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Building AI Systems

Building AI Systems

Organizations and entire Governments are in a hurry to define, understand and develop AI strategies that will help them remain competitive and deliver value in the years ahead. With so many tools and buzzwords it can be very difficult to see through the hype.

Every company is unique and there are a range of maturity levels when it comes to both building AI and deploying AI.

“Big Data” –> “Data Science” –> “ML/DL” –> “AI?” throw in a little “IoT” and you have the timeline for the last couple of decades.

The difference is that we are at a critical point in history where AI is no longer just a buzzword. Most people have had at least one if not countless AI experiences by now and companies / governments that are leveraging AI have pushed lightyears ahead of the rest.

The first thing to understand is that there is no panacea when it comes to AI. AI is not a product or point solution but rather the result of a solid strategy and execution over time. It is composed of several key ingredients and has many moving parts. It also has different forms and meanings.

You need a platform upon which to unite the builders of your AI systems. The Data Science required to build up an AI requires collaboration, experimentation, validation and a way to efficiently deploy algorithms in a manner that makes them easy to leverage. This platform must take into account that the scarce resource is skilled AI/ML/DS employees who can create algorithms, render visualizations, deploy models and integrate those into automated systems to generate value. This platform must manage the end to end lifecycle of the development and utilization of your models, as well as future proof your organization by adapting to your on premise and cloud strategy seamlessly. This is more than just picking a notebook environment or deciding on an MLOps tool. This is an operating system for AI.

With that said let’s build a simple AI using and illustrate the major components and phases that you will need to implement AI experiences for your business. is an AI OS. What separates an OS from a piece of software is that an OS brings together multiple components and systems together and exposes them to the user in a cohesive experience which allows efficiency and utilization of the underlying resources. does that seamlessly by managing compute CPU/GPU and for execution while also managing the file system and versioning that allows you to track changes and manage projects as they scale. The UI allows your creators to collaborate and build in an environment that manages all of the nasty details without disrupting the flow of creativity.

A typical flow is to begin by researching the problem at hand and identifying techniques and strategies that will help you solve the problem.


Each project in contains a research section for consolidating information and keeping it in the same place that the development occurs.

Workspaces allow users to choose the environment they are comfortable with, whether that is VSCode, RStudio, Jupyter. The workspaces can have compute allocated on the fly so resources are created only when you need them and automatically shut down when idle for a specific amount of time


The full lifecycle is taken into account from creating diagrams to coding in notebooks!


Once you have models created you can easily run experiments to automatically determine the most accurate model. Or, you can leverage the AI Library to use predefined flows in your project, compare several models, and automatically deploy the most accurate.

cnvrg visual flow

This is all great but now we hit a very important step in the process. The majority of organizations have a huge gap between creating a model and deploying/leveraging that model. With this is all seamless and part of the AI OS. Deployments can take many forms depending on what/where the data is. You might deploy a model on a large dataset you already have to further enrich the data or extract valuable information, i.e. “Batch”. There is also web service deployment which allows you to call a restful API and get a response containing the results of scoring. But what is most exciting is the Kafka stream deployment. This allows you to use Kafka as both the input and output of a deployment to allow real time AI architectures to be rapidly developed and deployed on top of your existing Lambda / Kappa architecture!


But what about visualizing data and presenting it to the user? You can deploy Dash, Shiny and Viola apps in

Everything you do is automatically managed and versioned by allowing you to rollback changes and track edits easily.

Continuous learning allows you to monitor metrics in your model and automatically kick off retraining flows to keep deployments from drifting.

All of this is possible within and can easily be expanded to leverage multiple cloud environments and compute templates allowing you to manage resources and utilization using the management and monitoring tools built into

cnvrg dashboard active

It would be outrageous for me to cover even more features in this one article so I give you the docs for further exploration of all the amazing components that go into and make it the best AI OS for organizations looking to enable true AI solutions.

Check out online and request a demo to see it in action! We can’t wait to help you get set up for success!

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