Empowering security excellence: The dynamic partnership between FreeDivision and IBM

In the ever-evolving landscape of cybersecurity, businesses are constantly seeking robust solutions to fortify their defenses and navigate the complex challenges posed by cyberthreats. FreeDivision, an IBM Business Partner, stands out in the field by understanding the local needs of its clients. Operating as a security service partner, FreeDivision leverages IBM’s endpoint detection and response (EDR) solution, IBM Security® QRadar® EDR, as part of its solution, freedivision.io, to address the unique security concerns of its clients.  

Clients look to FreeDivision for help in two key areas: Security audit and consultation, and incident response and recovery.  

Security audit and consultation

Many companies still underestimate the depth of security required, often relying solely on antivirus solutions. FreeDivision’s products and expertise distinctly stand out when conducting comprehensive security checks for clients. Its solution not only protects against threats, but also acts as a vigilant hunting tool.  

Through in-depth analysis of logs, FreeDivision guides clients toward fortified security postures, minimizing the risk of ransomware and other cyberthreats. Protection against ransomware is provided by IBM QRadar EDR’s adaptive system. Due to QRadar EDR, FreeDivision can resolve security incidents in seconds. The response procedures use artificial intelligence to prevent human error and enable rapid response to threats. 

“IBM Security QRadar EDR is like a powerful EDR–the built-in AI and automation make it fast, efficient and easy to use.”  —Sandro Huber, Chief Information Officer and Co-owner of FreeDivision

Incident response and recovery

For clients who seek assistance after falling victim to attacks, FreeDivision steps in to remediate the situation and fortify defenses. By engaging with clients who experienced a ransomware attack, FreeDivision not only resolves immediate threats, but also collaborates with them to establish resilient security measures for the future. To prepare its clients for any future attacks, FreeDivision has embedded IBM QRadar EDR to detect and block new and unknown threats, from ransomware to sophisticated file attacks, to memory-only attacks.  

A ransomware hacker attack 

When PeHtoo, a Czech manufacturer, was attacked by a ransomware hacker, it engaged FreeDivision to help it recover. Ivan Eminger, CEO of PeHtoo, tells the story: 

“By our standards, we have invested considerable resources in IT operations and security. Unfortunately, it turns out that this alone was not enough. We were attacked by a ransomware hacker. Our data was completely stolen and then encrypted. We had to start rebuilding the company from scratch. 

Fortunately, FreeDivision experts helped us set up new IT processes and security standards. Thanks to its MDR services, we have a constant overview of all user processes started in our company infrastructure and any deviation from normal user behavior is immediately addressed in an isolated environment outside of production operations. Combined with network security and a next-generation security gateway at the perimeter, we are now much better prepared to counter existing threats, allowing us to focus on the core activities of our business with greater peace of mind.” 

Why partner with IBM 

The choice of FreeDivision to build its solutions with IBM is rooted in the exceptional capabilities and support offered by IBM Security QRadar EDR, formerly known as ReaQta. It remediates known and unknown endpoint threats in near real-time with easy-to-use intelligent automation that requires little-to-no human interaction. You can make quick and informed decisions with attack visualization storyboards and leverage automated alert management and advanced continuous learning AI capabilities. 

FreeDivision shared 3 key features that made its decision to choose QRadar EDR easy: 

• The console is intuitive for users. It’s customizable, and easy to use. 
• The support from IBM is unparalleled. IBM not only delivers a solution but also stands by it when challenges arise. 
• Its customers appreciate the depth of their investigation tools. 

“Our strength lies in the perfect blend of IBM’s global stature and our localized insights,” says Sandro Huber, Chief Information Officer and Co-owner of FreeDivision. “It’s the combination of IBM’s cutting-edge technology and our deep understanding of what matters in the local market. It’s a dynamic relationship built on trust, expertise, and a shared commitment to elevating cybersecurity standards.” 

Source: ibm.com

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Build the foundation for SAP ERP modernization

Successful SAP ERP modernization programs begin with clear organizational alignment on wanted outcomes and expected business value, end-to-end scope and roadmap. This alignment is critical to enterprises that run their core operations on SAP ECC for years. It helps them determine where to start their modernization initiatives and how to prioritize, organize and plan to see the value of this investment.

To build this strategic plan, enterprises need a fact base that enables them to move forward with critical SAP S/4HANA-enabled transformation decisions across people, process, enterprise architecture and next-gen technology. 

With hundreds of successful implementations of S/4HANA programs that involved both SAP ERP applications and infrastructure modernization, IBM® has a well-defined approach that is called Rapid Discovery. This approach helps determine the what, why and how for SAP ERP modernization and is infrastructure agnostic. Whether you are running SAP ERP on AIX, IBM i, Linux® or Windows, the approach remains the same. IBM’s team of cross-functional experts uses innovative tools and frameworks to build a transformative foundation composed of six essential ingredients: 

