Striveworks provides a platform called Chariot for model development and management, enabling organizations to embed structured, scalable model development into existing workflows.

Striveworks

The world has looked to data analytics to bridge the gap between floods of data and the struggle to use that data effectively to make timely, impactful decisions. Today, most organizations are awash in analytics that “aren’t quite right”—models that were developed too generally, or too slowly, to be effective in dynamic, fast-paced environments. Striveworks is simplifying MLOps with a powerful, extensible, low barrier to entry platform that instantiates the data analytic process as high-level code. 

Striveworks is trusted by leading Fortune 500 firms and leaders in the public sector to be their primary solution for managing model development, monitoring, and governance, and for ensuring that those models solve the real challenges their organizations face. 

Striveworks’ Chariot platform enables users to turn their own production data into models and turn models into production systems. Uniquely, as you train, test, deploy, and use models, our lineage system enables you to track both the “upstream” provenance of model and data sources and also the “downstream” usage of the resultant model inferences. Combining this with a highly principled experience for data and model development, Chariot gives our customers in highly regulated industries an unmatched governance solution over the top of a performant MLOps platform. 

We're #hiring individuals looking for #AIjobs and #MLjobs. If you are looking to work with cool tech stacks and languages like #GO, #kubernetes, etc and are generally interested in the world of data science, please take a look at our job openings: www.striveworks.com/careers

Centana Growth Partners

Watch this video to better understand how Palantir Technologies and Striveworks are bringing AI to the frontlines of US defense.

Excited to be flying out for #Hook24 at the GSR, catching up with US Navy and commercial customers to discuss the impact machine learning can have on automated target identification, broad area search, and corrosion detection to name a few. Striveworks has created a flexible and proven solution for these real world problems that integrates with existing systems to offer real-time insights where they are most necessary. 

I'm looking forward connecting with folks over the next few days to help translate how AI/ML can further empower the warfighter and accelerate data driven decision making across the battlespace.

Striveworks cofounders James Rebesco and Anthony (Tony) Manganiello are making waves in the world of defense thought leadership in their newest article featured by War on the Rocks.

"The rapid commoditization of foundational models, paired with an automated technological solution that selects domain-specific models for inference in real time, creates a dynamic AI ecosystem where adaptations can occur millisecond to millisecond and the marginal cost is measured in cents, not millions."

Read the full article for more gems! https://bit.ly/3XanrdD

If AI is part of your organization, it needs to be effective, safe, and consistent. That can’t happen if your models fail after a few days or weeks.

“Training a model that performs well on Day 1 or 2 is table stakes,” says tech leader Kutta Srinivasan. “Ensuring that [a] model keeps performing as promised on Day 3 and beyond—without toil—is the real test of an MLOps platform and where Striveworks truly shines.”


The way to keep models performing is through rapid remediation—a process detailed in our new white paper, “Model Drift and The Day 3 Problem.” Read it now to learn why models fail in production and how to keep them performant in the days and weeks after deployment.

https://bit.ly/3AzsED9

#AI #ML #MLOps #ModelDrift #ModelRemediation

Join Striveworks at #Hook24, Tailhook’s 68th Symposium, August 22-24, at the Grand Sierra Resort in Reno. Striveworks’ BD Director and former Naval Aviator, Joe Bergeron, is on site to connect with customers, partners, and #warfighters about the role of machine learning operations in advancing intelligence for military aviation. 

Link up with Joe today via DM or schedule time to meet in person.

See you there!

#Tailhook #militaryaviation #navalation #beready

Remediation is critical for maintaining AI models in high-stakes scenarios. Striveworks worked with a Fortune 500 customer to remediate a data fusion model it used to predict lightning-induced wildfires—successfully producing 1.1 million inferences over six weeks and delivering just-in-time data to firefighters for use in their daily mission planning. 

Learn all about this project in Striveworks’ new white paper, “Model Drift and The Day 3 Problem.” The paper explains why models fail to produce value over the long haul—and what you can do to get your AI models producing reliable results.

https://bit.ly/3yEv8iS

#AI #ML #MLOps #ModelDrift #ModelRemediation

📨  In our newsletter this month, we announce the release of blueprints, making it even easier for our users to train machine learning models using any framework. And we highlight the recent Palantir video about how technology can better serve our warfighters. 

Check out these stories and more in the comments (and be sure to join our mailing list to get the latest Striveworks news delivered to your inbox).

👉#LinkInComment

#machinelearning #AI #MLOps

To find success with AI at scale, organizations need to ingrain remediation into their AI workflows.

“In the MLOps space, everybody has been saying, ‘You can build and deploy models.’ Of course that’s valuable, but even the language people are using is wrong,” says Eric Korman, Co-Founder and Chief Science Officer of Striveworks. “They need to think about the full life cycle of a machine learning model: build, deploy, and maintain.”

A proper machine learning workflow doesn’t stop once models are deployed. It incorporates automated monitoring to keep track of models’ performance metrics on an ongoing basis. Then, when models drift, it sets off a rapid remediation process—validating the problem, curating a new dataset, retraining the model, re-evaluating it for assurance, and redeploying it back into production. Then, the cycle starts all over again. 

We explore this remediation pathway and how to put it into practice in our new white paper, “Model Drift and The Day 3 Problem.” Read it now to learn how to make remediation a regular part of your MLOps workflow.

https://bit.ly/3Ypc9DH

#AI #ML #MLOps #ModelDrift #ModelRemediation

Evaluating machine learning operations (MLOps) vendors can be daunting. To make it easier, we’ve put together an MLOps Vendor Scorecard. 📋

This worksheet guides you through the vendor selection process, highlighting over 38 criteria to help you make systematic, apples-to-apples comparisons about your options. 

Download your free copy today  to find the right MLOps partner for your enterprise.

https://bit.ly/3xZ0a4E

#AI #ML #MLOps #Worksheet #Scorecard

⌚Last chance to connect with the Striveworks team at the 2024 Esri User Conference. Visit Booth Z10 now to see a demo of our no-code geospatial imagery toolkit (complete with ArcGIS integration), and learn how we’re empowering GIS technicians with easy-to-use AI. 

#AI #MachineLearning #GIS #ArcGIS #Esri #ComputerVision #EsriUC2024

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