Deploying and Serving Challenges

Question 1

What are the most common deployment and infrastructure challenges?

Answer 1

Model deployment complexity
Model monitoring and maintenance
Scalability and latency

Question 2

What are model deployment complexities?

Answer 2

Converting a trained model into an API or micro service

Deployment formats can vary if models need special runtime environments

Ensuring compatibility across cloud platforms and edge devices

Question 3

What are model monitoring and maintenance challenges?

Answer 3

Deployed models need monitoring to detect degradation overtime. Drift data can cause models to be outdated requiring retraining. Logging and tracking can help

Question 4

What are scalability and latency problems?

Answer 4

Models need to be able to handle a large volume of requests and data, requiring scalable infrastructure and efficient serving techniques

Question 5

What are infrastructure challenges

Answer 5

Setting up and maintaining the infrastructure for deploying and serving ML models can be complex, requiring expertise in cloud computing, networking, and DevOps

Question 6

What are real time vs batch processing challenges

Answer 6

Choosing the right serving strategy (real-time or batch) depends on the application requirements and can impact performance and resource utilization

Question 7

Why is monitoring and logging helpful?

Answer 7

Continuous monitoring of model performance and infrastructure health is essential for identifying and addressing issues promptly.