1. Enterprise Capability Model—Agreement on the business process hierarchy that defines the scope of the ERP implementation while also defining business requirements in the to-be business processes 
2. Governance Model—Clear structure, framework and operating model for program oversight and implementation including key roles, responsibilities and decision authority 
3. Business Value—Financial case for change in transformation that quantifies the tangible benefits of the program and compares them to the costs of implementation 
4. Implementation Roadmap—Clear articulation of key architectural decisions, scope of services, data strategy and implementation roadmap for transformation 
5. Executive Alignment—Alignment of executives across the business on the purpose, priorities, path forward, responsibilities and business benefits of transformation 
6. Sustainability Framework—Alignment of sustainable goals into the overall ERP strategy to allow for single source of truth data access for regulatory requirements 

As part of Rapid Discovery, we also help clients work through issues that are related to the following enablers: 

• Modern Enterprise Architecture—Design the future-state enterprise architecture, including strategic direction for application rationalization and RISE or non-RISE cloud strategy. 
• Data and Analytics—Determine the current-state realities of data readiness and develop an optimized data and analytics strategy to support and utilize the move to SAP S/4HANA. 
• Security and Controls—Define the security and controls architecture after reviewing the current maturity levels. 
• Change management—Uncover and understand the organizational change management opportunities and impacts related to ERP transformation and develop a high-level approach to unlock user adoption and value realization.

This well-defined discovery process for SAP ERP modernization helps you assess your current SAP ERP landscape, define your to-be state and align on business case, operating model and a modern enterprise architecture. If you would like to learn more, join us for the webinar, “Build the foundation for SAP ERP modernization with rapid discovery assessment” where we take a detailed analysis of this process. 

Source: ibm.com

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Simplifying IAM through orchestration

The recent validated what many of us in the industry already knew: Identity has become the leading attack vector. The 2024 report showed a 71% increase in valid identities used in cyberattacks year-over-year. What really puts it into perspective is the realization that you are just as likely to have your valid identity used in a cyberattack as you are to see a phishing attack in your organization. Hackers don’t hack in; they log in.

The risk of valid identities being used as the entry point by bad actors is expected to continue with the ever-increasing applications and systems being added in today’s hybrid environments. We are finding an overwhelming majority of organizations are choosing to use different identity vendors that offer the best capability for each use case, instead of consolidating with one vendor. The use of various identity tools is further compounded with managing access to your legacy application infrastructure, integrating new users during mergers and acquisitions. The hybrid reality has also led to an inconsistent user experience for your workers, partners and customers, an increased risk of identity-based attacks, and added an additional burden on your admins. 

To solve the identity challenges created by today’s hybrid environments, businesses need a versatile solution that complements existing identity solutions while effectively integrating various identity and access management (IAM) silos into a cohesive whole. Solutions that help create a consistent user experience for your workers, partners and customers across all applications and systems. Organizations and industry analysts refer to this connected IAM infrastructure as an Identity fabric. Organizations have begun to move toward connecting multiple IAM solutions through a common identity fabric.

Securing the digital journey

To protect the integrity of digital user journeys, organizations use a range of tools spanning bot mitigation, identity verification and affirmation, user authentication, authorization, fraud detection and adjacent capabilities such as risk analytics and access management. Building and maintaining these integrations is complex and carries an operational overhead regarding time and resources. These various tools don’t easily interconnect and don’t generate standardized types of signals. As a result, the interpretation of the varied risk signals is siloed across different events along the digital user journey. This lack of an integrated approach to managing risk along the digital user journey hinders the adoption of continuous adaptive trust principles and adds undue risk into the system. Various, disconnected identity tools prohibit you from creating that consistent user experience and security controls. Orchestration solutions improve the efficacy and efficiency of risk management along digital user journeys.

Identity orchestration

Identity and access management projects are complex enough with many taking 12-18 months. They require skilled staff to solve today’s identity challenges such as integrating IAM silos together and modernizing access to legacy applications. Many of the solutions out there are not helpful and actually create more vendor lock-in. What is really needed is an open integration ecosystem that allows for flexibility and integrations that are simple and require fewer skills to accomplish. This is where an identity fabric and identity orchestration come into play. Orchestration is the critical component and the integration glue for an identity fabric. Without it, building an identity fabric would be resource-intensive and costly. Orchestration allows more intelligent decision-making and simplifies everything from onboarding to offboarding and enables you to build consistent security policies. Identity orchestration takes the burden off your administrators by quickly and easily automating processes at scale. This enables consistent, frictionless user experiences, while improving identity risk posture, and helping you avoid vendor lock-in. 

Benefits of identity orchestration

Design consistent, frictionless user experiences

Identity orchestration enables you to streamline consistent and frictionless experiences for your workers, partners and customers across the entire identity lifecycle. From account creation to login to passwordless authentication using passkeys to account management, makes it easy to orchestrate identity journeys across your identity stack, facilitating a frictionless experience. IBM’s identity orchestration flow designer enables you to build consistent, secure authentication journeys for users regardless of the application. These journeys can be built effortlessly with low-code, no-code orchestration engines to simplify administrative burden.

Fraud and risk protection

Orchestration allows you to combine fraud signals, decisions and mitigation controls, such as various types of authenticators and identity verification technologies. You can clearly define how trusted individuals are granted access and how untrusted users are mitigated with security authentication. This approach overlays a consistent and continuous overlaying risk and fraud context across identity journey. IBM Security® Verify orchestration allows you to bring together fraud and risk signals to detect threats. It also provides native, modern and strong phishing-resistant risk-based authentication to all applications, including legacy apps, with drag-and-drop work-flows.

