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Investment Dynamics in AI Infrastructure: Renovus Capital

The acceleration of digital transformation is creating an unprecedented demand for robust, scalable, and secure infrastructure. As businesses race to adopt artificial intelligence (AI) and machine learning (ML) capabilities, they need an underlying infrastructure that can support these technologies, while also addressing operational efficiency, data security, and disaster resilience. One significant development in this sector is the recent investment by Renovus Capital Partners, a Philadelphia-based private equity firm, in Performive—a managed IT services provider specializing in cloud, infrastructure, and cybersecurity solutions. This move exemplifies the latest dynamics in AI infrastructure investment, where capital is flowing toward firms with the expertise and technological foundation to support the AI revolution. In this article, we’ll explore why such investments are crucial and how they signal broader shifts in technology, security, and business strategy. The Investment Landscape: Why Infrastructure is Critical for AI Growth The growing emphasis on digital-first business strategies has increased the demand for cloud services and managed IT solutions that can seamlessly support AI workloads. Key sectors such as finance, healthcare, and manufacturing are adopting AI-driven insights, from predictive analytics to automated decision-making. To ensure these technologies run smoothly and securely, companies need reliable infrastructure that can handle massive volumes of data and complex computational tasks. This is where managed IT service providers like Performive play a critical role. Renovus’s investment in Performive underscores the market’s recognition that scalable and secure IT infrastructure is foundational to the adoption and expansion of AI technologies. For private equity firms, this type of investment is strategic: it positions them at the forefront of an industry undergoing transformation, poised to see high growth as AI adoption rates increase. Performive’s Role in AI-Driven Digital Transformation Founded in 2005, Performive has earned a strong reputation in the mid-market enterprise segment by offering mission-critical services tailored to organizations that require both scalability and robust cybersecurity. With services spanning cloud management, infrastructure optimization, and data security, Performive addresses key needs that arise as companies leverage AI to enhance customer experience, streamline operations, and make more informed business decisions. Gary Simat, Performive’s Co-Founder and CEO, emphasized the significance of this partnership, describing it as a "pivotal moment" for the company’s future. This capital injection will not only fuel Performive's growth but also enable it to expand its service offerings, acquire complementary businesses, and strengthen its technical capabilities—all with the goal of better supporting AI-ready infrastructure for mid-sized enterprises across the U.S. Strategic Implications for AI Infrastructure Investments Private equity firms, including Renovus, have developed investment theses around differentiated managed service providers (MSPs) with recurring revenue streams, vital service offerings, and a strong commitment to customer service. Performive’s fit within this framework illustrates several important trends in the AI infrastructure market: - Scalability and Flexibility: As companies incorporate AI into core processes, they require scalable infrastructure solutions that can evolve with their growth. Performive’s cloud and infrastructure services are designed to provide this flexibility, allowing clients to adapt their IT resources based on demand. - Enhanced Security and Compliance: AI applications often involve sensitive data, whether in healthcare, finance, or other industries. Performive’s focus on cybersecurity positions it as a valuable partner for companies that must navigate stringent regulatory requirements, ensuring that AI initiatives do not compromise data integrity. - Customer-Centric Innovation: Renovus’s backing will allow Performive to continue to refine its customer experience, a crucial differentiator as businesses seek IT providers that offer high-touch, customizable solutions. By investing in innovation, Performive can stay ahead of the technology curve, ensuring its clients benefit from the latest advances in AI infrastructure. - AI and MLOps (Machine Learning Operations): With AI applications growing more complex, MLOps—practices that streamline the development and deployment of machine learning models—has become a priority for infrastructure providers. Performive is expected to channel some of the new funding toward enhancing its MLOps capabilities, which will make it easier for companies to deploy, manage, and monitor AI applications effectively. Broader Market Trends: AI, Cloud, and Cybersecurity The infrastructure segment of the AI ecosystem has been a major focus for investment, especially as enterprises accelerate cloud adoption and seek advanced data management solutions. According to recent studies, global spending on AI is projected to reach $300 billion by 2030, with infrastructure investments comprising a large portion of that total. Renovus’s recent move to partner with Performive aligns with these projections and reflects the growing importance of infrastructure providers in the AI value chain. With increasing cyber threats, there is also a heightened focus on security within AI applications. Managed IT service providers, particularly those offering cybersecurity alongside cloud infrastructure, are in a strong position to meet this demand. Performive’s investment in cybersecurity solutions will likely continue to be a critical component of its service offering as it grows. The Future of AI Infrastructure Investment Renovus’s partnership with Performive is just one of many signs that AI infrastructure will remain a high-priority investment area. Other private equity and venture capital firms are similarly seeking companies that are well-positioned to support the deployment of AI solutions across industries. Looking forward, we can expect a few key developments in this space: - Increased Mergers and Acquisitions: As companies look to scale their offerings, we may see a rise in mergers and acquisitions among MSPs, cloud providers, and data management firms. For instance, Performive may consider acquiring smaller companies with specialized technology to further bolster its capabilities. - Focus on Edge Computing: With the expansion of IoT and real-time AI applications, edge computing is gaining traction as a way to process data closer to its source. Investments in edge-compatible infrastructure will likely grow, enabling faster, more efficient AI-driven insights. - Ethics and AI Governance: As infrastructure providers help companies deploy AI at scale, they will also play a role in upholding ethical standards and AI governance. Managed IT providers may increasingly offer compliance solutions that help clients navigate ethical challenges and regulatory requirements. - Emphasis on Sustainable Infrastructure: The environmental impact of AI infrastructure is drawing scrutiny. Providers like Performive will be expected to implement sustainable practices, from energy-efficient data centers to carbon-neutral cloud offerings, aligning with broader corporate sustainability goals. Conclusion: The Strategic Role of AI Infrastructure in a Data-Driven World Renovus Capital Partners’ investment in Performive demonstrates the vital role that infrastructure will play in the AI ecosystem. As companies integrate AI and ML technologies into their operations, the demand for secure, scalable, and resilient infrastructure is more urgent than ever. Performive’s growth trajectory, backed by Renovus, is likely to set a benchmark for the industry, showing how managed service providers can adapt and thrive in an increasingly AI-driven world. For mid-market enterprises, partnerships like this one mean access to high-quality, customer-centered infrastructure that supports digital transformation. Looking ahead, AI infrastructure will continue to attract substantial investment, not only from private equity but also from other strategic investors who see the potential in fueling the next wave of AI-powered business innovation. Read the full article

