Machine Learning Development
The full lifecycle of machine learning (ML) or AI model development includes aspects of problems scoping, data preparation and engineering, machine learning model iteration, machine learning model evaluation, and ML model iteration and versioning. Building an effective ML model is challenging on both the business and technicals fronts. This is especially true with machine learning development for enterprise implementation. Machine learning model development needs to be done carefully and methodically if it is going to bring the returns you should expect from your investment. There are six steps to the process:
1. Determine Business Objective: Starting with a poorly defined problem is a direct route to failure.
2. Design Solution: Think through the solution carefully and accept input from all the user groups and other stakeholders.
3. Create Model: Test multiple approaches. Build on existing models when possible but do not expect to find a cut-and-paste solution.
4. Evaluate Model: Reserve a proportion of your historical data for testing. Then test in a real pilot environment.
5. Deploy Solution: You have to slow down to go fast. Think through the right architecture to make sure your algorithm translates from the training box to the real world.
6. Manage Continuous Improvement: Expect subpar results from the first deployment. A large deployment base will provide data to improve algorithm effectiveness.
Engagements typically range from a three to six months.
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Who is this for?
Seniority level: C-3 (e.g.. product managers), C-4 (technology professionals)
Functions: Operational (e.g., production, maintenance, logistics), Innovation (e.g., R&D, accelerator, CDO)
- Define a holistic strategy to use data science to drive business and operational results.
- Sense check the problem space to increase the probability of building an effective algorithm.
- Accelerate time from ideation to deployment to continuous improvement.