Also Read: Hivelocity Launches Bare Metal Cloud Suite to Deliver Predictable Cloud Economics

Furthermore, the introduction of Protera Partnerportal on the SAP Store offers businesses an intuitive, mobile-ready interface that integrates directly with back-end SAP systems to streamline order history tracking, invoices, and e-commerce collaboration without complex data replication. Highlighting the executive vision driving these architectural shifts, Frank Powell, Chief Strategy Officer at Protera, stated, “Joining the SAP PartnerEdge Build and Sell tracks and bringing Protera Partnerportal to SAP Store represents a major milestone for our organization and customers alike,” while adding that the native portal deployment “reinforces our commitment to delivering SAP-native innovation that streamlines operations, improves efficiency, and enhances the customer experience.” Ultimately, this consolidated operating model allows regulated enterprises to confidently replace costly technical overhead with rapid, highly optimized business processes governed strictly by SAP best practices, a milestone that Mike BeDell, CEO of Protera, concluded, “strengthens our ability to deliver innovative, automation-driven solutions, giving customers a faster, simpler path to modernization.”

Read More: Protera Advances SAP Partnership with SAP PartnerEdge Build and Sell Tracks and Partnerportal Availability on SAP Store

The post Protera Deepens SAP Alliance with PartnerEdge Tracks and SAP Store Launch appeared first on ITDigest.

Also Read: Hivelocity Launches Bare Metal Cloud Suite to Deliver Predictable Cloud Economics

This automated lifecycle significantly compresses digital transformation timelines, allowing enterprises to rapidly replace decades of technical debt with agile, governed agentic workflows. Emphasizing the operational philosophy driving this collaboration, John Higgins, chief of client and partner success at Pega, stated: “Modernizing core systems isn’t just about moving old code to the cloud – it’s about reimagining how work gets done. By embedding Pega Blueprint AI directly into the time-tested AWS Transform analysis capability, we’re removing friction from the most painful stages of modernization and helping enterprises finally move from legacy COBOL to intelligent, cloud‑ready applications faster and with greater confidence.” Currently available within the AWS Transform platform at no additional cost to existing clients, this joint capability bridges the gap between infrastructure legacy dependencies and the modern requirements of scalable corporate automation.

Read More: Pega Launches AI-Driven Modernization Capability on AWS To Reimagine Legacy Mainframe Apps Into Cloud-Native Systems

The post Pega and AWS Partner to Fast-Track Mainframe-to-Cloud Modernization via Generative AI appeared first on ITDigest.

The Gap Between AI Infrastructure and Enterprise AI

GPU cloud operators have invested heavily in accelerated infrastructure. NVIDIA Grace Blackwell, NVIDIA Blackwell, and NVIDIA Hopper systems are being deployed at scale, and neocloud revenue is growing rapidly. For many operators, the business model has been overly simple: rent GPU hours at a per-hour rate.

However, the needs of enterprise organizations extend beyond raw compute. Increasingly, enterprise organizations need managed development environments, distributed training orchestration, model fine-tuning pipelines, SSO and RBAC, usage tracking, and compliance tooling. Building this platform infrastructure in-house takes months of engineering and ongoing maintenance that most GPU cloud operators are not staffed to deliver.

“Operators shouldn’t have to build an AI development platform from scratch just to make their GPU infrastructure useful to enterprise teams. Saturn Cloud gives them managed environments, training orchestration, fine-tuning, OpenAI-compatible inference endpoints, and per-token billing on day one,” said Sebastian Metti, Founder, Saturn Cloud.

Also Read: AtScale Announces Integration for Snowflake Semantic Views to Extend Governed Metrics to Power BI and Excel 

How Saturn Cloud Works

Saturn Cloud’s Token Factory platform gives AI teams a way to fine-tune and serve open models without managing infrastructure. The workflow is straightforward: upload a dataset, configure a fine-tuning job, and deploy the resulting model to an inference endpoint, all within the operator’s branded environment.

Fine-tuning jobs support supervised fine-tuning (full-weight and LoRA) on open models, with automatic DeepSpeed multi-GPU configuration when the selected instance has multiple GPUs. Users specify a base model, dataset, and a small set of hyperparameters; Saturn Cloud renders a complete training config and handles orchestration, retry, and checkpoint output. Supported training frameworks include Axolotl, vLLM, Unsloth, TRL, PEFT, and DeepSpeed.

