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Why Technology Agnostic AI Platforms are the Way Forward

AI has become such a game-changer with its ability to make data-driven decisions that help enterprises overcome business challenges. Changing customer behaviors and expectations have become a more painful concern than ever before. Further, the Covid pandemic highlighted the necessity of AI and ML integration across enterprises so that guesswork can be replaced with certainty. 

However, despite having so much hype around AI and its benefits to enterprises, 50% of ML projects fail to achieve their successful deployment. Despite the promise of AI, many organizations’ AI efforts are falling short. McKinsey’s research shows that only 8% of enterprises engage in fundamental practices that ensure widespread AI adoption. And more surprising is that most enterprises have executed only ad hoc pilots or are leveraging AI in just a single business process.

AI project implementation challenges mainly include cost factors, undefined outcomes, lengthy development cycle, lack of expertise-skilled resources, and regulatory concerns. 

Thankfully Technology-Agnostic AI platforms allow defining a new success roadmap for enterprise AI adoption. Presenting this blog post to explore technology-agnostic AI platforms from different perspectives, such as their need, advantages, and our contribution as a Technology Agnostic AI Platform partner.

Why a Technology-Agnostic Approach Matters

With the massive need for AI adoptions, many players are positioned to provide their AI offerings, from open source solutions to comprehensive AI platforms to use-case-driven AI solutions. Even IT giants like Google and IBM offer excellent tools and utilities to ensure seamless AI adoption for business processes. Be it Google TensorFlow that enables machine learning, or AWS IoT, every offering is unique and corresponds to a specific problem in AI deployment. 

However, if we deep dive with different AI tools, technologies, and utilities, we realize that these applications and utilities cater to specific AI requirements. These are programmed using various programming languages and are supposed to work independently and not together. Further, each tool and utility employs a separate computational model and diverse data structures, making each tool-- a special one to address a particular problem. 

Effective implementation of AI comprises the following components and their smooth integration.

Data Preprocessing: A range of tools helping data preprocessing tasks include Glue GTL, DataFlow, Databricks, Data Explorer, etc.

ML Services: The man of the match indeed, it includes ML modeling tools like AWS Sagemaker, Azure ML, TensorFlow, AutoML, DL4J., etc.

Developer tools and UI: Application development supported by UI frameworks like React and Angular.

Data Visualization: Data visualization enhanced with the help of Power BI, Quicksight, Plotly, Data Lab, and others.

Deployment: Deployment simplified with Docker, Kubernetes, ONXX, Bitbucket/GIT, Argo, and CircleCI

Monitoring and version control: ML Flow, TensorFlow Extended, Prometheus, Seldon Core to help in monitoring and version control.

Technology Agnostic AI Platforms is a future-proof way of AI adoption to ensure enterprise AI implementation at scale. Technology agnostic approach allows devising AI solutions irrespective of technology and tools by combining multiple technologies of user’s choice.

Paving The Future For Enterprise AI Adoption

To begin with technology-agnostic AI Platforms, we need to understand the meaning of what technology-agnostic is. As mentioned in the previous section, we have a rich range of open source and proprietary ML tools and APIs available on the web. The technology-agnostic approach allows users to find the most effective AI tool and use it to solve the problem without restricting any particular type of technology.

Technology Agnostic AI platforms provide an end-to-end solution for AI development. With customizable and model-driven architecture, enterprises accelerate ML model deployment by adding an abstraction layer to simplify model deployment. A future-proof AI development approach ensures easy integration of different tools and technologies required to build an AI application.

Technology-Agnostic AI platforms overcome the drawback of other AI adoption approaches such as DIY or build it yourself and cloud services. Although creating a custom solutions approach sounds interesting, it has severe drawbacks such as complex architecture, vendor lock-in, lengthy development process, and high cost for development and maintenance.

Cloud-driven AI development involves the use of services and microservices offered by cloud providers. Although this approach ensures well-tested and highly sophisticated cloud features, it fails to break the complexity level with AI development. Vendor lock-in, complicated architecture, and development and maintenance cost overheads come as its downside.

AI approaches comparison
A comparison between enterprise AI adoption approaches


How Technology Agnostic AI Platforms Are Different

Interoperable and technology agnostic AI platforms define a new approach for enterprise AI adoption. These platforms help you focus on a bigger picture by considering a big spectrum of AI tools and utilities available.

Technology-Agnostic AI platform offers you the flexibility of being tech-forward or backward-compatible as and when needed. These platforms help you accommodate your current ML technology and also prepare the enterprise for future technology waves.

Multi-cloud computing and operations have become crucial for modern technology-agnostic AI platforms. Enterprise AI adoption demands use of multiple cloud services simultaneously irrespective of the cloud vendor. Fortunately, modern-day open AI platforms support hybrid cloud models to leverage cloud AI service of major public cloud providers. 

Technology agnostic open AI platforms help enterprises to interoperate with different products, technologies, and components. These platforms offer better flexibility and productivity by supporting APIs, DevOps capabilities, ML libraries, and third-party data presentation tools.

Attri’s Open AI Platform — A Modern Approach To Enterprise AI

In pursuit of the ideal machine learning platform, we have introduced our Open AI-based platform to ease enterprise AI adoption with cost optimization, maximum flexibility, and accelerated GTM. Attri’s Open AI Platform provides a concrete solution for AI adoption requirements due to the following reasons.

No reinventing efforts: Attri’s Open AI Platform minimizes your AI suites reinventing efforts and allows flawless integration of multiple AI services. The platform nurtures reusability and efficiency in enterprise AI adoption.

Accelerate GTM: Our Platform ensures an accelerated Go To Market with faster AI deployments and proven results in a limited timeframe.

No vendor lock-in: Attri Open AI Platform empowers clients with no vendor lock-in requirements and freedom over technology stacks with no tie-ins.

Reliability and future scalability: Attri’s Open AI Platform ensures higher reliability and future scalability to meet future needs of AI adoption. 

With the growing needs of enterprise AI adoption, Attri’s AI Platform becomes the ultimate AI implementation solution with its customizable best-of-breed architecture. The Platform rewards enterprises with maximum flexibility and reusability, thereby saving them money, time, and resources. Attri’s user-first AI development approach ensures fair and explainable AI solutions to cater to business needs in a justified way.

Enterprise AI adoption has always been a crucial task with many complications and responsibilities. But technology-agnostic AI Platform is helping enterprises with seamless AI adoption, enhanced success rate, and more scalability.


Mugdha Somani

Mugdha Somani

As a former professor and publisher of technical books, Mugdha always keeps up with the latest academic and industry advances. An educator at heart, she enjoys sharing knowledge and connecting with likeminded folks.