Clear business goals and objectives
Well defined and quantifiable success criteria
Good data management and governance
Easy access to good quality data for statistical analysis and model training
A small data analytics and data science team
Appropriate tools
Manual building ML / statistical models
Manual model testing
Basic framework for model verification and reproducibility
Clear business goals and objectives
Well defined and quantifiable success criteria
Good data management and governance
Easy access to good quality data for statistical analysis and model training
A small data analytics and data science team
Appropriate tools
Basic capability in data wrangling, data transformation, and feature engineering
Able to build relatively complex statistical models for business use cases
Manual building ML / statistical models
Manual model testing and updating
Basic framework for model verification and reproducibility
Basic model interpretability and analysis of feature importance
Clear business goals and objectives
Able to prioritize multiple ML use cases against business priorities
Well defined and quantifiable success criteria for each projects
Good data management and governance
Strong data science team
Appropriate tools and architecture to build complex machine learning models using open source libraries and frameworks
Strong capability in data wrangling, data transformation, and feature engineering
Able to run entire data pipeline manually
Able to manually build relatively complex ML models using offline batch learning
Manual model testing and monitoring
Able to manually detect performance deterioration and retrain ML models
Strong in model interpretability, reproducibility, and analysis of feature importance
Clear business goals and objectives
Senior stakeholders involvement
Able to prioritize multiple ML use cases against business priorities
Well defined and quantifiable success criteria for each projects
Able to store, manage, and process large amount of data for ML training
Strong data science team
Strong support in best coding practice from ML engineering
Appropriate tools and architecture to build complex machine learning models using open source libraries and frameworks
Strong capability in data wrangling, data transformation, and feature engineering
Able to build feature stores for reusability
Able to run entire data pipeline manually
Able to manually build relatively complex ML models using offline batch learning
Strong capability in model deployment
Manual model testing and monitoring
Able to manually detect performance deterioration and retrain ML models
Strong in model interpretability, reproducibility, and analysis of feature importance
Clear business goals and objectives
Senior stakeholders involvement
Able to prioritize multiple ML use cases against business priorities
Well defined and quantifiable success criteria for each projects
Able to store, manage, and process large amount of data for ML training
Strong data science and ML engineering team
Excellent coding practice
Appropriate tools and architecture to build complex machine learning models using open source libraries and frameworks
Strong capability in data wrangling, data transformation, and feature engineering
Able to build large feature stores for reusability
Able to perform continuous training of the models by automating the ML pipeline
Able to perform automated data and model validation steps to the pipeline
Able to automatically build relatively complex ML models using offline batch learning
Able to automate the process of using new data to retrain models in production
Strong capability in model deployment in live environment
Able to manually detect performance deterioration and retrain ML models
Strong in model interpretability, reproducibility, and analysis of feature importance
Clear business goals and objectives
Senior stakeholders involvement
Able to prioritize multiple ML use cases against business priorities
Well defined and quantifiable success criteria for each projects
Able to store, manage, and process large amount of data for ML training
Strong data science and ML engineering team
Modularized code for components and pipelines
Excellent coding practice
Appropriate tools and architecture to build complex machine learning models using open source libraries and frameworks
Strong capability in data wrangling, data transformation, and feature engineering
Able to build large feature stores for reusability
Able to perform continuous training of the models by automating the ML pipeline
Able to perform automated data and model validation steps to the pipeline, as well as pipeline triggers and metadata management
Able to automatically build complex ML models using both offline batch learning and online learning
Able to automate the process of using new data to retrain models in production
Ability to deploy ML models in live environment
Able to manually detect performance deterioration and retrain ML models
Strong in model interpretability, reproducibility, and analysis of feature importance
Clear business goals and objectives and large scale implementation
Very senior stakeholders involvement
Able to prioritize multiple ML use cases against business priorities
Well defined and quantifiable success criteria for each projects
Able to store, manage, and process large amount of data for ML training
Strong capability in big data processing
Strong data science and ML engineering team
Modularized code for components and pipelines
Excellent coding, testing, experimentation practice