Avoid vendor lock-in with identity-agnostic modernization

Organizations have invested in many existing tools and assets across their IAM stack. This can range from existing directories to legacy applications to existing fraud signals, to name a few. IBM Security Verify identity orchestration enables organizations to bring their existing tools to apply consistent, continuous and contextual orchestration across all identity journeys.It enables you to easily consolidate and unify directories, modernize legacy applications and streamline third-party integration for multifactor authentication (MFA), and risk and notification systems

Leverage IBM Security Verify

IBM Security Verify simplifies IAM with orchestration to reduce complexity, improves your identity risk posture, and simplifies the user journey by enabling you to easily integrate multiple identity system providers (IdPs) across hybrid environments through low-code or no-code experiences.

IBM provides identity-agnostic modernization tools enabling you to manage, migrate and enforce consistent identity security from one IAM solution to another while complementing your existing identity tools. By consolidating user journeys and policies, you can maintain security consistency across all systems and applications, creating frictionless user experiences and security controls across your entire identity landscape.

Source: ibm.com

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Unlocking business value: Maximizing returns from your SAP investments

Amid the dynamic realms of modern business and technology, organizations seek to maintain a competitive edge and elevate business outcomes and user experiences through their SAP investments. The crux of this endeavor lies in fostering continuous value creation throughout the journey. Drawing from my experience with clients across expansive, multi-year SAP engagements, there are three areas where collaborative value creation and charting future roadmaps intertwine seamlessly.

1. Value assurance throughout the engagement journey:

Value assurance is the cornerstone of every SAP engagement, ensuring alignment with strategic imperatives, adherence to predefined objectives and delivery of anticipated outcomes. To achieve this, a meticulous comprehension of the client’s requirements, goals and challenges is essential. It is important to structure the engagement journey with periodic assessments, clear milestones and ongoing feedback to ensure the transformation stays on track.

Using structured methodologies such as the IBM® SAP Value Continuum augments this assurance significantly. This comprehensive framework embraces the pillars of cooperate, co-execute and co-create to facilitate collaborative ideation, outcome delineation and executional prowess. This fosters a symbiotic journey towards value realization.

2. Drive micro transformations with process mining:

Micro transformations, characterized by incremental yet impactful alterations, serve as the catalysts for paradigm shifts in business outcomes. Using process mining methodologies unveils latent opportunities for enhancing operational efficiencies, thereby allowing clients to achieve quick wins and build momentum for larger-scale transformations.

Process mining is a powerful service offering that helps organizations identify inefficiencies in business processes and uncover areas for improvement. Whether streamlining order-to-cash processes, optimizing supply chain logistics or enhancing customer experience touchpoints, process mining catalyzes tangible enhancements such as cost reduction, heightened productivity and augmented customer satisfaction.

Process mining can help at every stage of the SAP lifecycle and enables the right decisions at the right time. Some of the key use cases are as follows:

• Process analysis: Mine critical processes for process behaviors, deviations and KPIs. Establish a performance baseline.
• Process visualization: Convert results into models or develop to collaborate and enrich. Establish as-is processes.
• Comparison and enrichment: Compare to industry best practices. Adjust for unique processes. Adopt standards and define critical unique processes.
• Simulation and measurement: Generate embedded business cases for value realization, including cost, time, resource and bottleneck impact. 
• Opportunity identification: Analyze process performance, blockers, root cause and recommendations. Get improvements and a business case.

3. Infusing latest technology trends:

In an era where mere stability is not enough, relentless innovation takes the lead. Constant evaluation of evolving business needs, coupled with technologies and product features, defines the pathway to market differentiation. Embracing the right blend of new technological trends such as generative AI (gen AI), RPA and new SAP products and features (such as BTP, Joule, SAP Build) resonates at every stage of the SAP lifecycle. As you work to achieve this balance, focus on:

• A strong foundation: Build the fact base to confidently move forward with SAP S/4HANA-enabled transformation decisions and SAP technology roadmap using advisory frameworks such as IBM® Rapid Discovery.
• Transformation: Co-execute your transformation journey by moving ERP to cloud with RISE with SAP and IBM ManagePlus offerings.
• Next-gen managed services: Deploy noiseless operations with gen AI-enabled digital operations and modernized ways of working.
• Sustainable and continuous innovation: Achieve continuous micro-transformations with intelligent process mining, SAP clean core capabilities and extreme automation.

A holistic approach to value creation in SAP engagements is essential to ensure that clients achieve their desired business outcomes. By focusing on value assurance throughout the engagement journey, driving micro-transformations with process mining, infusing the latest technology trends like gen AI and achieving high business outcomes, organizations embark on a transformative journey to achieve their strategic objectives.

Source: ibm.com

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How generative AI will revolutionize supply chain

Unlocking the full potential of supply chain management has long been a goal for businesses that seek efficiency, resilience and sustainability. In the age of digital transformation, the integration of advanced technologies like generative artificial intelligence brings a new era of innovation and optimization. AI tools help users address queries and resolve alerts by using supply chain data, and natural language processing helps analysts access inventory, order and shipment data for decision-making.

