We can educate you and your teams:
This step sounds trivial, but a general understanding is
vital to successfully using this technology. We recommend
taking a deep dive into topics like MLOps, Model
Development Lifecycle, and the importance of relative
data. Working in cross-functional teams is also a good way
to get familiar with AI/ML basics.
We together can select a pilot project:
Start small when selecting your pilot project. Avoid
attempting to solve the most complex problem with AI/ML
technology in your organization. Instead, find a small
initiative that can make a measurable impact for a
particular group or department in your organization.
We can get you expert advice:
If your organization doesn’t have the capability in-house,
get our expert advice. You may need experts who can assist
you with collaboration across teams, define new processes,
and gather technology advice.
We can help you prepare your data:
Data is the most crucial part of your project. You will
need lots of it. The more data, the better.
we can help you define the metrics for your model:
This is one of the most crucial phases in which the
subject matter experts (SME) define how to validate the
AI/ML model’s success. There are many metrics available
such as precision, recall and accuracy. Every use case is
different and selecting the correct validation metric is
vital for a successful outcome. A model built for medical
diagnoses will have different considerations than building
a model for spam detection.
Explore data with our SMEs and run experiments:
Work with SMEs or domain experts to further understand
what data is useful, and how to achieve optimal metrics
defined earlier. Experiment with different algorithms and
hyperparameters to find the best fit for your pilot’s use
Train and validate your model with us:
For training and validating your Model, it is recommended
to split your data into three sets: a training set (~
70%), a test set (~15%) and a validation set (~ 15%).
Ensure your training set is large enough to see meaningful
results when using the model on it.
we can implement DevOps and MLOps:
Building a model is a job half done, then integrating it
into your end-to-end lifecycle might be challenging. Often
when data scientists develop models on their laptops,
integration into production becomes a significant
challenge. The operations teams and development teams need
to collaborate with data scientists in iterative ways.
Automation is key, and continuous integration and
continuous deployment (CI/CD) can help get the model
Move your model into production:
Once the model has been built and thoroughly tested, it is
ready for production rollout. If possible, roll out to a
small number of users first. Monitor the model’s
performance over several days. Be sure to include
stakeholders and SMEs in these discussions to evaluate
results and provide continuous feedback. Once the
stakeholders have accepted the model’s performance, the
model can then be rolled out to a broader audience.
Keep your model relevant to the real world:
Once your model is in production, we can help you to
continuously monitor and adjust the model’s performance
based on its current market situation. Market conditions
can be triggered through various events.
Celebrate success and promote the outcome:
Once your pilot project is successful, promote and
advertise it within your organization. Use internal
newsletters, internal websites, or even consider an email
from the pilot’s sponsor sent out to stakeholders to
promote the successes.