Name: Myths of Parallel File Systems #2 - Parallel file systems are hard to manage
Start: 2020-05-06T18:05:00Z
End: 2020-05-06T18:05:19.000Z
Location: BrightTALK
Rating: 0

Weka offers WekaFS, the modern file system that uniquely empowers organizations to solve the newest, biggest problems holding back innovation. Optimized for NVMe and the hybrid cloud, Weka handles the most demanding storage challenges in the most data-intensive technical computing environments, delivering truly epic performance at any scale. Its modern architecture unlocks the full capabilities of today’s data center, allowing businesses to maximize the value of their high-powered IT investments. Weka helps industry leaders reach breakthrough innovations and solve previously unsolvable problems.

Don’t tell anyone, but there’s a rumor going around that artificial intelligence practitioners are playing favorites. Seriously, their training models get all the love. Companies throw resources at developing robust training models, but the basic infrastructures surrounding them are often ignored to the extent that companies underestimate the level that performance plays across the board. Here’s what they do:

 

Underestimate the need for scale when building their AI infrastructures
Overfocus on model training at the expense of data cleansing and prep work
Forget to remain agile as they leverage AI 
 

We are bringing together Scott Sinclair, who is an IT veteran, and Ken Grohe, the CRO of WekaIO, both of whom spend many hours talking with infrastructure managers and data scientists about the future of their data pipelines and putting their AI models into production.

The Future of Data Pipelines: The Cracks in Your AI Data Pipeline Infrastructure

Artificial intelligence (AI) is becoming widely used in the Federal space for facial recognition, real-time translation, image detection, and voice recognition. As organizations go into production with AI, the amount of data sets being analyzed has exploded. Traditional storage appliances can’t keep up, so AI compute is now resembling high-performance computing (HPC).

Chesapeake Systems (CHESA), in partnership with Hewlett Packard Enterprise (HPE), WekaIO, Scality, and NVIDIA, provides customers with software-defined storage solutions tailored to HPC and AI workloads that achieve multi-petabyte scalability and high-performance, while simultaneously controlling costs.

Join these five industry leaders for a panel discussion on how your agency can dramatically lower your total cost of ownership of your HPC environments while increasing performance and scalability in your AI workflows.

Supercharge Your HPC and AI Workflow in Federal

As organizations start amplifying their usage of AI/ML and enriching it with large volumes of data, new technologies and methodologies must be implemented to utilize its full value. To put it simply, infrastructure is key to the success of AI/ML projects. Within that infrastructure, networking, compute, and storage are key factors that will impact how new environments will perform, as well as assure a project’s ability to grow and scale in the future. 

In this fireside chat, WekaIO’s Field CTO, Shimon Ben David, will talk with Darrin Johnson, Director of Solutions Architecture and Technical Marketing, Enterprise at NVIDIA and Scot Schultz, Sr. Director, Mellanox HPC and Technical Computing at NVIDIA. Both Darrin and Scot have been involved in dozens of AI & ML projects in various stages of implementation and will share their experience and insights.

What you can expect to learn:
- How to design an environment for high-performance AI/ML workloads
- Why most AI pipelines can’t run on a standard 10G network
- The one thing companies neglect to consider when starting with AI, which usually ends up costing time and money as they scale AI projects in production

Optimizing Infrastructure for AI

SAS is one of the most common tools used to enable High-Performance Data Analytics (HPDA). This includes the data wrangling and predictive modeling workloads used in areas such as fraud and anomaly detection, affinity marketing, and business intelligence.


This webinar discusses the issues many clients encounter when processing large amounts of data to produce analytics and how they can be solved. Latency is one of the biggest challenges. Typical symptoms include long-running processes, stability issues with the environment, running out of resources, and unnecessary extra costs due to improperly designed infrastructure. Destiny Corporation will discuss the issues and the requirements for SAS and other analytic workloads. When latency is removed, I/O intensive workloads can complete in minutes or seconds instead of hours and days, without changing SAS code.

Accelerate and Scale Cloud Ready SAS Analytics Solutions

High-frequency and algorithmic trading has undergone a revolution through the application of predictive financial models to trading strategies.  Success is measured by fast execution based on scientific model results, with quantitative trading companies constantly seeking new strategies to gain better market insight and improve trading outcomes.  This new data-driven approach, and the evolution of new models, requires a modern IT architecture that can handle the increased demand from data-intensive and latency-sensitive applications.

During this webinar, we’ll be discussing WekaIO’s record-breaking performance on STAC M3 benchmarks using kdb+ time-series database, how we partner to overcome these challenges and drive a 4x price/performance gain compared to traditional delivery models.  We’ll be using real-world examples to demonstrate this, as well as highlighting some additional benefits:

-  Improved wall clock time
-  Simplified infrastructure management
-  The ability to leverage the public cloud for compute bursting, back-up and DR

How to Get 4X Storage Price/Performance For Algorithmic Trading

In this webinar, you will learn about container eco-system and Kubernetes orchestration, architecture requirements of high-performance stateful applications at scale, the WekaFS Kubernetes solution, and edge to core to cloud accelerated DataOps.

Deploying Containerized Stateful Applications with WekaFS

Learn how industry leaders – HPE, Scality, and Weka – deliver the fastest time to insight and higher accuracy results for better AI outcomes. The session will address use cases from Financial Services, GPU-Accelerated Workloads, and Life Sciences.

Boost Speed and Accuracy of Your AI Data Pipelines with HPE, Weka, and Scality

Learn how to get faster results for a quicker time to market for the AI, Genomics, and Financial Services industries. Discover the world’s fastest high-performance storage solution, featuring the WekaFS™ file system running on Supermicro server platforms.

Faster Results and a Quicker Time to Market with Weka and Supermicro

As the saying goes, data is only valuable if you can analyze it. For large enterprises, the amount of data that needs to be analyzed can be significant. Even the performance of AI and ML solutions are dependent on the storage architecture utilized. Learn how to best design and deploy large-scale storage systems to support the use of AI and ML for business intelligence.

Utilizing HPC-Scale Storage and AI for Business Intelligence

Scaling a parallel file system is hard and complicated. Myth or fact? Field CTO, Shimon Ben-David shows how the Weka File System (WekaFS) debunks this myth.

Myths of Parallel File Systems #1 - Scaling a parallel file system

Join this on-demand webinar to learn about how Weka AI™ has been a strong foundation to work from to solve big problems across industries in AI, machine learning, and analytics. The Weka AI framework, inclusive of WekaFS™, NVIDIA, and Mellanox  provides an accelerated modern storage infrastructure and delivers time to value and time to insights.

Turbocharge AI Data Pipelines with Weka AI, NVIDIA, and Mellanox

Are parallel file systems actually hard to manage? Field CTO, Shimon Ben-David goes over the management complexity of legacy parallel file systems compared to the Weka File System (WekaFS).

Myths of Parallel File Systems #2 - Parallel file systems are hard to manage

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Myths of Parallel File Systems #2 - Parallel file systems are hard to manage

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