How Broadcom Delivers Unparalleled Advantages With Automation.ai

How Broadcom Delivers Unparalleled Advantages With Automation.ai

To stay ahead of their organization’s rapidly evolving technological environments, business requirements, and security threats, teams need new AI and machine learning technologies that augment and automate decision making. Digital BizOps from Broadcom addresses these emerging requirements by integrating business, development, and operations data to generate actionable insights. Powered by Automation.ai, the industry’s first AI-driven software intelligence platform, Digital BizOps can enable teams to continuously improve decision making and the execution of digital initiatives, therefore yielding enhanced business outcomes.

In our three part blog series, we are taking an in-depth look at the machine learning architecture of Automation.ai. In our first post, we examined why artificial intelligence (AI) and machine learning are becoming such critical imperatives for today’s enterprises. In our second post, we offered a detailed look at the architectural approaches Broadcom has taken in developing Automation.ai. In this final post, we examine the key advantages the platform provides.

The Advantages of Automation.ai

Through employing a range of patented and differentiated capabilities and features, the Automation.ai platform provides a wide range of advantages to customers:

Maximizing data utility

With Automation.ai, teams can combine data from all products into one model, so algorithms can be used to evaluate everything—maximizing the value of data captured and aggregated.

Offering contextualized, correlated intelligence

With isolated, domain-specific visibility, efforts like troubleshooting can demand a lot of time, effort, and expertise. Automation.ai streamlines these efforts by providing the power of multi-layer visibility and correlation. With the platform, teams can unify intelligence from different domains. The platform can incorporate different ontologies to create a uniform playing field for data that algorithms can run on.

Facilitating effective analysis

By describing the realities of an environment with a unified data model, the platform enables teams to have robots take on a range of analysis use cases. With these capabilities, Automation.ai enables teams to conduct analysis that can be very broad in scope and also very precise.

Powering intelligent automation

The platform’s combination of a unified data model and applied expertise enable teams to establish intelligent automation that offloads a range of tasks from human analysts.

Delivering current, complete, and accurate model of the run-time environment

As environments continue to grow more complex and dynamic, gaining a clear, accurate picture of the operating environment only gets more difficult and time consuming. Automation.ai  enables teams to establish—and maintain—a current, complete, and accurate picture of the run-time environment. As a result, teams don’t have to rely on old diagrams, or keep manually creating models that quickly become out of date.

Conclusion

Digital BizOps represents a modern solution that uniquely empowers business, IT, and development teams to meet their ever-intensifying demands. Digital BizOps is powered by Automation.ai, a software intelligence platform that provides an unparalleled array of capabilities. Through its differentiated architecture, Automation.ai enables teams to gain a unified view of their dynamic business and IT landscape, and leverage the automation and insights needed to realize optimized decision making, operational execution, and business outcomes.

To learn more, be sure to read our white paper, How Automation.ai Delivers Scalable, Powerful, and Agile Machine Learning.

How Broadcom Has Made Machine Learning Both Pragmatic and Powerful

How Broadcom Has Made Machine Learning Both Pragmatic and Powerful

To contend with the growing changes in the technology landscape, companies have to make radical improvements in their business insights and operations. This means companies need to adopt solutions with artificial intelligence (AI) and machine learning capabilities to gain new levels of speed, intelligence, and sophistication.

In our last post, we examined why artificial intelligence (AI) and machine learning are becoming critical for today’s enterprises. We also introduced Digital BizOps from Broadcom, an innovative solution that addresses the growing demands that businesses face. Powered by Automation.ai, the industry’s first AI-driven software intelligence platform, Digital BizOps enables teams to continuously improve decision making and the execution of digital initiatives.  In this post, we offer a detailed look at the architectural approaches Broadcom has taken in developing Automation.ai.

How We Do It: The Automation.ai Architecture

In developing Automation.ai, we set out to create a modern platform for organizations, one that provided a comprehensive model for business, IT operations, and development. Instead of trying to rearchitect our existing tools, we chose to establish an intelligence layer that could sit on top of a number of specific solutions. We also set out to make it possible to access data from across a number of silos, while preserving the context of the source content and enabling this context to be shared efficiently. This contextual, comprehensive visibility is vital in delivering the actionable insights today’s decision makers, developers, and operations teams need.