Appropriate tools and architecture to build complex machine learning models using open source libraries and frameworks
Strong capability in data wrangling, data transformation, and feature engineering
Able to perform continuous training of the models by automating the ML pipeline
Able to perform automated data and model validation steps to the pipeline, as well as pipeline triggers and metadata management
Able to automatically build complex ML models using both offline batch learning and online learning
Able to automate the process of using new data to retrain models in production
Ability to deploy ML models in live environment
Large scale model deployment severing millions of customers
Able to manually detect performance deterioration and retrain ML models
Strong in model interpretability, reproducibility, and analysis of feature importance
Clear business goals and objectives and large scale implementation
Very senior stakeholders involvement
Able to prioritize multiple ML use cases against business priorities
Well defined and quantifiable success criteria for each projects
Able to store, manage, and process large amount of data for ML training
Strong capability in big data processing
Strong data science, ML engineering, DevOps team
Modularized code for components and pipelines
Excellent coding, testing, experimentation practice
Appropriate tools and architecture to build complex machine learning models using open source libraries and frameworks
Ability to build new tools and libraries for internal use
Strong capability in data wrangling, data transformation, and feature engineering
Able to build large feature stores for reusability
Able to perform automated data and model validation steps to the pipeline, as well as pipeline triggers and metadata management
Able to automatically build complex ML models using both offline batch learning and online learning
Able to automate the process of using new data to retrain models in production
Ability to deploy ML models in live environment
Large scale model deployment severing millions of customers
Able to perform automatic performance monitoring and retraining ML models
Build a robust automated CI/CD/CT system
Strong in model interpretability, reproducibility, and analysis of feature importance
Clear business goals and objectives for worldwide deployment
Very senior stakeholders involvement
Able to prioritize multiple ML use cases against business priorities
Well defined and quantifiable success criteria for each projects
Able to store, manage, and process large amount of data for ML training
Strong capability in big data processing
Strong data science, ML engineering, DevOps team
Modularized code for components and pipelines
Excellent coding, testing, experimentation practice
Appropriate tools and architecture to build complex machine learning models using open source libraries and frameworks
Strong ability to build new tools and large libraries for internal use
Strong capability in data wrangling, data transformation, and feature engineering
Able to build large feature stores for reusability
Strong capability in simulation testing
Able to perform automated data and model validation steps to the pipeline, as well as pipeline triggers and metadata management
Able to automatically build complex ML models using both offline batch learning and online learning
Able to automate the process of using new data to retrain models in production
Ability to deploy ML models in live environment
Worldwide model deployment severing hundreds of millions of customers concurrently
Able to perform automatic performance monitoring and retraining ML models
Build a robust automated CI/CD/CT system
Strong in model interpretability, reproducibility, and analysis of feature importance
Robust protocols to deal with external attacks
Clear business goals and objectives for worldwide deployment
Teams of very senior stakeholders involved
Able to prioritize multiple ML use cases against business priorities
Well defined and quantifiable success criteria for each projects
Able to store, manage, and process large amount of data for ML training
Strong capability in big data processing
Strong capability in edge computing
Strong data science, ML engineering, DevOps, and software engineering team
Modularized code for components and pipelines
Excellent coding, testing, experimentation practice
Strong capability in edge computing for deploying high-performing models on edge devices
Appropriate tools and architecture to build complex machine learning models using open source libraries and frameworks
Strong ability to build new open source tools and libraries
Strong ability to build complex hybrid models
Strong ability in simulation testing
Strong capability in deploying models on edge devices
Strong capability in data wrangling, data transformation, and feature engineering
Able to build large feature stores for reusability
Able to perform automated data and model validation steps to the pipeline, as well as pipeline triggers and metadata management
Able to automatically build complex ML models using both offline batch learning and online learning
Able to automate the process of using new data to retrain models in production
Ability to deploy ML models in live environment
Worldwide model deployment severing hundreds of millions of customers concurrently
Able to perform automatic performance monitoring and retraining ML models
Build a robust automated CI/CD/CT system
Strong in model interpretability, reproducibility, and analysis of feature importance
Robust protocols to deal with external attacks