Datasets are typed, validated collections of training data in conversational, instruction, text, or pretokenized formats. Users can upload datasets directly, import from external sources (S3, NFS), or curate data in a managed workspace and then register it as a Token Factory dataset. All dataset storage uses high-performance parallel filesystems rather than object storage, eliminating the cold-start tax that degrades GPU utilization during training jobs.

Checkpoints and artifact lineage are managed automatically. When a fine-tuning job completes, the resulting checkpoint is registered in Saturn Cloud’s artifact registry, preserving the full lineage from training run to model weights. Checkpoints are immediately available as inputs to inference endpoint deployments.

Inference endpoints deploy fine-tuned or base models as persistent serving deployments backed by vLLM, with per-deployment subdomains, health monitoring, and per-token metering. The serving configuration (dtype, max context length, quantization) is rendered at deployment time and no custom serving scripts are required.

The entire workflow is isolated per organization. Token Factory resources are scoped to the tenant, so datasets, checkpoints, and endpoints from one customer are never visible to another.

What Operators Get

Saturn Cloud gives GPU cloud operators a turnkey path from bare-metal infrastructure to a revenue-generating AI platform. The operator-facing layer includes white-label branding, per-token and per-GPU-hour billing infrastructure, tenant onboarding and self-service provisioning, usage dashboards and chargeback reporting, and enterprise security tooling including SSO, RBAC, and SOC 2 compliance.

Without a platform layer, operators sell compute hours and compete on price. With Saturn Cloud, they sell a platform and compete on developer experience, security posture, and time to production. Saturn Cloud enables operators to pass enterprise security reviews because the compliance tooling is already in place. It lets operators show tenants usage dashboards, cost controls, and team management. It gives the operator’s sales team a product demo, not a spec sheet.

Source: PRNewswire

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The gap between migration and modernization is becoming sort of impossible to ignore. Accenture is reporting that 59% of workloads still hang around on-premises or in legacy environments, while only 2% of organizations have actually integrated data and AI capabilities for real time insights. And you know those figures they do tell a story. Because companies are moving the infrastructure but a lot of them are not rebuilding the underlying foundations that are required for long term value, so it feels like the ‘move’ happened but the ‘modern’ part didn’t, not really.

Cloud migration is the process of moving applications, data, and workloads to the cloud. Cloud modernization is what happens next. It involves redesigning architectures, reducing technical debt, improving operational models, and preparing systems for future technologies. The organizations creating meaningful outcomes understand that migration is an event. Modernization is a strategy.

Setting the Foundation Through Workload Assessment and Cloud Readiness

Most migration failures do not begin during migration. They begin months earlier when teams assume they understand their environments better than they actually do. A surprising number of enterprise systems run on years of undocumented decisions, hidden integrations, and legacy dependencies that only become visible when someone tries to move them.

That is why workload assessment matters. Before selecting tools, platforms, or timelines, organizations need a clear picture of what exists today. Legacy architecture audits help identify technical debt. Dependency mapping exposes relationships between applications, databases, APIs, and infrastructure components. Without that visibility, even simple migrations can turn into expensive recovery projects.

There is also a business side to this process that often gets overlooked. IT may want modernization. Finance may want lower costs. Operations may want stability. Security teams may want tighter controls. All of them are technically right, but cloud migration strategies rarely succeed when every stakeholder is optimizing for a different outcome.

Alignment matters because KPIs drive decisions. If the goal is cost optimization, the migration path may look different from a strategy focused on AI readiness or scalability. Cloud readiness assessments should therefore evaluate technology, governance, operations, talent, and business objectives together rather than in isolation.

The Application Modernization Matrix Through the 7 R’s

One of the quickest ways to create problems is treating every application the same. Not every workload deserves the same investment, and not every system belongs in the cloud.

The 7 R’s framework helps organizations make smarter decisions.

Rehost involves moving applications with minimal changes. It is fast and often useful for reducing data center dependencies.

Replatform introduces targeted improvements without completely redesigning the application.

Refactor takes things further by redesigning applications around cloud-native principles, microservices, containers, and automation.

Repurchase replaces legacy software with modern SaaS solutions.

Retain keeps selected workloads where they are because migration may not deliver enough value.

