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6 key considerations for selecting an AI systems vendor

CIO

Choosing the right AI systems vendor – one with the right capabilities – won’t solve all these issues. To achieve AI success, IT leaders need a systems vendor who goes far beyond simply plugging the latest GPU processor into a standard rack-mount server. But it can simplify achieving your AI goals.

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Pull Systems launches out of Up.Labs-Porsche partnership to tackle EV performance

TechCrunch

On Porsche’s list: software that helps manage and automate the performance of EVs. Pull Systems, the first startup borne out of the partnership, has developed a software product that the two companies say can solve it. And it has already rolled out to Porsche Taycan vehicles that are on the road today.

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Why Internet Performance Monitoring is the new frontier in a distributed world

CIO

Moreover, with users spread across the globe, understanding regional performance variations has become crucial to ensuring experiences for your customers and employees. Given this backdrop, it’s unsurprising that traditional Application Performance Monitoring (APM) and Network Performance Monitoring (NPM) tools are struggling to keep pace.

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Explainer: Building a high-performing last-mile delivery software

CIO

The staff monitoring the logistics management systems are at their wit’s end, trying to compensate for the lost time. Since the last mile process is highly agile, CIOs must ensure the software systems have built-in deep learning capabilities to make in-the-moment decisions. This integration brings forth a suite of tangible benefits.

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Build Trustworthy AI With MLOps

We also look closely at other areas related to trust, including: AI performance, including accuracy, speed, and stability. How MLOps helps bridge the production gap between systems and teams. AI operations, including compliance, security, and governance. AI ethics, including privacy, bias and fairness, and explainability.

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What are decision support systems? Sifting data for better business decisions

CIO

Decision support systems definition A decision support system (DSS) is an interactive information system that analyzes large volumes of data for informing business decisions. Decision support system examples Decision support systems are used in a broad array of industries. Example uses include: GPS route planning.

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OpenAI open-sources Whisper, a multilingual speech recognition system

TechCrunch

In a step toward solving it, OpenAI today open-sourced Whisper, an automatic speech recognition system that the company claims enables “robust” transcription in multiple languages as well as translation from those languages into English. “[The models] show strong ASR results in ~10 languages.

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Why Distributed Tracing is Essential for Performance and Reliability

Speaker: Daniel "spoons" Spoonhower, CTO and Co-Founder at Lightstep

However, this increased velocity often comes at the cost of overall application performance or reliability. Worse, teams often don’t understand what’s affecting performance or reliability – or even who to ask to learn more. Understand a distributed system and improve communication among teams.

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Resilient Machine Learning with MLOps

But we can take the right actions to prevent failure and ensure that AI systems perform to predictably high standards, meet business needs, unlock additional resources for financial sustainability, and reflect the real patterns observed in the outside world.

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Best Practices for Micro-Services Management, Traceability and Visualization

Speaker: Robert Starmer, Cloud Advisor, Founding Partner at Kumulus Technologies

Service mesh models were initially targeted at supporting efficient management of application deployment and upgrade routing, but are also well suited to capturing the interactive traces of distributed applications, providing a secondary insight into the environment with very little change to the application, and potentially no performance impact.

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The Complete Predictive Analytics Lifecycle for Application Teams

Speaker: Sriram Parthasarathy, Senior Director of Predictive Analytics, Logi Analytics

Find out how a real-world application decided what predictive questions to ask, sourced the right data, organized resources, built models, deployed predictive analytics in production, and monitored model performance over time. How to source data from multiple systems and overcome common data challenges.

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A PM’s Guide to Forging an Outcome-Driven Product Team

Speaker: Kim Antelo, Transformation Coach

In this webinar, Transformational Coach Kim Antelo will walk through a case study of a healthcare company with lofty OKRs, but with little tie-back to the product performance. How to use a systems approach to solve your most pressing problems. She will also discuss: The overlap between HEART and Pirate AAARRR metrics.

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Monetizing Analytics Features: Why Data Visualizations Will Never Be Enough

Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.