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Cognitive Workloads on the Cloud: What Clouds Need To Do

Cognitive Workloads on the Cloud: What Clouds Need To Do

January 18, 2020

With the emergence of new powerful hardware accelerators, such as Graphical Processing Units (GPUs) and Programmable Field Gate Arrays (FPGAs), more and more artificial intelligence (AI) has appeared …
Quantum Computing is Here, Powered by Open Source

Quantum Computing is Here, Powered by Open Source

January 18, 2020

Quantum computing is no longer a thought experiment. Companies, labs, and even nation states all have achieved success at working prototypes that will be scaled within 3 years. As we build toward a se …
Mesos vs Kubernetes: What We Learned Working With Both From Customers

Mesos vs Kubernetes: What We Learned Working With Both From Customers

January 15, 2020

IBM has been working in Apache Mesos community for almost two year and also builds a product named as Conductor for Container (https://hub.docker.com/r/ibmcom/cfc-installer/) which is based on Kuberne …
Providing Services to Cloud Native Platforms with the Open Service Broker API

Providing Services to Cloud Native Platforms with the Open Service Broker API

January 11, 2020

In a digital world, widely adopted and easy to use APIs are the cornerstone of collaboration and interoperability. As Cloud technologies mature and become commonplace, the absence of standards surface …
Isolated Container Runtime for Docker Images

Isolated Container Runtime for Docker Images

January 7, 2020

Isolated Container Runtime (Harshal Patil, IBM) - Docker by default uses a container runtime called runc to run containers using cgroups and namespaces. But Docker by design can run one or many runt …
The future of data science and machine learning (sponsored by IBM)

The future of data science and machine learning (sponsored by IBM)

December 28, 2019

A changing market landscape and open source innovations are having a dramatic impact on the consumability and ease of use of data science tools. Carlo Appugliese examines the impact these trends and changes will have on the future of data science and how machine learning is making data science available to all.
Engineering the future of AI for businesses (sponsored by IBM Watson)

Engineering the future of AI for businesses (sponsored by IBM Watson)

December 26, 2019

Ruchir Puri explores the opportunities and challenges of AI for business, focusing on what is needed to truly scale out AI applications and systems across the breadth of enterprises.
Panel Discussion: If APIs are like Snowflakes, How Do You Make them More "Standardized" - or Do You? - Moderated by Jeff Borek, IBM

Panel Discussion: If APIs are like Snowflakes, How Do You Make them More "Standardized" - or Do You? - Moderated by Jeff Borek, IBM

December 26, 2019

APIs form the connecting glue between modern applications and are used to connect third-party data services, public/private data sources, and other applications. The Open API Initiative (OAI) came tog …
Open Source Cloud Ecosystem for Next-Gen Disaggregated Datacenters

Open Source Cloud Ecosystem for Next-Gen Disaggregated Datacenters

December 24, 2019

Modern computer systems have been for long designed as monolithic blocks built around the boundaries of the motherboard. This talk looks at novel architectures for low-power and high-utilization disag …
IBM LinuxONE: The Largest Scalable Linux Server

IBM LinuxONE: The Largest Scalable Linux Server

December 23, 2019

The Modernization possibilities on the Most Scalable Compute Platform for Secure Data Driven Workloads Open source has become a hub for innovation. New use cases such as containers, new classes of dat …
Mapping data in Jupyter notebooks with PixieDust (sponsored by IBM)

Mapping data in Jupyter notebooks with PixieDust (sponsored by IBM)

December 21, 2019

Raj Singh offers an overview of PixieDust, a Jupyter Notebook extension that provides an easy way to make interactive maps from DataFrames for visual exploratory data analysis. Raj explains how he built mapping into PixieDust, putting data from Apache Spark-based analytics on maps using Mapbox GL.
The future of AI is now (sponsored by IBM)

The future of AI is now (sponsored by IBM)

December 17, 2019

Damion Heredia explores how augmented intelligence is helping companies disrupt industries and enabling them to make better decisions.
Container Runtime and Image Format Standards - What it Means to be OCI-Certified [I] - Jeff Borek, IBM &

Container Runtime and Image Format Standards - What it Means to be OCI-Certified [I] - Jeff Borek, IBM &

December 14, 2019

With the proliferation and rapid growth of container-based solutions over the past few years including container-based solutions from almost all major IT vendors, cloud providers, and emerging start- …
Hadoop and object stores: Can we do it better?