Retire removes applications that no longer justify the cost of maintenance.

Relocate shifts workloads without major architectural changes.

On paper, these options look straightforward. In practice, they involve trade-offs. Lift-and-shift projects often move faster, but they can also carry old inefficiencies into a new environment. Refactoring creates greater long-term flexibility, although it requires more upfront effort and investment.

This is where strategy becomes more important than speed. AWS notes that its Migration Acceleration Program, built from thousands of enterprise migration experiences, has helped organizations achieve average outcomes including 31% infrastructure savings and 62% more efficient IT infrastructure management. Those results highlight a simple reality. Migration decisions influence operational performance long after the project is complete.

Building the Modernization Factory Through Automation and AI Integration

Many organizations still approach modernization as a one-time initiative. The problem is that technology never stands still. By the time one transformation project ends, another requirement appears.

That is why leading enterprises focus on creating repeatable modernization capabilities rather than isolated projects.

DevOps practices play a huge role here, you know, CI and CD pipelines they help teams ship updates more often, while at the same time cutting down on deployment risk. Rather than leaning on those big release cycles, orgs can push out small incremental improvements and also sanity-check changes using automated testing.

And automation adds yet another layer of value. AWS says modernization efforts have already moved tens of thousands of virtual machines, processed about 4.5 billion lines of code, saved roughly 1.69 million hours of manual work, and sped up modernization tasks by as much as five times. Those numbers reflect something bigger than efficiency. They show how automation is changing the economics of modernization.

Data modernization is equally important. Many enterprises migrate applications but leave data environments stuck in the past. That approach creates limitations later when AI initiatives enter the conversation.

Modern data lakes, scalable data pipelines, vector databases, and API-driven architectures create the foundation needed for machine learning, advanced analytics, and Retrieval-Augmented Generation workflows. Organizations that modernize both applications and data are far better positioned to support future innovation.

Also Read: How to Develop a Comprehensive Cybersecurity Framework for Modern Enterprise Protection?

A Security-First Paradigm for Governance and Compliance

Security has a habit of becoming urgent only after something goes wrong. Cloud modernization requires the opposite mindset.

Zero Trust Network Architecture kind of became a core idea, since older perimeter based security doesn’t really match how modern systems actually work. Every person, app workload, device, and even each link in between has to be continuously checked and re-checked, not just once.

Identity and Access Management is the piece that really matters here. With automated IAM policies you can enforce least privilege access, which helps cut down the chance of human slips. And honestly, as the cloud gets bigger and more tangled, manual ways to manage permissions become unsustainable pretty fast.

Governance also counts a lot; maybe even more than folks think. Companies need unambiguous rules for data stewardship, where workloads are allowed to run, the compliance expectations, and the access guardrails. Laws like GDPR, HIPAA, and PCI-DSS don’t magically disappear after migration, they still apply. The difference is that responsibilities are now shared between providers and customers.

Microsoft kind of frames its Azure migration abilities as a full, end to end modernization thing, and it also says Azure Copilot can help move teams from discovery over to execution in hours, not weeks. Microsoft further points to Azure Red Hat OpenShift, like it helps organizations take AI pilots into production sooner, but with governance, security, and scale built in. That mix really matters, since innovation without governance tends to create a lot of risk, and governance without innovation can slide into stagnation.

Maximizing Value Through FinOps and Continuous Performance Optimization

Reaching the cloud is not the same thing as extracting value from it. Many organizations discover that lesson after migration is complete.

Cloud environments introduce flexibility, but they also introduce financial complexity. Resources can scale instantly. Costs can do the same.

FinOps is basically there to close that gap, you know. It brings engineering, finance, and business teams together around one shared objective, which is maximizing value while still keeping accountability in place.

Continuous optimization then becomes the everyday operating model. Teams keep an eye on consumption, right size resources, cut off waste, and nudge efficiency forward across container based and serverless setups. Those tiny improvements add up over time, and more often than not they turn into meaningful savings without hurting performance, or at least not in a noticeable way.

The opportunity remains enormous. McKinsey estimates that cloud adoption could generate $3 trillion in value by 2030. Yet only 10% of organizations have fully captured cloud’s potential value. The challenge is no longer getting to the cloud. The challenge is turning cloud investments into measurable business outcomes.