[sifytech]

Kickstarting your MLOps journey in 2024: Essential practices and strategies

MLOps can create real value for your business, thereby becoming a data-driven champion in 2024 and beyond. Read More. https://www.sify.com/ai-analytics/kickstarting-your-mlops-journey-in-2024-essential-practices-and-strategies/

[shailesh-shetty]

An Easy-To-Understand Guide On MLOps

MLOps is an essential part of the machine learning process. It helps organizations streamline their ML workflow, ensure the accuracy and reliability of their ML models, and stay competitive in the rapidly-evolving market. To get your job done right the first time, collaborate with EnFuse Solutions today.

[govindhtech]

Unlock Efficiency NVIDIA’s MONAI Cloud Simplifies Imaging AI

Through fully controlled, cloud-based application programming interfaces, NVIDIA created a cloud service for medical imaging AI to further expedite and automate the generation of ground-truth data and training of specialized AI models.

Using pretrained foundation models and AI workflows for enterprises, the NVIDIA MONAI cloud APIs, which were unveiled at the Radiological Society of North America’s annual meeting this week in Chicago, offer developers and platform providers a quicker way to incorporate AI into their medical imaging offerings. The open-source MONAI project, started by King’s College London and NVIDIA, serves as the foundation for the APIs.

With medical imaging accounting for almost 90% of healthcare data, it is essential to the industry. Medical device manufacturers utilize it to give real-time decision assistance; radiologists and physicians use it for screening, diagnosis, and intervention; biopharma researchers use it to assess how clinical trial patients react to novel medications.

Due to the volume of work in each of these fields, a medical imaging-specific AI factory is needed, as well as an enterprise-grade platform that manages enormous amounts of data, produces ground-truth annotations, expedites the construction of models, and ensures the smooth deployment of AI applications.

Solution providers may more readily incorporate AI into their medical imaging platforms with NVIDIA MONAI cloud APIs, giving them the ability to offer radiologists, researchers, and clinical trial teams enhanced resources to create AI factories that are domain-specific. Early access to the APIs is offered via the NVIDIA DGX Cloud AI supercomputing service.

Flywheel, a top medical imaging data and AI platform that facilitates end-to-end workflows for AI development, is connected with the NVIDIA MONAI cloud API. Developers at firms that provide machine learning operations (MLOps) platforms, such as Dataiku, and medical image annotation companies, like RedBrick AI, are well-positioned to incorporate NVIDIA MONAI cloud APIs into their products.

Annotation & Training for Medical Imaging that is Ready to Run

A strong, domain-specific development foundation, including of state-of-the-art research, scalable multi-node systems, and full-stack software optimizations, is necessary to build effective and economical AI solutions. Additionally, it needs high-quality ground-truth data, which can be difficult and time-consuming to collect, especially for 3D medical images that need to be annotated by experts.

The VISTA-3D (Vision Imaging Segmentation and Annotation) foundation model powers interactive annotation in the NVIDIA MONAI cloud APIs. It was designed with continuous learning in mind, a feature that enhances the performance of AI models in response to human input and fresh data.

VISTA-3D is trained on a collection of annotated pictures from over 4,000 3D CT scans of different body parts and disorders, which speeds up the process of creating 3D segmentation masks for medical image analysis. The AI model’s annotation quality becomes better with continued learning.

This release provides APIs that enable it simple to create bespoke models based on MONAI pretrained models, which will speed up AI training even further. Auto3DSeg is another feature of the NVIDIA MONAI cloud APIs that streamlines the model creation process by automating hyperparameter tuning and AI model selection for a specific 3D segmentation assignment.

Recently, NVIDIA researchers used Auto3DSeg to win four challenges at the MICCAI medical imaging conference. These included artificial intelligence models to evaluate 3D cardiac and kidney CT scans, brain MRIs, and 3D ultrasounds of the heart.