A recent IBM Institute of Business Value study, The CEO’s guide to generative AI: Supply chain, explains how the powerful combination of data and AI will transform businesses from reactive to proactive. Generative AI, with its ability to autonomously generate solutions to complex problems, will revolutionize every aspect of the supply chain landscape. From demand forecasting to route optimization, inventory management and risk mitigation, the applications of generative AI are limitless. 

Here are some ways generative AI is transforming supply chain management: 

Sustainability

Generative AI helps to optimize companies’ supply chains for sustainability by identifying opportunities to reduce carbon emissions, minimize waste and promote ethical sourcing practices through scenario analysis and optimization algorithms. For example, combining generative AI with technologies such as blockchain helps to keep data about the material-to-product transformation unchangeable across different entities, providing clear visibility into products’ origin and carbon footprint. This allows companies proof of sustainability to drive customer loyalty and comply with regulations. 

Inventory management

Generative AI models can continuously generate optimized replenishment plans based on real-time demand signals, supplier lead times and inventory levels. This helps maintain optimal stock levels that minimize carrying costs and can improve customer satisfaction through accurate available-to-promise (ATP) calculations and AI-driven fulfillment optimization. 

Supplier relationship management

Generative AI can analyze supplier performance data and market conditions to identify potential risks and opportunities, recommend alternative suppliers and negotiate favorable terms, enhancing supplier relationship management. 

Risk management

Generative AI models can simulate various risk scenarios, such as supplier disruptions, natural disasters, weather events or even geopolitical events, allowing companies to proactively identify vulnerabilities or react to disruptions with agility. AI-supported what-if modeling helps develop contingency plans such as inventory, supplier or distribution center reallocation. 

Route optimization

Generative AI algorithms can dynamically optimize transportation routes based on factors like traffic conditions, weather forecasts and delivery deadlines, reducing transportation costs and improving delivery efficiency. 

Demand forecasting

Generative AI can analyze historical data and market trends to generate accurate demand forecasts, which helps companies optimize inventory levels and minimize stockouts or overstock situations. Users can predict outcomes by quickly analyzing large-scale, fine-grain data for what-if scenarios in real time, allowing companies to pivot quickly. 

The integration of generative AI in supply chain management holds immense promise for businesses seeking to transform their operations. By using generative AI, companies can enhance efficiency, resilience and sustainability while staying ahead in today’s dynamic marketplace. 

Source: ibm.com

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How fintech innovation is driving digital transformation for communities across the globe

To meet the demands of today’s consumers, enterprises must be continuously innovating. But innovation doesn’t happen in silos. Fintechs, for example, have been transformational for the financial services industry, from democratizing finance to establishing digital currencies that revolutionized the way that we think of money. 

As fintechs race to keep up with the needs of their customers and co-create with larger financial institutions, they can leverage AI and hybrid cloud solutions to drive true digital transformation and meet these evolving demands. 

How Dollarito is connecting larger financial institutions with financially underserved communities 

According to research from the US Government Accountability Office, roughly 45 million people lack credit scores because they don’t have certain data points that credit scores are based on, which limits their eligibility. Traditional credit report models use parameters such as the status of an active loan or credit card payment records to give an individual a credit score. If someone does not fit within these parameters, it can be difficult to procure a loan, take out a mortgage or even buy a car. However, with a more accurate model, such as one powered by AI, financial institutions can better identify applicants who are fit for credit. This can result in a higher approval rate for these populations that otherwise would typically be overlooked. 

Dollarito, a digital lending platform, is focused on helping the Hispanic population with no credit history or low FICO scores access fair credit. The platform offers a unique solution that measures repayment capabilities by using new methodology based on AI, behavioral economics, cloud technology and real-time data. Leveraging AI, Dollarito’s models tap into a wide store of data from banking transactions, behavioral data and economic variables related to the credit applicant’s income source. 

With IBM Cloud for Financial Services, Dollarito, an IBM Business Partner, is able to scale their models continuously and quickly deploy the services that their clients need, while ensuring their services meet the standards and regulations of the industry.  

“Dollarito uses IBM Cloud for Financial Services technologies to optimize infrastructure and demonstrate compliance, allowing us to focus on our mission of providing financial services to underserved communities. We are dedicated to building a bridge of trust between these populations and traditional financial institutions and capital markets. With AI and hybrid cloud technologies from IBM, we are developing solutions to serve these groups in a cost-effective way while addressing risk.” – Carmen Roman, CEO and Founder of Dollarito 

Dollarito is also embracing generative AI, integrating IBM watsonx™ assistant to help its users interact easily and get financial insights to improve the likelihood of access to credit. Like IBM®, Dollarito recognizes the great opportunity that AI brings for the financial services industry, allowing enterprises to tap into a wealth of new market opportunities.  

How Ionburst is helping to protect critical data in a hybrid world 

Data security is central to nearly everything that we do, especially within financial services as banks and other institutions are trusted to protect the most sensitive consumer data. As data now lives everywhere, across multiple clouds, on-premises and at the edge, it is more important than ever before that banks manage their security centrally. And this is where Ionburst comes in. 

With their platform running on IBM Cloud, Ionburst provides data protection across hybrid cloud environments, prioritizing compliance, security and recovery of data. Ionburst’s platform provides a seamless and unified interface allowing for central management of data and is designed to help clients address their regulatory requirements, including data sovereignty, which can ultimately help them reduce compliance costs.  