Key Takeaways for Enterprise Leaders

The biggest mistake organizations make is assuming cloud migration is the transformation. It is not. It is the admission ticket.

Real transformation happens when migration becomes modernization. That means, doing something with technical debt, picking the proper migration approach, then building automation capabilities that actually stick, also strengthening governance and modernizing the data foundations then keep on continuously tuning performance, as things evolve.

I mean organizations that just try to move workloads, usually end up with the same kind of headaches, just in a new environment, and it can feel a bit pointless. Organizations that lean into modernization instead, tend to craft platforms that are ready for expansion, better resilience, and future AI initiatives, not only for the next release, but for what comes after that too.

The post Best Practices for Cloud Migration and Modernization: A Strategic Roadmap for Enterprise Success appeared first on ITDigest.

Also Read: IBM Cloud Unveils Sovereignty Risk Profile to Strengthen AI and Data Compliance

Highlighting the strategic impact of this deployment model on enterprise operations, Richard Nicholas, CEO of Hivelocity, stated, “Our new SaaS Bundle represents the convergence of cloud-native automation and bare metal performance. For companies managing data-intensive workflows, public cloud costs have become an erratic, major operational burden. With this suite, we are giving organizations the automated deployment and scaling capabilities they expect from the public cloud, but with the dedicated power, security, and absolute price predictability that only bare metal infrastructure can provide. It is designed to change how growing technology companies scale their operations without compromising their bottom line.” Ultimately, by bridging operational flexibility with infrastructure-level transparency, Hivelocity provides digital businesses with a scalable, highly reliable alternative to public cloud hyper-scalers, enabling predictable financial management alongside uncompromised performance.

Read More: Hivelocity Launches SaaS Bundle – Cloud Workflow, Bare Metal Economics, Predictable Bill

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The post ITDigest’s Weekly News Roundup Featuring Microsoft, NVIDIA, Experian, Mayo Clinic, NetApp and Cisco appeared first on ITDigest.

The XMLA Endpoint helps customers extend governed business definitions from Snowflake Semantic Views to Microsoft Power BI and Excel without mirroring data into another analytics environment, relying on stale extracts, or recreating metric logic outside Snowflake. An analyst can ask Snowflake CoWork or CoCo about gross margin and see the same answer in a Power BI dashboard or Excel pivot table, with data and compute remaining on Snowflake.

Snowflake Semantic Views provide a trusted semantic layer for Snowflake AI applications, including Snowflake CoWork and Cortex Agents, by defining consistent business metrics and dimensions. AtScale extends those same definitions to Power BI and Excel through live connections, giving AI agents, dashboards, and spreadsheets a shared semantic foundation.

“AI did not create the metrics consistency problem. It exposed it,” said Chris Lynch, CEO of AtScale. “For years, companies got away with different numbers in dashboards, spreadsheets, and reports because humans could reconcile the gaps. Agents won’t get the benefit of the doubt. XMLA Endpoint for Semantic Views, powered by AtScale, is a statement about where enterprise AI and analytics are headed: governed semantics in Snowflake, live access from the tools business users already trust, and data and compute in Snowflake. Customers own their semantics, choose their tools, choose their compute platform, and build for whatever comes next.”

Also Read: Silobreaker Introduces Agentic AI for Intelligence Workflows With Embedded Analysis and Built-in Governance 

For Snowflake customers, the embedded AtScale product is designed to:

• Bring governed Snowflake metrics to Microsoft analytics users through live access from Power BI and Excel.
• Use XMLA-based connectivity so Power BI and Excel users can query trusted Snowflake metrics without fragile extracts or disconnected semantic logic.
• Keep analytics workloads on Snowflake without mirroring data into another platform, creating duplicated extracts, or moving compute away from Snowflake.
• Deliver consistent answers across Cortex Agents, CoWork, dashboards, and spreadsheets by grounding each experience in the same governed definitions.

“Snowflake customers should not have to move or duplicate semantic context to give business users access to governed metrics in the tools they already use,” said Josh Klahr, Product Manager at Snowflake. “With Snowflake’s XMLA Endpoint for Snowflake Semantic Views, powered by AtScale, customers can extend trusted business definitions to Power BI and Excel while keeping Snowflake as the foundation for consistent AI and analytics.”