Platform builders and solution providers adopt NVIDIA MONAI Cloud APIs

NVIDIA MONAI cloud APIs are being used by machine learning platforms and suppliers of medical imaging solutions to supply their clients with extremely valuable AI insights that expedite their job.

In order to expedite medical image curation, labeling analysis, and training, Flywheel has integrated MONAI with NVIDIA AI Enterprise and is currently providing NVIDIA MONAI cloud APIs. The Minneapolis-based company uses a centralized cloud-based platform to identify, collect, and train medical imaging data for the creation of reliable artificial intelligence. This platform is used by biopharma companies, life science organizations, healthcare providers, and academic medical institutes.

Dan Marcus, chief scientific officer of Flywheel, stated that “NVIDIA MONAI cloud APIs lower the cost of building high-quality AI models for radiology, disease research, and the evaluation of clinical trial data.” The integration of cloud APIs for automated segmentation and interactive annotation enables our medical imaging AI platform’s clients to construct AI models more quickly and produce creative solutions.

NVIDIA MONAI cloud APIs will also be used by annotation and viewer solution providers, such as Redbrick AI, Radical Imaging, V7 Labs, and Centaur Labs, to expedite the release of AI-assisted annotation and training capabilities, all without the need to host and maintain the AI infrastructure in-house.

RedBrick AI is providing interactive cloud annotation for its medical device customers that support distributed teams of physicians by integrating the VISTA-3D model made available through NVIDIA MONAI cloud APIs.

RedBrick AI CEO Shivam Sharma stated, “VISTA-3D enables our clients to quickly build models across various modalities and conditions.” “With accurate and dependable segmentation results, the foundation model can be easily adjusted for a range of clinical applications due to its generalizability.”

MLOps platform builders like Dataiku, ClearML, and Weight & Biases are also looking into using NVIDIA MONAI cloud APIs to speed up the construction of enterprise AI models.

To make the process of creating AI models for medical imaging applications even easier, Dataiku intends to use the cloud APIs for NVIDIA MONAI.

Through Dataiku’s web interface connected to an NVIDIA-hosted, GPU-accelerated service, “Auto3DSeg, a low-code option to accelerate the development of state-of-the-art segmentation models, would be easily used by Dataiku users with NVIDIA MONAI cloud APIs,” stated Kelci Miclaus, global head of AI health and life sciences solutions at Dataiku. “By giving data and domain experts the ability to create and implement AI-driven workflows, this democratizes AI in biomedical imaging.”

Register for early access to join the medical imaging pioneers who are using NVIDIA MONAI cloud APIs to accelerate AI research.

Read more Govindhtech.com

[clarifaiinc]

Machine Learning (ML) is an overall term given to calculations that can take in designs from existing information and utilize these examples to settle on forecasts or choices with new information. MLOps (Machine Learning Operations) is the utilization of AI models by advancement/activities (DevOps) groups.

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[salvatorede]

SAP Data Intelligence as a Machine Learning...

SAP Data Intelligence as an MLOps platform #SAPDataIntelligence #MachineLearning #MLOps #MachineLearningOperations

SAP Data Intelligence as a Machine Learning...

MLOps (from Machine Learning and Operations) refers to the process of managing the production lifecycle of Machine Learning models, including also the concept of collaboration between data scientists, data engineers and IT professionals. The objective is to define recommendations and best practices to automate the process, comply with regulatory requirements as well as provide agility to react to changing business requirements

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[crystaluki]

SAP Data Intelligence as a Machine Learning...

SAP Data Intelligence as an MLOps platform #SAPDataIntelligence #MachineLearning #MLOps #MachineLearningOperations

SAP Data Intelligence as a Machine Learning...

MLOps (from Machine Learning and Operations) refers to the process of managing the production lifecycle of Machine Learning models, including also the concept of collaboration between data scientists, data engineers and IT professionals. The objective is to define recommendations and best practices to automate the process, comply with regulatory requirements as well as provide agility to react to changing business requirements

SAP Get Social

[oliverhuschke]

SAP Data Intelligence as a Machine Learning...

SAP Data Intelligence as an MLOps platform #SAPDataIntelligence #MachineLearning #MLOps #MachineLearningOperations

SAP Data Intelligence as a Machine Learning...

MLOps (from Machine Learning and Operations) refers to the process of managing the production lifecycle of Machine Learning models, including also the concept of collaboration between data scientists, data engineers and IT professionals. The objective is to define recommendations and best practices to automate the process, comply with regulatory requirements as well as provide agility to react to changing business requirements

SAP Get Social

[nicolesydneyaus]

SAP Data Intelligence as a Machine Learning...

SAP Data Intelligence as an MLOps platform #SAPDataIntelligence #MachineLearning #MLOps #MachineLearningOperations

SAP Data Intelligence as a Machine Learning...

MLOps (from Machine Learning and Operations) refers to the process of managing the production lifecycle of Machine Learning models, including also the concept of collaboration between data scientists, data engineers and IT professionals. The objective is to define recommendations and best practices to automate the process, comply with regulatory requirements as well as provide agility to react to changing business requirements

SAP Get Social

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