools, process, standards, and more. Join in to learn what impact machine learning has on data governance and vice versa.

Executive Briefing: The hidden data scientists lurking in your company

January 11, 2020

Many companies delay addressing core improvements in increasing revenues, reducing costs and risk exposure by tying changes to a to-be-hired data scientist. Drawing on three customer examples, Jack Norris explains how to achieve excellent results faster by starting with domain experience and helping developers and analysts better leverage data with available and understandable analytics.

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.

Executive Briefing: Why managing machines is harder than you think

January 11, 2020

In the next decade, companies that understand how to apply machine intelligence will scale and win their markets. Others will fail to ship successful AI products that matter to customers. Pete Skomoroch details how to combine product design, machine learning, and executive strategy to create a business where every product interaction benefits from your investment in machine intelligence.