We are thrilled to announce several major additions to Modzy’s Edge capabilities with the release of Modzy v1.6. These additions, from accelerated inference times to expanded connectors and integrations, make it possible to execute machine learning workloads at speed and scale on nearly any device from the cloud, to on-prem, to the edge. Modzy v1.6 offers significant improvements in inference speeds and model throughput, and support for batching, real-time, and streaming inferences at the edge. With these new capabilities, customers unlock the power of running accelerated ML workloads anywhere ..read more

As the opportunities to leverage AI expand, organizations are increasingly looking to run AI models at the edge. Edge computing refers to analyzing and processing data near where the data is generated, to decrease data flow and thereby reduce network traffic and response time. With the recent rise in use of machine learning- based technologies, and new interest in applications such as the Internet of Things (IoT), designing solutions that combine machine learning with edge computing emerged as a significant and valuable part of artificial intelligence (AI) research and development. What you ne ..read more

As organizations embark on their journeys to integrate AI and ML into every aspect of their businesses, there are many solutions in the market to accelerate the process. From model training platforms and frameworks, data management solutions, CI/CD pipelines, and more, there is no shortage of tools. Naturally, the cloud service providers offer a rich set of tools for each part of the ML pipeline, and for many who are already running many systems atop their infrastructure, it was easy to start using their MLOps solutions. But there are three main areas where cloud MLOps features come in second ..read more

We’re excited to announce that we’ve joined MxD as a Tier 3 Solution provider. MxD is a non-profit that brings hundreds of partners together to advance the future of the U.S. manufacturing industry. Its state-of-the-art innovation center offers its partners an ideal environment to focus on developing, demonstrating, deploying, and commercializing innovations that address manufacturing’s most pressing problems. “Organizations like MxD are critical for driving cutting edge innovation for the manufacturing industrial base in the U.S.,” said Josh Sullivan, Modzy CEO and Co-founder, “We’re excited ..read more

Three Types of Solutions There is no shortage of MLOps platforms in the market today. In fact, once you’ve made the decision to acquire an MLOps capability, it’s very easy to be overwhelmed by the different options, complementary or overlapping features, and to understand whether or not a solution meets your needs. There are many lists, market guides, categorizations and more that attempt to document all the possible options, but it boils down to three types of MLOps platforms. Cloud Native Platforms – each cloud service provider (CSP) includes native services for many components of the MLOps ..read more

GPU Computing (general-purpose computing on graphics processing units) enables many modern machine learning algorithms that were previously impractical due to slow runtime. By taking advantage of the parallel computing capabilities of GPUs, a significant decrease in computational time can be achieved relative to traditional CPU computing. CUDA, developed and provided free of charge by NVIDIA, is the parallel computing runtime and software API for developers used to power most leading machine learning frameworks. What you need to know By using the CUDA platform and APIs, the parallel computatio ..read more

Chances are, you’ve likely heard of Machine Learning Operations (MLOps) and how it can help you succeed at generating value with AI. And, if you’re reading this, you’re probably experiencing the telltale signs that you need to invest in an MLOps capability (e.g., overly long production deployment timelines, friction between data scientists and developers, and point solutions that don’t enable composable business and technical architectures.) This led you to where you are now in your solutions research, evaluating whether to build vs. buy an MLOps platform so you can present a clear concept, bu ..read more

It’s no surprise that machine learning and artificial intelligence offer great promise. By integrating these capabilities into applications and solutions that users rely on every day, organizations can drive process efficiencies, improve data-driven decision-making, increase revenue, and reduce costs. But AI-enabled transformation is often stalled due to talent shortages, lack of quality data, difficulty integrating emerging technologies, and evolving AI-related risks. Recent studies show that almost 50 percent of pilot projects never make it into production, and on average, it takes seven mon ..read more

Containers and Microservices have become the most popular way for new software deployments, and for application re-factoring, such as twelve factor apps. At Modzy, we use containers and Kubernetes for our entire platform. This blog covers choices, lessons learned, and how Modzy uses Kubernetes—and what that means for our customers. Why Kubernetes? Choice isn’t a huge factor when it comes to container orchestrators. Kubernetes, Hashicorp Nomad, Docker Swarm, and Marathon/Mesos are the primary tools in use today. In addition to cloud provider managed offerings such as Elastic Kubernetes Service ..read more

Modzy® announces being named as a Sample Vendor in nine 2022 Gartner® Hype Cycles, including the Hype Cycle for Data Science and Machine Learning, the Hype Cycle for Artificial Intelligence, and the Hype Cycle for Analytics and Business Intelligence. Modzy is named as a Sample Vendor for ModelOps, AI Trust, Risk, & Security Management (TRiSM), and Edge AI in the Hype Cycle for Artificial Intelligence. Modzy is name as a Sample Vendor for ModelOps and Explainable AI in the Hype Cycle for Analytics and Business Intelligence, and as a Sample Vendor for Explainable AI in the Hype Cycle for Dat ..read more