model-deployment 
CIO
NOVEMBER 16, 2023
A large language model (LLM) is a type of gen AI that focuses on text and code instead of images or audio, although some have begun to integrate different modalities. But there’s a problem with it — you can never be sure if the information you upload won’t be used to train the next generation of the model. It’s not trivial,” she says.
TechCrunch
NOVEMBER 22, 2022
Users can also leverage Taktile to experiment with off-the-shelf data integrations and monitor the performance of predictive models in their decision flows, Wehmeyer said, performing A/B tests to evaluate those flows. ” Wehmeyer also sees Noble, a platform that provides a rules-based engine to edit and launch credit models, as a rival.
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TechCrunch
FEBRUARY 13, 2023
Writer is such a one, and it just announced a new trio of large language models to power its enterprise copy assistant. The company lets customers fine-tune these models on their own content and style guides, from which point forward the AI can write, help write or edit copy so that it meets internal standards.
Dzone - DevOps
MARCH 20, 2024
In the age of Big Data and Artificial Intelligence (AI), effectively managing and deploying machine learning (ML) models is essential for businesses aiming to leverage data-driven insights. However, the journey from model development to deployment poses unique challenges.
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Dzone - DevOps
JANUARY 5, 2024
However, deploying and managing ML models in production environments can be a daunting task. This is where containerization comes into play, offering an efficient solution for packaging and deploying ML models.
Dzone - DevOps
NOVEMBER 6, 2023
In the rapidly evolving domain of machine learning (ML), the ability to seamlessly package and deploy models is as crucial as the development of the models themselves. Containerization has emerged as the game-changing solution to this, offering a streamlined path from the local development environment to production.
Advertiser: Data Robot
Enterprise interest in the technology is high, and the market is expected to gain momentum as organizations move from prototypes to actual project deployments. Ultimately, the market will demand an extensive ecosystem, and tools will need to streamline data and model utilization and management across multiple environments.
Advertiser: Data Robot
To prevent deployment delays and deliver resilient, accountable, and trusted AI systems, many organizations invest in MLOps to monitor and manage models while ensuring appropriate governance. Download today to find out more!
Advertiser: Data Robot
It may require changing your operation models and finding the right guidance to realize the full breadth of capabilities. Democratization puts AI into the hands of non-data scientists and makes artificial intelligence accessible to every area of an organization. Democratizing AI through your organization requires more than just software.
Advertiser: Data Robot
Our eBook covers the importance of secure MLOps in the four critical areas of model deployment, monitoring, lifecycle management, and governance. In our eBook, Building Trustworthy AI with MLOps, we look at how machine learning operations (MLOps) helps companies deliver machine learning applications in production at scale.
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.
Advertiser: Data Robot
As machine learning models are put into production and used to make critical business decisions, the primary challenge becomes operation and management of multiple models.
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. Top 3 challenges you’ll encounter when creating your predictive model.
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