In the following sections, we offer a detailed look at some of the key approaches we’ve taken in developing Automation.ai.

Building a knowledge graph to optimize algorithm usage

Often, when it comes to deriving value from machine learning, it isn’t the algorithms themselves that matter. Typically, what matters is the way these algorithms are orchestrated and scoped. To be able to use algorithms effectively, we set out to maintain a multi-domain knowledge graph that describes the enterprise in great detail. Once this detailed information is available, our platform decides what machine learning technique to apply, and which specific data set to apply it to. In addition, the platform evolves dynamically as the organization and environment change.

In effect, we sought to match the situational awareness of a real human analyst so that our platform can pick and apply the right analysis technique to the right data set, dynamically, based on what it discovers about the environment. This approach means that any specific analysis we apply can change and adapt to the enterprise as it evolves. This is very different than the rule-based expert systems of the past, which were simply too brittle for today’s dynamic enterprises.

Harnessing domain expertise

Domain experts can leverage data effectively because they know what questions to ask. To harness this knowledge, we are working with domain experts and studying how users interact with our products and other vendors’ tools. So far, we’ve interviewed hundreds of experts about the type of analysis they do under recurring situations. We asked them what signals matter for them while testing their hypothesis about a situation, including what patterns they scan, what correlations they seek, and so on. We then captured these heuristics with machine learning robots so that our system can leverage this expertise and apply it correctly to the right scenarios, technologies, and problems.

Constraining problem scope and employing small, reusable components

Powerful machine learning techniques are often costly to run, and their efficacy typically increases with input curation. Consequently, to be most effective, machine learning has to be employed within the right guardrails. It is important to constrain the scope of the problem you’re trying to solve.

Automation.ai is built based on an approach in which we narrowly define tasks and use small, reusable components to build robots. Through this approach, we can make robots fast and efficient to run, and extend their utility. We’ve created one robot that has the sole responsibility of detecting incidents, for example, while another will handle incident response. Each of these robots can be managed independently; they don’t need to be run on the same computer or built by the same team. Further, these robots can be enhanced and optimized on independent schedules, according to evolving priorities.

Employing ontological abstractions to establish a product-agnostic, software intelligence platform

In developing the machine learning architecture for Automation.ai, we’ve focused on establishing capabilities around ontologies, rather than being tied to specific products. For example, in the area of application performance management (APM), we didn’t focus on developing around any specific product, which can have distinct collection methods, terminology, and so on. Instead, we focused on the common, industry-accepted ontology that all APM solutions share. Consequently, our architecture can work for all APM solutions, including those from Broadcom as well as third parties.

At the same time, it’s important to recognize the fact that ontologies vary across domains. For example, while an infrastructure monitoring ontology will be concerned with elements like routers and switches, a DevOps ontology will be focused on testing and production rules. That’s why we’ve built our architecture to accommodate different ontologies, including those for APM, infrastructure, networks, DevOps, security, and more. Most importantly, the platform can incorporate and integrate the intelligence from all these different domains.

Developing an open, flexible architecture

In the market today, many topology approaches are closed in nature, bound by specific technological approaches and linear models. By contrast, Automation.ai employs an open, source-agnostic approach. The platform’s architecture is flexible in several key ways:

  • Data source extensibility. The platform’s architecture is not bound by any specific product, but features an open data lake, algorithms, and more. Customers can easily accommodate new data sources, including those from multiple Broadcom solutions as well as solutions from a wide range of third-party vendors.
  • Architecture extensibility. With Automation.ai, Broadcom, partners, and customers can introduce entirely new ontologies, without having to make any architectural changes.
  • Ontology extensibility. Teams can add different properties onto existing ontologies, and so easily accommodate organization-specific information, including tribal knowledge, naming or classification approaches, and so on.
  • Robot extensibility. The Automation.ai architecture can efficiently accommodate new robots as needed, while at the same time, enabling each robot to be employed against a unified, consistent data set.

In addition, by employing Automation.ai’s documented, public APIs, customers can use external machine learning tools to access all the platform’s correlated data, knowledge graphs, and more.

