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The Wayback Machine - https://web.archive.org/web/20240813021528/https://devops.com/logz-demo/
Logz Demo - DevOps.com
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Logz Demo
July 12, 2022
by
AWS Builder Community Hub
Deploying AI/ML Data To Production
AI/ML Feature Store
Step
1
of
7
14%
What are the most challenging aspects of the Data Science lifecycle? (Select all that apply)
(Required)
Understanding and framing the problem
Acquiring and understanding the data
Data prep and feature engineering
Model training and validation
Model and pipeline deployment
Demonstrating business value
Collaborating with key stakeholders, including business and IT
Rank the following phases based on the level of difficulty in your ML data process (Rank from most to least challenging)
Getting access to the right data
Understanding data semantics
Dealing with data quality
Ideating and creating features
Generating historical datasets for training
Deploying feature pipelines
Monitoring pipelines
Which of the following solutions are you currently exploring to address data science and ML challenges? (Select all that apply)
(Required)
Copilots for code generation
Automated data preparation and deployment tools
Automated modeling and deployment (AutoML)
Agentic data science systems
MLOps platforms
Enhanced collaboration and communication tools
With automation, who besides data scientists could build and deploy ML models in your organization? (Select all that applyl)
(Required)
Software developers
Data engineers
ML engineers
Data analysts
Business analysts
On average, how long does it take for your team to move from raw data to a production-ready ML model? (Select one)
(Required)
Less than 1 month
1-2 months
2-4 months
4-6 months
More than 6 months
What impact do delays in deploying AI/ML models have on your business operations? (Select all that apply)
(Required)
Significant lost business opportunities
Moderate impact on business processes
Minimal impact
No noticeable impact
What role do you play in your organization?
(Required)
Data scientist - practitioner
Data scientist - leadership
Business/Data Analyst - practitioner
Analytics - leadership
Developer/Software Engineer - practitioner
Developer/Software Engineer - leadership
Data engineering - practitioner
Data engineering - leadership
CTO/CIO/CDO
Other
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