Starting out along your ML journey can be an interesting and exciting time. People are probably trying to sell you on the benefits of a multitude of things. You can find academic interpretations and checklists of readiness for ML in production.[1] A lot of checklists and other rubrics exist. The very first place I start to question at the start of any ML journey happens to be the basic use cases the company handles. You need to start out with some back of the envelope sketches of what exactly the company is trying to do or hopefully is actually doing. Maybe they are selling something or providing some type of service. After getting that initial sketch of what is happening the next question is tell me about your data storage, KPIs, and objective measures. Mentally I’m sketching out the use case the company is trying to achieve and how they are reporting and monitoring that use case. Between those two things is where your ML readiness is going to unfold. Maybe a straightforward API integration with AWS, GCP, or Azure could get you going without a ton of effort. It's entirely possible that could be the answer and getting going might happen very quickly.
Figuring out ML readiness
Figuring out ML readiness
Figuring out ML readiness
Starting out along your ML journey can be an interesting and exciting time. People are probably trying to sell you on the benefits of a multitude of things. You can find academic interpretations and checklists of readiness for ML in production.[1] A lot of checklists and other rubrics exist. The very first place I start to question at the start of any ML journey happens to be the basic use cases the company handles. You need to start out with some back of the envelope sketches of what exactly the company is trying to do or hopefully is actually doing. Maybe they are selling something or providing some type of service. After getting that initial sketch of what is happening the next question is tell me about your data storage, KPIs, and objective measures. Mentally I’m sketching out the use case the company is trying to achieve and how they are reporting and monitoring that use case. Between those two things is where your ML readiness is going to unfold. Maybe a straightforward API integration with AWS, GCP, or Azure could get you going without a ton of effort. It's entirely possible that could be the answer and getting going might happen very quickly.