Ionburst is actively bridging the security gap between data on-premises and the cloud by providing strong security guardrails and integrated data management. With Ionburst’s solution available on IBM Cloud for Financial Services, we are working together to reduce data security risks throughout the financial services industry. 

“It’s critical financial institutions consider how they can best mitigate risk. With Ionburst’s platform, we’re working to give organizations control and visibility over their data everywhere. IBM Cloud’s focus on compliance and security is helping us make this possible and enabling us to give customers confidence that their data is protected – which is critically important in the financial services sector,” – David Lanc and Anne Lanc, Co-Founders and Inventors of Ionburst 

Leveraging the value of ecosystems

Tapping into innovations from fintechs has immensely impacted the financial services industry. As shown by Ionburst and Dollarito, having an innovative ecosystem that supports your mission as a larger financial institution is critical for success and accelerating the adoption of AI and hybrid cloud technology can help drive innovation throughout the industry. 

With IBM Cloud for Financial Services, IBM is positioned to help fintechs ensure that their products and services are compliant and adhere to the same stringent regulations that banks must meet. With security and controls built into the cloud platform and designed by the industry, we aim to help fintechs and larger financial institutions mitigate risk, address evolving regulations and accelerate cloud and AI adoption. 

Source: ibm.com

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VeloxCon 2024: Innovation in data management

VeloxCon 2024, the premier developer conference that is dedicated to the Velox open-source project, brought together industry leaders, engineers, and enthusiasts to explore the latest advancements and collaborative efforts shaping the future of data management. Hosted by IBM® in partnership with Meta, VeloxCon showcased the latest innovation in Velox including project roadmap, Prestissimo (Presto-on-Velox), Gluten (Spark-on-Velox), hardware acceleration, and much more.

An overview of Velox

Velox is a unified execution engine that is built and open-sourced by Meta, aimed at accelerating data management systems and streamlining their development. One of the biggest benefits of Velox is that it consolidates and unifies data management systems so you don’t need to keep rewriting the engine. Today Velox is in various stages of integration with several data systems including Presto (Prestissimo), Spark (Gluten), PyTorch (TorchArrow), and Apache Arrow.

Velox at IBM

Presto is the engine for watsonx.data, IBM’s open data lakehouse platform. Over the last year, we’ve been working hard on advancing Velox for Presto – Prestissimo – at IBM. Presto Java workers are being replaced by a C++ process based on Velox. We now have several committers to the Prestissimo project and continue to partner closely with Meta as we work on building Presto 2.0.

Some of the key benefits of Prestissimo include:

• Hugh performance boost: query processing can be done with much smaller clusters
• No performance cliffs: no Java processes, JVM, or garbage collections, as memory arbitration improves efficiency
• Easier to build and operate at scale: Velox gives you reusable and extensible primitives across data engines (like Spark)

This year, we plan to do even more with Prestissimo including:

• The Iceberg reader
• Production readiness (metrics collection with Prometheus)
• New Velox system implementation
• TPC-DS benchmark runs

VeloxCon 2024

We worked closely with Meta to organize VeloxCon 2024, and it was a fantastic community event. We heard speakers from Meta, IBM, Pinterest, Intel, Microsoft, and others share what they’re working on and their vision for Velox over two dynamic days.

Day 1 highlights

The conference kicked off with sessions from Meta including Amit Purohit reaffirming Meta’s commitment to open source and community collaboration. Pedro Pedreira, alongside Manos Karpathiotakis and Deblina Gupta, delved into the concept of composability in data management, showcasing Velox’s versatility and its alignment with Arrow.

Amit Dutta of Meta explored Prestissimo’s batch efficiency at Meta, shedding light on the advancements made in optimizing data processing workflows. Remus Lazar, VP Data & AI Software at IBM presented Velox’s journey within IBM and vision for its future. Aditi Pandit of IBM followed with insights into Prestissimo’s integration at IBM, highlighting feature enhancements and future plans.

The afternoon sessions were equally insightful, with Jimmy Lu of Meta unveiling the latest optimizations and features in Velox. While Binwei Yang of Intel discussed the integration of Velox with the Apache Gluten project, emphasizing its global impact. Engineers from Pinterest and Microsoft shared their experiences of unlocking data query performance by using Velox and Gluten, showcasing tangible performance gains.

The day concluded with sessions from Meta on Velox’s memory management by Xiaoxuan Meng and a glimpse into the new simple aggregation function interface that was presented by Wei He.

Day 2 highlights

The second day began with a keynote from Orri Erling, co-creator of Velox. He shared insights into Velox Wave and Accelerators, showcasing its potential for acceleration. Krishna Maheshwari from NeuroBlade highlighted their collaboration with the Velox community, introducing NeuroBlade’s SPU (SQL Processing Unit) and its transformative impact on Velox’s computational speed and efficiency.

Sergei Lewis from Rivos explored the potential of offloading work to accelerators to enhance Velox’s pipeline performance. William Malpica and Amin Aramoon from Voltron Data introduced Theseus, a composable, scalable, distributed data analytics engine, using Velox as a CPU backend.

