Welcome back to the oneAPI channel.​
AI is everywhere, we can see the results, the products based on AI all around us​
But getting to these products is sometimes a continuous operation that is not easy to manage​
Some of the models are pretty big, so they need special machines,
or a Kubernetes managed cluster to even complete​
And you cannot wait for the model to finish, you have to run several experiments in parallel,
each experiment with a different combinations of parameters.​
Every day you have a new version of the data,
and we have 10 data-scientists in the company optimizing models every day..​
One big headache
And training models is really just part of the process,​
In an average company the flow include dozens of scripts, tools, applications and more​
The end result is that many models don’t even find their way to production,
and we are wasting time on… overhead​
Another issue is that in many companies, data-science, the creation of the models
is done in a different department than the product and deployment people.​
This disconnect has its cost
CNVRG platform was built to solve exactly these problems.​
And it can help you do it in several levels..​
The first level is CNVRG ability to easily construct a workflow​
We consider every script of yours, every model, every snapshot of data
as a building block that can be shared, re-used and construct a work-graph​
As you can see for example in this image,
we take some version of the data, to a data-preparation script and the output​
can be used to run different DL models in parallel , go to a different tool and so on.​
A full workflow
The second level is the ability to take many such graphs and execute
each of them in a container or other way on each of the resources at my disposal​
You can manage All these graphs centrally, monitor the progress, compare the results
and choose the best model with the right parameters that meet our targets​
​
With CNVRG, the development flows are now modular, easy to re-use and to share​
It is easier to track what set of parameters, with which set of data
and which version each run was launched.​
Runs could be better managed, resources could be better utilized- it could be machines,
could be Kubernetes clusters – ​
which means time and money savings​
, let me show you in a minute
CNVRG can work in the cloud, on your local machine, or in hybrid mode​
The easiest way to get started is to navigate to Cnvrg.io and ask for a demo​
It’s basically a short meeting where we can show you the capabilities, allow you to ask questions​
And guide you through your first steps on the platform​
This is what the CNVRG interface looks like​
All our projects​
All the Data-sets are arranged here​
In the AI LIBRARY I can find the models that I can access, ​
models that I work on and models that others are optimizing​
​
Under DASH BOARD you can see all our runs, ​
with their status and the results​
Containers, compute and more​
Let's go to our list of projects ...​
look for the project MNIST ​
get into the project ​
get into WORKSPACE ​
When you open a new WORKSPACE, You can select the work environment .Jupiter notebook, CVAT,
VS Code, R studio ​
And I can ask for a specific machine to run, ​
a specific dataset out of all available to me, ​
what container to run on​
And I get a work environment ready to use​
I can go to EXPERIMENTS And run experiments there ​
Or go directly to FLOWS​
Edit a workflow , as we see here​
I take data, pre-process it, train 3 different models on it, compare results, etc .​
.And I have a lot of options here for parameters and execution ​
And I can run this whole graph and follow its progress and the results in DASHBOARD​
When I'm happy with the results I can just switch to SERVING ​
And I get a ready SERVICE​
That can run the model I chose with all the parameters .. Serve it, monitor and more​
Of course there are lots of more options but I hope it is obvious
that it is easy and intuitive to get started
Cnvrg​
Scalable, hybrid, flexible, interactive, end to end platform​
By data scientists. For data scientists​
​ Enjoy, subscribe, thank you