How to Build Your AI ML Infrastructure and Pipelines Faster and Cheaper

Introduction to Building AI/ML Infrastructure

In today’s business landscape, Artificial Intelligence (AI) and Machine Learning (ML) have become indispensable for growth and success of any organization. As an expert in AI/ML infrastructure, we understand the complexity involved in building such systems, but also the immense benefits that they offer to organizations.

In this blog, we will guide you through the different approaches to building AI/ML infrastructure and help you make an informed decision. There are three main ways to build AI/ML pipelines: (1) building a custom solution, (2) buying an end-to-end platform, or (3) combining Best of Breed solutions. We will provide you with valuable insights on the advantages and disadvantages of each approach.

Build Your Own Infrastructure and Software Stack

Building a custom AI/ML infrastructure from scratch provides maximum control and customization. However, this approach requires a large team with expertise in AI/ML, compute infrastructure, GPUs, storage and networking. It also demands a deep understanding of the latest AI/ML algorithms, programming languages, and frameworks.

As an expert in AI/ML infrastructure, we understand the challenges of building a custom solution. While it provides maximum control and customization, it can be time-consuming, expensive, and complex. Therefore, we have seen only large companies in software space use this approach. Some examples include Google, Facebook, Amazon, Netflix, Uber, Airbnb and Microsoft. We recommend this approach only for organizations with a very large scale need and existing teams that are working on infrastructure for other projects. For large organizations in non-technical areas, this may be a risky approach to take.

Buy End-to-End Solution

Buying an end-to-end AI/ML platform like AWS Sagemaker, Azure ML provides a one-size-fits-all solution. These vendors have built an end to end solution that can be used in a specific way to solve a use case. By design they have to cater to a feature set which is applicable across a large number of use cases and leave the rest to the organizations using them. For any specific need, either the organizations have to hire their own people or use a technical partner to work with them. In addition, this approach may not be the best fit for every organization, as the platform is focusing on general use and not building the best application for each step of a pipeline. Organizations may have specific requirements or workflows that are not supported by the platform, leading to additional costs and complexity.

As an expert in AI/ML infrastructure, we understand the importance of evaluating an end-to-end platform carefully before making a decision. We run into customers who start with an end to end approach but soon realize that they have to bring specific applications for certain steps and deploy and manage themselves. So while this is the easiest way to get started, it can get more expensive in the long run. So, it is essential to ensure that the platform meets your specific needs and requirements.

Putting Together Best of Breed Applications

The Best of Breed approach allows organizations to use the best tools for their specific use case, and put together a custom pipeline that meets their needs. This is the best approach for most organizations, as it enables them to keep up with the rapid pace of innovation in the AI/ML space. Organizations get to choose the tools that are best suited to their specific use case, ensuring that they can achieve the best results possible.The field is rapidly evolving, and new tools and technologies are emerging all the time. By adopting a modular approach, organizations can easily swap out outdated tools for newer, more effective ones, ensuring that they are always using the latest and most advanced technologies.

However, the Best of Breed approach requires more effort to integrate tools and manage different components. Therefore, we recommend this approach only for organizations that have the necessary resources and expertise to manage their AI/ML infrastructure. Another option is to use solutions like Edgebricks and others who make it easier to put together a pipeline using best of the breed applications. We have curated and tested various applications and also come up with best practices to put them together.


Building an AI/ML infrastructure is a significant undertaking that requires careful consideration of the different approaches available. As an expert in AI/ML infrastructure, we recommend evaluating your specific needs and requirements before making a decision. With the right approach, organizations can build a robust AI/ML pipeline that meets their specific needs while being cost-effective and innovative. By leveraging the latest advancements in AI/ML tools and frameworks, organizations can improve their decision-making, optimize their workflows, and gain a competitive advantage in their respective industries.



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