What challenges are ML Engineering team facing?
Key success factor that defines a ML Engineering team is that they have access to Data and then they have automated their software engineering to achieve agility and efficiency in delivering value to the business. A ML Engineering team faces following challenges in two broad category.
- DataOps – Data Engineering
- Data collection from different sources and extraction
- Data pipelines that primes for analysis and models
- Data Science
- Develop features and models
- Train Models
- Discover new features
- Data: Not able to get the production data “quickly” for analysis and feature engineering
These challenges make getting machine learning (ML) into production a difficult and painful process. In comparison with classical software development and deployment, AI/ML engineering and deployment is an order of magnitude harder. As a result of this challenges, most AI/ML models never see the light of production-day. Thus, due these challenges, many organizations fail to capture benefits of ML quickly to capitalize on their data and provide their customer with next generation experiences.
