Business AI Platform For Commercial Development

Business models that demand significant technological agility and fuel uncertainty about what the enterprise may need in the not-so-distant future. Tackling this complexity may feel akin to building an airplane while in flight.
 
How do you decide where to invest when you aren’t sure what you need, what solutions will meet those needs and what resources you’ll need to deliver them? The answer lies in developing an enterprise AI platform.
 
Enterprise AI Platform
An enterprise AI platform is a framework for accelerating the full life cycle of enterprise AI projects at scale. It gives organisations a structured yet flexible way to create AI-driven solutions today and over the long term. It also enables AI services to scale from proofs of concept to production-scale systems. It does so by incorporating specific guidelines drawn from the world of service-oriented and event-driven architectures. 
 
When designed well, an enterprise AI platform facilitates faster, more efficient and more effective collaboration among AI scientists and engineers. 
 
It helps contain costs in a variety of ways, avoiding duplication of effort, automating low-value tasks and improving reproducibility and reusability of all work. It also eliminates some costly activities, namely, copying and extracting data and managing data quality. 
 
What’s more, an enterprise AI platform can help in tackling skills gaps. It not only serves as a focal point for onboarding new talent, but also helps in developing and supporting best practices throughout a team of AI scientists and machine learning engineers. And, it can aid in ensuring that work is distributed more evenly and completed more quickly. 
 
Within an enterprise AI platform, the elements are organized into five logical layers. 
 
The Data & Integration Layer gives access to the enterprise data. This data access is critical because in AI, developers are not hand-coding the rules. Rather, the machine is learning the rules based on the data it has access to. Data components also include data transformation and governance elements to help manage data repositories and sources. 
Data sources may be wrapped in services that make it possible to interact with the data at an abstract level, providing a single reference point to an existing platform data ontology. Most importantly, the data must be of good quality, and the AI scientists must be able to build the data pipelines they need without dependence on the IT team, ideally through easy self-service, so they can experiment as needed.
 
The Experimentation Layer is where AI scientists develop, test and iterate their hypotheses. A good experimentation layer brings automation for feature engineering, feature selection, model selection, model optimization and model interpretability. Idea management and model management are both key to empowering AI scientists to collaborate and avoid repetition.
 
The Operations & Deployment Layer is important for model governance and deployment. This is where the model risk assessment is conducted so that the model governance team or model risk office can validate and see proof-of-model interpretability, model bias and fairness, and model fail-safe mechanisms.  The operations layer includes the results of experiments by AI DevOps engineers and system administrators. It offers tools and mechanisms to manage the “containerised” deployment of various models and other components across the platform. It also enables the monitoring of model performance accuracy. 
 
The Intelligence Layer powers AI during run time, with training-time activities handled in the experimentation layer. It is the product of technical solutions and product teams working in conjunction with cognitive experience experts. 
The intelligence layer can expose both re-useable components, such as low-level service APIs, to intelligent products that are composite orchestrations of many low-level APIs. 
 
Central to the orchestration and delivery of intelligent services, the intelligence layer is the primary resource for directing service delivery and can be implemented as simply as a fixed relay from requests to responses. Ideally, however, it is implemented using such concepts as dynamic service discovery and intent identification to provide a flexible response platform that enables cognitive interaction despite ambiguous directions. 
 
The Experience Layer engages with users through technologies such as conversational UI, augmented reality and gesture control. AI platforms are a growing area encompassing the components that enable the visual and conversational design work for a solution.  It is generally owned by a cognitive experience team with traditional user experience workers, conversational experience workers, visual designers and other creative individuals who craft rich and meaningful experiences enabled by AI technology.
 
Below we examine the operations layer in greater depth to discuss the governance and deployment of AI.
 
In the Operations layer: Managing ideas, models and configuration
As an organisation explores opportunities to use AI, it needs a formal approach for keeping track of those ideas: testing the possibilities, capturing what works and maintaining an “idea graveyard” for concepts that have been tested and determined to be untenable. That might sound simple enough, but the potential quantity of ideas, and nuances among them, can quickly become overwhelming. To avoid that complexity, firms should design and implement an automated idea management process for tracking and managing the lifecycle of ideas and experimentation. 
 
Doing so helps in tracking idea performance and ensuring the quality of ideas. There are also efficiencies to be gained by providing team-wide visibility to successful ideas and managing duplicate works and potential conflicts.
 
A similar approach can be applied to managing models. Building real-world machine-learning algorithms is complex and highly iterative. An AI scientist may build tens or even hundreds of models before arriving at one that meets some acceptance criteria. Now, imagine being that AI scientist without a formal process or tool for managing those work products. 
 
A formal process for model management will alleviate that pain for the individuals and the organisation. It makes it possible for AI scientists to track their work in detail, giving them a record of their experiments.  Such a process also enables them to capture important insights along the way, from how normalisation affected results to how granular features appear to affect performance for a certain subset of data. 
 
Across an organization, sound model management empowers data scientists to review, revise and build on each other’s work, helping accelerate progress and avoid wasted time. It also enables the organization to conduct meta-analyses across models to answer broader questions (e.g., “What hyper-parameter settings work best for these features?”). 
 