Yoav Helfman from Meta unveiled Nimble, a cutting-edge columnar file format that is designed to enhance data storage and retrieval. Pedro Pedreira and Sridhar Anumandla from Meta elaborated on Velox’s new technical governance model, emphasizing its importance in guiding the project’s development sustainability.

The day also featured sessions on Velox’s I/O optimizations by Deepak Majeti from IBM, strategies for safeguarding against Out-Of-Memory (OOM) kills by Vikram Joshi from ComputeAI, and a hands-on demo on debugging Velox applications by Deepak Majeti.

What’s next with Velox

VeloxCon 2024 was a testament to the vibrant ecosystem surrounding the Velox project, showcasing groundbreaking innovations and fostering collaboration among industry leaders and developers alike. The conference provided attendees with valuable insights, practical knowledge, and networking opportunities, solidifying Velox’s position as a leading open source project in the data management ecosystem.

Source: ibm.com

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Bigger isn’t always better: How hybrid AI pattern enables smaller language models

As large language models (LLMs) have entered the common vernacular, people have discovered how to use apps that access them. Modern AI tools can generate, create, summarize, translate, classify and even converse. Tools in the generative AI domain allow us to generate responses to prompts after learning from existing artifacts.

One area that has not seen much innovation is at the far edge and on constrained devices. We see some versions of AI apps running locally on mobile devices with embedded language translation features, but we haven’t reached the point where LLMs generate value outside of cloud providers.

However, there are smaller models that have the potential to innovate gen AI capabilities on mobile devices. Let’s examine these solutions from the perspective of a hybrid AI model.

The basics of LLMs

LLMs are a special class of AI models powering this new paradigm. Natural language processing (NLP) enables this capability. To train LLMs, developers use massive amounts of data from various sources, including the internet. The billions of parameters processed make them so large.

While LLMs are knowledgeable about a wide range of topics, they are limited solely to the data on which they were trained. This means they are not always “current” or accurate. Because of their size, LLMs are typically hosted in the cloud, which require beefy hardware deployments with lots of GPUs.

This means that enterprises looking to mine information from their private or proprietary business data cannot use LLMs out of the box. To answer specific questions, generate summaries or create briefs, they must include their data with public LLMs or create their own models. The way to append one’s own data to the LLM is known as retrieval augmentation generation, or the RAG pattern. It is a gen AI design pattern that adds external data to the LLM.

Is smaller better?

Enterprises that operate in specialized domains, like telcos or healthcare or oil and gas companies, have a laser focus. While they can and do benefit from typical gen AI scenarios and use cases, they would be better served with smaller models.

In the case of telcos, for example, some of the common use cases are AI assistants in contact centers, personalized offers in service delivery and AI-powered chatbots for enhanced customer experience. Use cases that help telcos improve the performance of their network, increase spectral efficiency in 5G networks or help them determine specific bottlenecks in their network are best served by the enterprise’s own data (as opposed to a public LLM).

That brings us to the notion that smaller is better. There are now Small Language Models (SLMs) that are “smaller” in size compared to LLMs. SLMs are trained on 10s of billions of parameters, while LLMs are trained on 100s of billions of parameters. More importantly, SLMs are trained on data pertaining to a specific domain. They might not have broad contextual information, but they perform very well in their chosen domain. 

Because of their smaller size, these models can be hosted in an enterprise’s data center instead of the cloud. SLMs might even run on a single GPU chip at scale, saving thousands of dollars in annual computing costs. However, the delineation between what can only be run in a cloud or in an enterprise data center becomes less clear with advancements in chip design.

Whether it is because of cost, data privacy or data sovereignty, enterprises might want to run these SLMs in their data centers. Most enterprises do not like sending their data to the cloud. Another key reason is performance. Gen AI at the edge performs the computation and inferencing as close to the data as possible, making it faster and more secure than through a cloud provider.

It is worth noting that SLMs require less computational power and are ideal for deployment in resource-constrained environments and even on mobile devices.

An on-premises example might be an IBM Cloud® Satellite location, which has a secure high-speed connection to IBM Cloud hosting the LLMs. Telcos could host these SLMs at their base stations and offer this option to their clients as well. It is all a matter of optimizing the use of GPUs, as the distance that data must travel is decreased, resulting in improved bandwidth.

How small can you go?

Back to the original question of being able to run these models on a mobile device. The mobile device might be a high-end phone, an automobile or even a robot. Device manufacturers have discovered that significant bandwidth is required to run LLMs. Tiny LLMs are smaller-size models that can be run locally on mobile phones and medical devices.

Developers use techniques like low-rank adaptation to create these models. They enable users to fine-tune the models to unique requirements while keeping the number of trainable parameters relatively low. In fact, there is even a TinyLlama project on GitHub.  

Chip manufacturers are developing chips that can run a trimmed down version of LLMs through image diffusion and knowledge distillation. System-on-chip (SOC) and neuro-processing units (NPUs) assist edge devices in running gen AI tasks.

While some of these concepts are not yet in production,  solution architects should consider what is possible today. SLMs working and collaborating with LLMs may be a viable solution. Enterprises can decide to use existing smaller specialized AI models for their industry or create their own to provide a personalized customer experience.

Is hybrid AI the answer?

