AI stack

AI stack: Pros and cons of building your own stack.

All firms will benefit greatly from AI tools. You will eventually employ them to generate income, boost productivity, boost operational effectiveness, and improve customer service.

What strategies are you doing to build these AI capabilities for your company? Are you creating your AI solutions from scratch or leveraging pre-existing tools?

What is AI stack?
Imagine that your AI solution has been constructed in layers. The AI-enabled tool or application you will employ to increase productivity or get a competitive edge is at the very top of the stack. The three main layers that enable it are listed below:

Algorithms are fundamental guidelines or computer codes that process data, like a deep neural network.
Various frameworks and technologies, like Google’s well-liked Keras and TensorFlow, are required to develop machine learning capabilities.

ML Platform and Infrastructure

A cloud service provider, like Google Cloud, that offers your ML/AI capabilities as a service is what we mean by this.
Previously, you were forced to develop, train, and deploy your ML. The cloud is now available for your ML framework or even a full cognitive capacity. For instance, you might train your algorithm to spot your product in an image using Google’s existing image recognition features.

Is creating your stack preferable?
We decided to construct all our AI-driven technologies in-house for our contact center solution, including a voice bot platform and speech analytics tools.

Our main component is a patented proprietary AI algorithm. We rely on our technologies created on top of python libraries rather than Keras or TensorFlow. We utilize our proprietary infrastructure and have developed our own internal AI platform.

Our fundamental algorithm’s features include its superior flexibility compared to other deep learning algorithms. Our ML platform’s upkeep guarantees that our data will always belong to us. We are in total control and don’t have to give Google or anybody else access to your data.

When should your machine learning platform be outsourced?
You can use Google Cloud ML or Amazon ML if you have the skills to create models or algorithms on your own but do not want to maintain the infrastructure components. Pay-per-use business concepts let you experiment on a budget. Additionally, you have access to more powerful computing.

Additionally, one can employ pre-built AI features like speech recognition, video recognition (Amazon Rekognition Video, Google Vision), and video recognition (Amazon Comprehend, Text Analytics API). You can alter these to fit your applications.

Utilizing pre-made modules or existing infrastructure components has the benefit of cutting down on development time and overhead expenses. Time to market will be greatly shortened. Data and security issues, however, will surface. In these situations, you must fully understand the terms of service before uploading your data for training, especially if you’re providing sensitive data that you must preserve. When using machine learning skills, it’s crucial to consider whether your provider has full control over the data.

For businesses that don’t use the cloud, whether for security or other reasons or for businesses that want to build highly customized solutions, it will be essential to build their stack or work with smaller providers (who own their stack). Smaller companies and lone developers can use cloud services, particularly for straightforward or unproven use cases.

If you want to do it alone, ensure you have enough processing power, safe infrastructure, and data to build your own AI stack. If not, cloud machine learning (ML) solutions or a PAAS (platform as a service) could be your fix, provided you remember to exercise caution when using your training data.


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