By automating processes with AI, you can reduce costs and make workflows more efficient. From our experience, we will show you the steps necessary to implement an AI project and how to make it a success.
The foundation for the project is the initial meeting and evaluation. In steps 3 to 5, we drive the development forward. They are repeated until we reach the desired final product.
Bonus: Tips for a successful AI project.
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1. How to properly implement
an AI project
3rd part of our infographic series
How Deep Learning drives businesses forward through automation.
3. Iteration
4. Integration
Current situation: Analysis of your current
processes and change requests.
Goal definition: What end result and processes do
you want?
Budget: What budget do you have? Goal and budget
define the paths we can take together in the project.
Data situation: Do you have data that we can use
for training? Is there a continuous data collection?
Assessment of your data and planning of a
data cycle
Definition of a Minimum Viable Product (MVP)
to get a quickly deployable result
Definition of Key Performance Indicators (KPI)
for objective quality measurement
Planning the integration into your productive
system
Once an acceptable quality level of the model is
reached after training, we deliver you a first product
version, usually a Docker image with API. You then
start integrating it into your system and workflows.
We accompany you during this process.
We train the algorithm with most of the available
data. Then we check the performance of the model
with unseen data. Depending on the project, training
the model is only a fraction of the whole
development. Often it is necessary for us to build a
separate process in which the model can be
embedded, such as a web service.
Copyright by pixolution GmbH und Datanomiq GmbH 2022.
2. Evaluation
1. Initial meeting
By automating processes with AI, you can reduce costs and make workflows more efficient.
From our experience with deep learning projects, we will show you the steps necessary to
implement an AI project and how to make it a success. The foundation for the project is the
initial meeting and evaluation. In steps 3 to 5, we drive the development forward. They are
repeated until we reach the desired final product.
5. Collecting feedback
After integration, it is very important to collect data
about the use in productive operation. This way you
can assess whether the AI is working as it should.
We then analyze what the model can already do
and what it can't. You collect that data, and then we
feed it into the next training iteration.
Tips for a successful AI project
An AI-based process must be introduced iteratively to increase the quality and
functionality of the development step by step.
Understand training data delivery as a cycle in which you, our customer, play a critical role.
Measure the project against target values. It’s the only way to identify regressions or
progress and finally reach your goal.