Hadoop and object stores: Can we do it better?

December 3, 2019

Trent Gray-Donald and Gil Vernik explain the challenges of current Hadoop and Apache Spark integration with object stores and discuss Stocator, an open source object store connector that overcomes these shortcomings by leveraging object store semantics. Compared to native Hadoop connectors, Stocator provides close to a 100% speedup for DFSIO on Hadoop and a 500% speedup for Terasort on Spark.
Multinode restricted Boltzmann machines for big data

Multinode restricted Boltzmann machines for big data

December 1, 2019

Nikolay Manchev offers an overview of the restricted Boltzmann machine, a type of neural network with a wide range of applications, and shares his experience using it on Hadoop (MapReduce and Spark) to process unstructured and semistructured data at a scale.
Building serverless applications on the Apache OpenWhisk platform

Building serverless applications on the Apache OpenWhisk platform

November 27, 2019

Apache OpenWhisk on IBM Bluemix provides a powerful and flexible environment for deploying cloud-native applications driven by data, message, and API call events. Daniel Krook explains why serverless architectures are attractive for many emerging cloud workloads and when you should consider OpenWhisk for your next project.
Developer on the rise: Blurring the line between developer and data scientist with PixieDust

Developer on the rise: Blurring the line between developer and data scientist with PixieDust

November 26, 2019

Ready to dip your toe into data science? Va Barbosa explains why you should start with notebooks and PixieDust, a new open source library that helps data scientists and developers working in the Jupyter Notebook and Apache Spark be more efficient.
Why choose open infrastructure? (sponsored by IBM)

Why choose open infrastructure? (sponsored by IBM)

November 19, 2019

Open source isnt winning; its won. In the last decade, there's been an incredible explosion in open source software. Massive projects have been developed in the open, on open operating systems, using open languages and compilers. But, Christopher Aedo asks, was all the infrastructure open as well?
Serverless architectures built on an open source platform

Serverless architectures built on an open source platform

November 18, 2019

Daniel Krook explores Apache OpenWhisk on IBM Bluemix, which provides a powerful and flexible environment for deploying cloud-native applications driven by data, message, and API call events.
How is design driving outcomes?

How is design driving outcomes?

November 12, 2019

Doug Powell explains how IBM's design transformation is resulting in real impact for the worlds most established and enduring tech company.
Measuring adoption of design thinking in organizations

Measuring adoption of design thinking in organizations

November 12, 2019

Creating a culture around design thinking in any company requires education, evaluation, and iteration. IBM has educated and activated 10,000+ of its employees in design thinking. . .but how do you know if it's having an impact? Using IBM as a case study, Jordan Shade explains how to measure the adoption of this new way of working, including common blockers and essential enablers.
Machine learning is about your data and deployment, not just model development (sponsored by IBM)

Machine learning is about your data and deployment, not just model development (sponsored by IBM)

November 4, 2019

Which is more important: the model or the data? Dinesh Nirmal explains how your data can help you build the right cognitive systems to learn about, reason with, and engage with your customers.
Sparklyr: An R interface for Apache Spark

Sparklyr: An R interface for Apache Spark

November 2, 2019

Sparklyr makes it easy and practical to analyze big data with Ryou can filter and aggregate Spark DataFrames to bring data into R for analysis and visualization and use R to orchestrate distributed machine learning in Spark using Spark ML and H2O SparkingWater. Edgar Ruiz walks you through these features and demonstrates how to use sparklyr to create R functions that access the full Spark API.