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[MLOps] The Clear SHOW - S02E11 - DIY Strikes Back! Building the Model Store!

Ariel extends ClearML's "experiment first" approach towards a "model first" approach - by building a model store. See how easy it is to add metadata to the model artifacts. + Colab notebook (uses the demo server, just run it and see what happens) ClearML is the only open-source tool to manage all your MLOps in a unified and robust platform providing collaborative experiment management, powerful orchestration, easy-to-build data stores, and one-click model deployment.

Top 7 IoT Tools & Platforms for App Development

The connected reality of gadgets presented by the Internet of Things (IoT) can no longer be considered as just a buzz as it has already transformed our living to a great extent. The connected things made our homes, workplaces, and transportation smarter. No wonder IoT apps are now one of the most popular app categories. As the latest statistics reveal, the global market share of IoT apps is expected to touch a whopping 520 billion USD by the end of 2021.

[MLOPS] From #GTC21: Best Practices in Handling Machine Learning Pipelines on DGX Clusters

Learn how to set up and orchestrate end-to-end ML pipelines, leveraging large DGX clusters. We'll demonstrate how to orchestrate your training and inference workloads on DGX clusters, with optional setup of remote development environments leveraging the multi-instance GPUs on the NVIDIA A100. We'll also show how pipelines can be built to serve both research and deployment workloads, all while leveraging the compute inherent in the DGX cluster.

[MLOPS] From #GTC21: How to Supercharge Your Team's Productivity with MLOps

Learn how to structure a data scientist-first orchestration setup that allows your DS team to self-manage their allocated NVIDIA GPU clusters, without needing continuous hand-holding from DevOps/IT. We'll demonstrate this setup while using NVIDIA Clara Train SDK to walk through best practices in orchestration, experiment management, and data operations and pipelining. While examples will be health-care-focused, the concepts demonstrated are agnostic to any ML/DL use case in any industry.

[MLOPS] From #GTC21: Workshop - Demonstrating an End-to-End Pipeline for ML/DL Leveraging GPUs

Learn how to take models from research into deployment in an efficient and scalable manner. We'll demonstrate workflows and methodologies so that your data science team can make the most of their NVIDIA hardware systems and software tools (including TRITON!).

Load testing and Azure DevOps with José Luis Latorre Millas (k6 Office Hours #20)

In this episode of k6 Office Hours, Nicole and Simme are joined by José Luis Latorre Millas, who will talk about why k6 and Azure DevOps go well together and how to add load testing to a CI/CD pipeline. José is the Developer Community Lead at Swiss Life AG.

4 Considerations When Building Your Government Data Strategy

If you’ve followed Cloudera for a while, you know we’ve long been singing the praises—or harping on the importance, depending on perspective—of a solid, standalone enterprise data strategy. While certainly not a new concept, Government missions are wholly dependent on real time access/analysis of data (wherever it may be (legacy data centers or public cloud) to render insight to support operational decisions.

How to Move Kubernetes Logs to S3 with Logstash

Sometimes, the data you want to analyze lives in AWS S3 buckets by default. If that’s the case for the data you need to work with, good on you: You can easily ingest it into an analytics tool that integrates with S3. But what if you have a data source — such as logs generated by applications running in a Kubernetes cluster — that isn’t stored natively in S3? Can you manage and analyze that data in a cost-efficient, scalable way? The answer is yes, you can.

How to Visualize & Accelerate Digital Twins IoT Development

The Internet of Things (IoT) is changing the way we physically and digitally interact in the world. With the global IoT market projected to reach nearly $2 trillion by 2028, developers are looking for ways to take advantage of the increasing demand and gain a cutting edge. For many teams, IoT development combined with using digital twins for verification allows them to make better decisions during development and visually market their devices after launch. How?