For enterprises with broader semantic requirements, AtScale Enterprise extends this foundation beyond the embedded Snowflake use case to support time intelligence, hierarchies, dimensional modeling, advanced business calculations, additional analytic tools, AI agents, and data platforms. This keeps Snowflake at the center while allowing trusted business logic to reach the AI agents, warehouses, analytic tools, and SaaS applications enterprises depend on.

Source: BusinessWire

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Based on the press release, the deployment of Hyland’s Content Innovation Cloud on Microsoft Azure cloud technology and AI services is set to give organizations a unified solution for managing and deriving value out of their business content through the use of artificial intelligence. In particular, the solution is intended to empower organizations to leverage AI agents and automate business workflows by ensuring secure access and interpretation of business documents and records.

This strategic alliance between Hyland and Microsoft comes after the realization by enterprises of the importance of a proper content infrastructure alongside sophisticated AI models. Many companies face challenges related to fragmented data storage systems and inadequate content governance solutions. Through this collaboration, Hyland and Microsoft expect to provide customers with the ability to derive value from their information assets.

Content Becomes the Foundation of Agentic AI

This can also be seen as part of a larger change happening across enterprise technology. Yes, initial AI use centered around generative AI assistants and chatbots. Organizations Though are shifting onto agentic AI systems, which autonomously perform tasks, orchestrate workflows and drive business execution. But to be successful, AI agents will need factual contextual well-governed information.

As a result, enterprise content management has advanced from document management back-office operations to a strategic technology priority. Documents contracts invoices, customer records, operational dataare all integral assets to intelligent automation.

Hyland’s Content Innovation Cloud acts as a back end to enable this as it is a central platform where enterprise content can be interrogated, sorted and made available to artificial intelligence systems. This is reinforced by its partnership with Microsoft Azure providing scalable infrastructure and accessing the better AI solutions.

Also Read: NVIDIA Unveils Cosmos 3 to Accelerate the Next Generation of Physical AI 

Impact on the IT Industry

For the IT industry, the partnership underscores the growing convergence of cloud computing, content management, and artificial intelligence. As organizations invest in AI-driven transformation initiatives, demand is rising for platforms that can bridge the gap between enterprise data and intelligent automation.

The collaboration is likely to accelerate investment in content intelligence, data governance, cloud migration, and AI orchestration technologies. IT teams are increasingly tasked with ensuring that AI systems can access business information securely while maintaining compliance, privacy, and regulatory standards.

The announcement also reinforces the importance of cloud ecosystems in enterprise AI adoption. Major cloud providers such as Microsoft, AWS, and Google Cloud are expanding partnerships with software vendors to create integrated AI environments that simplify deployment and improve interoperability across enterprise systems.

Business Impact and Industry Outlook

For businesses, the Hyland-Microsoft collaboration could help address one of the most significant barriers to AI adoption: access to usable enterprise information. Many organizations possess vast amounts of valuable content but struggle to extract actionable insights due to fragmented systems and manual processes.

By enabling AI agents to interact with enterprise content more effectively, businesses may be able to automate document-intensive workflows, improve customer service, accelerate decision-making, and enhance operational productivity. Industries such as healthcare, financial services, government, insurance, and manufacturing, where large volumes of documents and records play a central role, could see particularly strong benefits.

The partnership may also help organizations scale AI initiatives more rapidly by providing a structured foundation for intelligent automation. Rather than deploying isolated AI applications, businesses can build connected ecosystems where content, workflows, and AI agents operate together seamlessly.

Looking ahead, the collaboration illustrates a growing reality within enterprise technology: AI success increasingly depends on content readiness. As organizations continue investing in agentic AI, platforms that combine content intelligence, governance, and cloud-scale AI capabilities are likely to become critical components of future digital transformation strategies. Hyland and Microsoft’s expanded partnership positions both companies at the center of this evolving enterprise AI landscape.

The post Hyland and Microsoft Expand Collaboration to Advance the Agentic Enterprise on Azure appeared first on ITDigest.

In Hardware and Network news this week…

Lightera and Nokia Partner to Expand Optical LAN Deployment Across Europe
Lightera and Nokia have partnered to accelerate the deployment of optical LAN infrastructure across Europe. The collaboration aims to provide enterprises with scalable, energy-efficient, and high-performance connectivity solutions. As organizations modernize network environments, optical LAN technology is emerging as a key enabler of next-generation digital infrastructure.