While running SLMs on-premises seems practical and tiny LLMs on mobile edge devices are enticing, what if the model requires a larger corpus of data to respond to some prompts? 

Hybrid cloud computing offers the best of both worlds. Might the same be applied to AI models? The image below shows this concept.

When smaller models fall short, the hybrid AI model could provide the option to access LLM in the public cloud. It makes sense to enable such technology. This would allow enterprises to keep their data secure within their premises by using domain-specific SLMs, and they could access LLMs in the public cloud when needed. As mobile devices with SOC become more capable, this seems like a more efficient way to distribute generative AI workloads.

IBM® recently announced the availability of the open source Mistral AI Model on their watson™ platform. This compact LLM requires less resources to run, but it is just as effective and has better performance compared to traditional LLMs. IBM also released a Granite 7B model as part of its highly curated, trustworthy family of foundation models.

It is our contention that enterprises should focus on building small, domain-specific models with internal enterprise data to differentiate their core competency and use insights from their data (rather than venturing to build their own generic LLMs, which they can easily access from multiple providers).

Bigger is not always better

Telcos are a prime example of an enterprise that would benefit from adopting this hybrid AI model. They have a unique role, as they can be both consumers and providers. Similar scenarios may be applicable to healthcare, oil rigs, logistics companies and other industries. Are the telcos prepared to make good use of gen AI? We know they have a lot of data, but do they have a time-series model that fits the data?

When it comes to AI models, IBM has a multimodel strategy to accommodate each unique use case. Bigger is not always better, as specialized models outperform general-purpose models with lower infrastructure requirements. 

Source: ibm.com

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5 steps for implementing change management in your organization

Change is inevitable in an organization; especially in the age of digital transformation and emerging technologies, businesses and employees need to adapt. Change management (CM) is a methodology that ensures both leaders and employees are equipped and supported when implementing changes to an organization.

The goal of a change management plan, or more accurately an organizational change plan, is to embed processes that have stakeholder buy-in and support the success of both the business and the people involved. In practice, the most important aspect of organizational change is stakeholder alignment. This blog outlines five steps to support the seamless integration of organizational change management.

Steps to support organizational change management

1. Determine your audience

Who is impacted by the proposed change? It is crucial to determine the audience for your change management process.

Start by identifying key leaders­ and determine both their influence and involvement in the history of organizational change. Your key leaders can provide helpful context and influence employee buy-in. You want to interview leaders to better understand ‘why’ the change is being implemented in the first place. Ask questions such as:

◉ What are the benefits of this change?

◉ What are the reasons for this change?

◉ What does the history of change in the organization look like?

Next, identify the other groups impacted by change, otherwise known as the personas. Personas are the drivers of successful implementation of a change management strategy. It is important to understand what the current day-to-day looks like for the persona, and then what tomorrow will look like once change is implemented.

A good example of change that an organization might implement is a new technology, like generative AI (Gen AI). Businesses are implementing this technology to augment work and make their processes more efficient. Throughout this blog, we use this example to better explain each step of implementing change management.

Who is impacted by the implementation of gen AI? The key leaders might be the vice president of the department that is adding the technology, along with a Chief Technical Officer, and team managers. The personas are those whose work is being augmented by the technology.

2. Align the key stakeholders

What are the messages that we will deliver to the personas? When key leaders come together to determine champion roles and behaviors for instituting change, it is important to remember that everyone will have a different perspective.

To best align leadership, take an iterative approach. Through a stakeholder alignment session, teams can co-create with key leaders, change management professionals, and personas to best determine a change management strategy that will support the business and employees.

Think back to the example of gen AI as the change implemented in the organization. Proper alignment of stakeholders would be bringing together the executives deciding to implement the technology, the technical experts on gen AI, the team managers implementing gen AI into their workflows, and even trusted personas—the personas might have experienced past changes in the organization.

3. Define the initiatives and scope

Why are you implementing the change? What are the main drivers of change? How large is the change to the current structure of the organization? Without a clear vision for change initiatives, there will be even more confusion from stakeholders. The scope of change should be easily communicated; it needs to make sense to your personas to earn their buy-in.

Generative AI augments workflows, making businesses more efficient. However, one obstacle of this technology is the psychological aspect that it takes power away from individuals who are running the administrative tasks. Clearly defining the benefits of gen AI and the goals of implementing the technology can help employees better understand the need.

Along with clear initiatives and communication, including a plan to skill employees to understand and use the technology as part of their scope also helps promote buy-in. Drive home the point that the change team members, through the stakeholders, become evangelists pioneering a new way of working. Show your personas how to prompt the tool, apply the technology, and other use cases to grow their excitement and support of the change.

4. Implement the change management plan

After much preparation on understanding the personas, aligning the stakeholders and defining the scope, it is time to run. ‘Go live’ with the change management plan and remember to be patient with employees and have clear communication. How are employees handling the process? Are there more resources needed? This is the part where you highly consider the feedback that is given and assess if it helps achieve the shared goals of the organization.

Implementing any new technology invites the potential for bugs, lags or errors in usage. For our example with gen AI, a good implementation practice might be piloting the technology with a small team of expert users, who underwent training on the tool. After collecting feedback from their ‘go live’ date, the change management team can continue to phase the technology implementation across the organization. Remember to be mindful of employee feedback and keep an open line of communication.