To succeed at an enterprise scale, an organization must be able to store, track, version and index models as well as data pipelines. Traditional model management should be expanded to include configuration management. Logging each model, its parameters and data pipelines enables models to be queried, reproduced, analyzed and shared. 
 
Consider, for example, that model management will track hyper-parameters that have been tested and record what was eventually used for deployment. However, model management will not simultaneously test what features were tested and discarded, what modifications were made to data pipelines or what compute resources were made available to support sufficient training, to name just a few key activities.
 
Together with model management data, tracking that kind of configuration information can accelerate the deployment of AI services while reducing duplicate work. An organisation will never achieve that level of visibility and analysis when managing models via spreadsheets.
 
Make Knowledge a Service
An enterprise AI platform paves the way for an organization to deliver intelligent services and products, empowering not just AI scientists but all workers and customers to tap into the tool or combination of tools they need.
Untethered to any one AI type or solution, it enables the rapid provisioning of a high-performance environment to support virtually any kind of AI. In short, it transforms AI from a series of finite point solutions to an enterprise capability that can be continuously improved as it is tailored and deployed to meet business goals over time.
 
As AI becomes a mainstay of every data-driven organisation, it must be managed strategically, with a focus on agility and extensibility. The most successful organisations will be those that take the time to build their enterprise AI platform for the business. 
 
With this approach, they can deliver more value more quickly, not just today, but also as new opportunities take shape in the future.  With an enterprise AI platform, rather than a patchwork of standalone tools, an enterprise will be well positioned for advancements in AI use cases and other emerging and supporting technologies.
 
Information Managment
 
You Might Also Read: 
 
Getting The Most From Investing In AI:
 
How AI & Machine Learning Can Revolutionise eCommerce:
 
« How Computer Data Helped Investigate Quebec Shooter
British Universities Have Many Cyber Threats »

ManageEngine
CyberSecurity Jobsite
Check Point

Directory of Suppliers

MIRACL

MIRACL

MIRACL provides the world’s only single step Multi-Factor Authentication (MFA) which can replace passwords on 100% of mobiles, desktops or even Smart TVs.

Authentic8

Authentic8

Authentic8 transforms how organizations secure and control the use of the web with Silo, its patented cloud browser.

Practice Labs

Practice Labs

Practice Labs is an IT competency hub, where live-lab environments give access to real equipment for hands-on practice of essential cybersecurity skills.

IT Governance

IT Governance

IT Governance is a leading global provider of information security solutions. Download our free guide and find out how ISO 27001 can help protect your organisation's information.

The PC Support Group

The PC Support Group

A partnership with The PC Support Group delivers improved productivity, reduced costs and protects your business through exceptional IT, telecoms and cybersecurity services.

Black Duck Software

Black Duck Software

Black Duck Hub allows organizations to manage open source code security as well as license compliance risks.

Cyber Risk Agency

Cyber Risk Agency

Cyber Risk Agency is a cybersecurity consulting firm specializing in managing cyber risks for SMEs.

Verve Industrial

Verve Industrial

Verve specialize in providing software and services to help protect and secure critical industrial control systems.

ZyberSafe

ZyberSafe

ZyberSafe is an innovative Danish company specialized within building hardware encryption solutions.

Approach

Approach

Approach is a leading provider of cyber security consulting and secure application development services in Belgium.

Kratikal

Kratikal

Kratikal provides a complete suite of manual and automated security testing services.

SimSpace

SimSpace

SimSpace is the visionary yet practical platform for measuring how your security system responds under actual, sustained attack.

Iterasec

Iterasec

Iterasec provides a full range of security services to hacker-proof your products and make software engineering process secure by design.

eaziSecurity

eaziSecurity

eaziSecurity has built an eco-system of technology and services that bring enterprise scale security solutions to the SME marketplace.

SilverEdge Government Solutions

SilverEdge Government Solutions

SilverEdge is a next generation provider of innovative and proprietary cybersecurity, software, and intelligence solutions for the Defense and Intelligence Communities.

ThreatNix

ThreatNix

ThreatNix is a tight knit group of experienced security professionals who are committed to providing competent cybersecurity solutions that adhere to international standards.

Ampcus Cyber

Ampcus Cyber

Ampcus Cyber specialize in providing comprehensive security solutions and services that are tailored to safeguard our clients' networks, infrastructure, and valuable assets.

Proaxiom

Proaxiom

Proaxiom are focused on erasing cyber driven panic paralysis for Small and Medium Enterprises through brilliant cyber technologies which drive productivity and support growth.

DataPatrol

DataPatrol

DataPatrol is a software company, specialized in providing Security and Privacy of company’s data and information in an evolved way.

Scinary Cybersecurity

Scinary Cybersecurity

Scinary was founded in 2015 on the premise that cybersecurity should not be limited to just large corporations or large government entities.

Emagine IT

Emagine IT

Emagine IT supports federal agencies and enterprises by leveraging a data-first approach to delivering cutting-edge IT, cybersecurity, and digital transformation services.