First steps in leveraging machine learning
Where to start and what to expect from a machine learning project? Here is one approach that we have successfully used with our clients.
Step 1: Identifying a potential business challenge
A Data & Machine Learning workshop, in which:
- you learn about the data used at your company;
- you find potential business challenges that could be resolved through machine learning; and
- after the workshop, methods (Lamia) and the data (client) are reviewed.
Step 2: Data audit
The purpose of a data audit is to carefully examine whether the data allows the project to progress.
- Do we have the required data?
- Do we have access to the data?
Step 3: Preparing the data
If the data audit gives the green light, the data is prepared and imported to an analyzing environment in the required format.
Step 4: Proof of concept
A proof-of-concept (PoC) test shows whether the chosen model and data will achieve the desired benefits. The proof-of-concept study includes teaching a machine learning model and possible cloud automatization steps (e.g. Google Cloud Platform, GCP).
Step 5: Pilot or deployment
If the PoC is accurate enough, a pilot project is launched or the model is deployed.