In the whole Machine Learning development, model development is the easy and least time-consuming effort. Operationalization is the hardest and that’s where all projects are either failing or struggling to justify investment.
It’s not a one time effort.
Most Machine Learning projects start with access to existing data and particular use case to solve. The analogy I use often is making a resolution to stay healthy.
Steps to stay healthy:
- Get a proper night’s sleep
- Start your day with exercise
- Make healthier food choices
Doing it once is Easy
Making it as a practice is HARD
The above analogy fits well for Machine Learning projects. It’s easy for project teams to work on point in-time data and build Machine learning models.
Steps to build Machine learning model:
- Fetch relevant point in-time data.
- Build Model
- Test, train and evaluate model results.
Again, doing it once is EASY
Integration is HARD
Automation/Doing it at scale is SUPER HARD
In the next subsequent blogs, we’ll talk about platforms (Google, Amazon, Microsoft) that are investing signifcant resources to provide ML platform as a service compared to Predictive Apps (Salesforce, Oracle) that provide ML as APIs.