5. Adapt to improve

Adapting the process is something that can be done throughout any stage of implementation but allocating time to analyze the Return on Investment (ROI) should be done at the ‘go live’ date of change. Reviewing can be run via the “sense and respond” approach.

Sense how the personas are reacting to said change. This can be done via sentiment analysis, surveys and information sessions. Then, analyze the data. Finally, based on the analysis, appropriately respond to the persona’s reaction.

Depending on how the business and personas are responding to change, determine whether the outlined vision and benefits of the change are being achieved. If not, identify the gaps and troubleshoot how to better support where you might be missing the mark. It is important to both communicate with the stakeholders and listen to the feedback from the personas.

To close out our example, gen AI is a tool that thrives on continuous usage and practices like fine-tuning. The organization can both measure the growth and success of the technology implemented, as well as the efficiency of the personas that have adapted the tool into their workflows. Leaders can share out surveys to pressure test how the change is resonating. Any roadblocks, pain points or concerns should be responded to directly by the change management team, to continue to ensure a smooth implementation of gen AI.

How to ensure success when implementing organizational change

The success formula to implementing organizational change management includes the next generation of leadership, an accelerator culture that is adaptive to change, and a workforce that is both inspired and engaged.

Understanding the people involved in the process is important to prepare for a successful approach to change management. Everyone comes to the table with their own view of how to implement change. It is important to remain aligned on why the change is happening. The people are the drivers of change. Keep clear, open and consistent communication with your stakeholders and empathize with your personas to ensure that the change will resonate with their needs.

As you craft your change management plan, remember that change does not stop at the implementation date of the plan. It is crucial to continue to sense and respond.

Source: ibm.com

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Deployable architecture on IBM Cloud: Simplifying system deployment

Deployable architecture (DA) refers to a specific design pattern or approach that allows an application or system to be easily deployed and managed across various environments. A deployable architecture involves components, modules and dependencies in a way that allows for seamless deployment and makes it easy for developers and operations teams to quickly deploy new features and updates to the system, without requiring extensive manual intervention.

There are several key characteristics of a deployable architecture, which include:

1. Automation: Deployable architecture often relies on automation tools and processes to manage deployment process. This can involve using tools like continuous integration or continuous deployment (CI/CD) pipelines, configuration management tools and others.
2. Scalability: The architecture is designed to scale horizontally or vertically to accommodate changes in workload or user demand without requiring significant changes to the underlying infrastructure.
3. Modularity: Deployable architecture follows a modular design pattern, where different components or services are isolated and can be developed, tested and deployed independently. This allows for easier management and reduces the risk of dependencies causing deployment issues.
4. Resilience: Deployable architecture is designed to be resilient, with built-in redundancy and failover mechanisms that ensure the system remains available even in the event of a failure or outage.
5. Portability: Deployable architecture is designed to be portable across different cloud environments or deployment platforms, making it easy to move the system from one environment to another as needed.
6. Customisable: Deployable architecture is designed to be customisable and can be configured according to the need. This helps in deployment in diverse environments with varying requirements.
7. Monitoring and logging: Robust monitoring and logging capabilities are built into the architecture to provide visibility into the system’s behaviour and performance.
8. Secure and compliant: Deployable architectures on IBM Cloud® are secure and compliant by default for hosting your regulated workloads in the cloud. It follows security standards and guidelines, such as IBM Cloud for Financial Services® , SOC Type 2, that ensures the highest levels of security and compliance requirements.

Overall, deployable architecture aims to make it easier for organizations to achieve faster, more reliable deployments, while also  making sure that the underlying infrastructure is scalable and resilient.

Deployable architectures on IBM Cloud

Deploying an enterprise workload with a few clicks can be challenging due to various factors such as the complexity of the architecture and the specific tools and technologies used for deployment. Creating a secure, compliant and tailored application infrastructure is often more challenging and requires expertise. However, with careful planning and appropriate resources, it is feasible to automate most aspects of the deployment process.  IBM Cloud provides you with well-architected patterns that are secure by default for regulated industries like financial services. Sometimes these patterns can be consumed as-is or you can add on more resources to these as per the requirements. Check out the deployable architectures that are available in the IBM Cloud catalog.

Deployment strategies for deployable architecture

Deployable architectures provided on IBM Cloud can be deployed in multiple ways, using IBM Cloud projects, Schematics, directly via CLI or you can even download the code and deploy on your own.

Use-cases of deployable architecture

Deployable architecture is commonly used in industries such as finance, healthcare, retail, manufacturing and government, where compliance, security and scalability are critical factors. Deployable architecture can be utilized by a wide range of stakeholders, including:

1. Software developers, IT professionals, system administrators and business stakeholders who need to ensure that their systems and applications are deployed efficiently, securely and cost-effectively. It helps in reducing time to market, minimizing manual intervention and decreasing deployment-related errors.
2. Cloud service providers, managed service providers and infrastructure as a service (IaaS) providers to offer their clients a streamlined, reliable and automated deployment process for their applications and services.
3. ISVs and enterprises to enhance the deployment experience for their customers, providing them with easy-to-install, customizable and scalable software solutions that helps driving business value and competitive advantage.

Source: ibm.com

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