In Artificial Intelligence news this week…

Expert.ai and Fincons Expand Partnership to Advance Neuro-Symbolic AI Adoption
Expert.ai and Fincons Group are expanding their partnership to accelerate the adoption of neuro-symbolic AI solutions. By combining machine learning with symbolic reasoning, the collaboration aims to improve explainability, contextual understanding, and enterprise decision-making. This reflects the growing demand for more transparent and trustworthy AI systems.

In Cloud Security & Compliance news this week…

IBM Cloud Unveils Sovereignty Risk Profile to Strengthen AI and Data Compliance
IBM Cloud has introduced a Sovereignty Risk Profile solution designed to help enterprises address evolving AI and data compliance requirements. The offering provides organizations with deeper visibility into regulatory risks, data residency, and governance obligations. As global data regulations tighten, sovereign cloud strategies are becoming increasingly critical.

In Enterprise AI news this week…

Snowflake to Acquire Natoma to Strengthen Governance for Enterprise AI Agents
Snowflake has announced plans to acquire Natoma to strengthen governance and oversight for enterprise AI agents. The acquisition is aimed at helping organizations manage security, compliance, and operational control as AI agents become more autonomous. This highlights the growing importance of governance frameworks in enterprise AI adoption.

In Quantum Computing news this week…

Crypto4A Launches QXVault to Simplify Quantum-Safe Secrets Management
Crypto4A has launched QXVault, a platform designed to simplify quantum-safe secrets management. The solution helps organizations protect sensitive credentials and cryptographic assets against future quantum threats. As quantum computing advances, enterprises are increasingly investing in post-quantum security strategies to safeguard critical infrastructure.

In FinTech news this week…

OpenAI Unveils Self-Improving AI Tax Agents Powered by Codex
OpenAI has introduced self-improving AI tax agents powered by Codex to automate and optimize tax-related workflows. These AI agents are designed to continuously learn and adapt, improving efficiency and accuracy in financial operations. The development signals a growing shift toward autonomous AI systems in enterprise finance.

In HealthTech news this week…

JotPsych Launches JotMeet, an Integrated Telehealth Product
JotPsych has launched JotMeet, an integrated telehealth platform aimed at streamlining virtual care delivery. The solution combines communication, scheduling, and patient engagement tools to improve telemedicine experiences for healthcare providers and patients alike. This reflects the continued growth of digital healthcare services and remote care adoption.

In Cybersecurity news this week…

Proofpoint Expands AI Security Strategy with Claude Compliance API Integration
Proofpoint has expanded its AI security strategy through integration with the Claude Compliance API. The move enhances monitoring, governance, and compliance capabilities for enterprises using generative AI systems. As organizations deploy AI tools at scale, comprehensive security oversight is becoming a core enterprise requirement.

Article of the Week

Security Challenges for Smart Medical Devices in Hospitals: How Healthcare Providers Can Reduce Cyber Risk

Connected medical devices are transforming healthcare delivery, but they are also expanding the cyberattack surface for hospitals and healthcare providers. From patient monitoring systems to smart diagnostic tools, these devices often operate in highly sensitive environments with strict uptime requirements. To reduce cyber risk, healthcare organizations must adopt stronger security frameworks, continuous monitoring, device segmentation, and proactive threat management strategies. As digital healthcare ecosystems grow, securing smart medical infrastructure will remain a critical priority.

The post ITDigest’s Weekly News Roundup Featuring Lightera, Nokia, Expert.ai, IBM, Snowflake, Natoma, OpenAI appeared first on ITDigest.

Also Read: Promevo Unveils New Gemini AI Services to Accelerate Enterprise Adoption

It helps organizations monitor cloud workloads. It also checks compliance with data residency and encryption rules. Finally, it creates audit-ready evidence for regulators. IBM’s larger sovereignty strategy has four pillars: provability, prevention, privacy, and portability. These pillars help businesses manage data access, encryption keys, where they deploy, and how they move workloads in hybrid and multi-cloud environments. The offering also complements IBM Sovereign Core, reinforcing IBM’s push to help highly regulated industries securely scale AI and cloud operations while addressing growing global compliance demands.

Read More: IBM Cloud Announces Sovereignty Risk Profile

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