Systems | Development | Analytics | API | Testing

July 2021

Four Questions To Accelerate Edge-to-Cloud AI Strategy Development

“More than 15 billion IoT devices will connect to the enterprise infrastructure by 2029.” Finding data is not going to be a challenge, clearly, but taking advantage of it all to drive business outcomes will be. Combining AI and machine learning (ML) with data collection and processing capabilities of the edge and the cloud may hold the answer.

[MLOps] The Clear SHOW - S02E13 - mlops_this: Copilot Shenanigans

Ariel should have known better than to mess with shitposts on mlops.community ;) Here is a ClearML pipeline integrated with the notorious mlops_this generated by GitHub's Copilot. 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.

Using AI/ML to Increase Gaming Monetization

Gamers are not shy about reaching into their wallets for premium content and features. They also won’t hesitate to tap the uninstall button at the first sign of trouble. It’s not uncommon for a gamer to boot up a hotly anticipated new game or revisit an old favorite only to put it down days or weeks later. The culprit is often gaming monetization issues that get in the way of what would otherwise be a long-term rewarding gaming experience.

[MLOps] The Clear SHOW - S02E12 - Goodbye Fig .1 [Sculley15]

Sometimes, even in a field as young and bustling, one has to say goodbye to an old friend. Today we bid adieu to Fig. 1 of D. Sculley et al., AKA "Hidden technical debt in Machine learning systems." Listen to Ariel Biller explaining what's going on and what are we going to use in lieu of Fig. 1

[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.

[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!).

[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.

Four Industries That Will Be Disrupted by AI in 2021

With the never-ending potential of technology to disrupt everyday processes, more and more industries are deciding to adapt to one exciting area of innovation today: artificial intelligence (AI). In fact, Global Industry Analysts Inc. predicts that AI will be worth 164.03 billion GBP by 2026, and here, we look at four industries set to be disrupted by AI. Since the healthcare sector collects and greatly depends on personal data from their patients, AI will play a crucial role in data management.

Business Monitoring for Gaming: Catch More Profit Opportunities with AI

Anomalies don’t have to be a fear factor; they could even present an opportunity to make money. Imagine detecting positive spikes in in-app purchases, conversions, or gaming activity in real-time and then having your business monitoring system identify what caused them 10x faster than you can now – autonomously. With 95% accuracy in the root cause analysis you could replicate and capitalize on the deviation immediately.