Employing an intelligent, flexible, scalable data model

In developing the data model for Automation.ai, we’ve employed a patented, property-graph based approach that has history awareness. This approach is structured based on entities, relationships, and their properties, and is journaled over time. These time-stamped records represent an immutable data point, and a valuable way to establish incremental observation of an environment.

With property graphs, complex relational lookups can be done instantaneously. As a result, they provide an excellent structure for doing ontological inference. By comparison, using a traditional relational database management system (RDBMS) for this model would require an impractical amount of join queries between schemas and tables, introducing an unacceptable level of performance-degrading latency.

Conclusion

Through the design principles outlined above, the Automation.ai machine learning model can provide users with an unparalleled mix of characteristics. The platform equips customers with the ability to gain value immediately, and to leverage the flexibility they need to gain maximum benefits over the long term. To learn more, be sure to read our white paper, How Automation.ai Delivers Scalable, Powerful, and Agile Machine Learning.

 

Why Machine Learning Has to Move from the Lab to the Production Floor

Why Machine Learning Has to Move from the Lab to the Production Floor

The IT landscape has changed tremendously in the past decade. As it continues to evolve, business, IT and development teams face unprecedented demands, and in response, these teams have to gain new levels of speed, intelligence, and sophistication.  To meet these new mandates, Broadcom offers innovative solutions that harness artificial intelligence (AI) and machine learning, enabling teams to continuously improve decision making and the execution of digital initiatives.  In our three-part blog series, we examine the Broadcom approach to building a machine learning architecture and the unique advantages the Broadcom solution provides. In this first post, we start by examining why machine learning has emerged as an imperative for enterprises.

Evolving IT Landscape and Operations

In recent years, the IT landscape has seen massive transformation, and the rate of change only continues to accelerate. In the not-too-distant past, monolithic applications and static infrastructures were the norm. Today, applications, and the way they’re developed, delivered, monitored, and supported looks very different. The following are just a few aspects of this change:

  • Proliferating technologies. Environments are now composed of a diverse mix of virtualization technologies, clouds, containers, microservices, orchestration systems, and more. Environments continue to grow increasingly dynamic and ephemeral. Increasing layers and types of security technologies continue to be implemented as well.
  • Expanding toolsets. It isn’t just that the volume and complexity of technologies has been increasing. These evolving environments accelerate the velocity of change and require continuous analysis. As a result, the number of tools adopted to manage these complex environments has also continued to expand. Now, it’s not uncommon for IT organizations to be employing hundreds of tools for managing IT operations, security, and development.
  • Changing approaches. Approaches like DevOps, BizOps, site reliability engineering (SRE), and continuous integration/continuous delivery (CI/CD) are emerging as norms, which all serve to accelerate the rate of change. These emerging paradigms place fundamentally different demands on team members, requiring staff to move from being specialists with a focus on specific tools and technologies to generalists who are dedicated to managing services.
  • Escalating demands. For today’s enterprises, the quality and performance of digital services is increasingly intertwined with business performance. It is paramount to bring innovative, compelling digital services to market faster. Teams need to maximize service levels and security at all times. At the same time, wringing maximum productivity out of staff and technology investments continues to grow increasingly critical.

All these factors are necessitating a fundamental change in the way business, IT, and development organizations work. These organizations simply can’t continue to rely on legacy models and approaches to meet their charters.

Introducing Digital BizOps from Broadcom, Powered by Automation.ai

To keep in front of their organization’s rapidly evolving technological environments, business requirements, and security threats, teams need new AI and machine learning technologies that augment and automate decision making. Broadcom is focused on delivering advanced solutions that address these emerging requirements.

Digital BizOps from Broadcom integrates business, development, and operations data to generate actionable insights. Powered by Automation.ai, this solution enables teams to establish continuous improvement in the business outcomes of digital initiatives.

Automation.ai is the industry’s first AI-driven software intelligence platform. Automation.ai harnesses the power of advanced AI, machine learning, intelligent automation and open-source frameworks to transform massive volumes of enterprise data into actionable insights.

To learn more, be sure to read our white paper, How Automation.ai Delivers Scalable, Powerful, and Agile Machine Learning