View ML Engineer tasks
The Machine Learning Engineer (ML Engineer) is one of the most technically skilled roles. The ML Engineer background includes a variety of completed end-to-end Java-based projects and a wide experience in creating business applications, rolling out to production, and dealing with environment challenges.
What is expected from ML Engineer
The ML Engineer's tasks are as follows:
- Enforce a qualitative training set as a starting point of ML solution:
- Perform and explain Data Analyst (DA) key functions: document tagging, analyze P/R/TP/FP/FN.
- Identify problems with a training set and solve them by manipulating document sets.
- Improve a model based on data analysis following the IA Cloud solution workflow:
- Analyze training results and understand what needs to be improved based on data analysis.
- Know parameters that influence the overall model extraction quality and methods to improve it.
- Know how to read extraction per field output statistics and what needs to be improved based on this analysis.
- Understand typical AutoML pipeline: OCR > Tagging > OOTB Model training > AutoML SDK.
- Possess practical skills in Search Engine settings for training (fields, experiments, time limit, max iterations).
- Understand how to launch fixed model training or an improved training set for the previously trained model without Search Engine.
- Be able to split a data set and a test model on a test data set.
- Have business case implementation experience, with a delivered PoC case as a final certification task during the Automation Academy learning path.
- Use WorkFusion AutoML components as an ML solution:
- Set up an ML Business Process in Control Tower, fine-tune a Business Process, configure AutoML in a Manual Task, perform training in Control Tower, create Bot Tasks, and design sequential launch of training processes.
- Deploy a model to make it available via Control Tower.
- Transfer an ML solution through DEV > QA > UAT > PROD.
- Understand the difference between the Development and Production environments and their usage strategy.
- Solve issues on failed training processes.
- Configure Mesos/Marathon and ZooNavigator settings.
- Tune IA Cloud OCR capabilities, distinguish OCR errors, and fix them using AutoML SDK.
- Know and use environment health checks.
- Apply investigation skills when training fails.
- Apply AutoML SDK when needed.
- Understand the theoretical meaning of Annotators and Feature Extractors and how they relate to model fields.
- Be able to describe the AutoML SDK flow logically.
Role in RACI matrix
RACI stands for:
- R: Responsible
- C: Consult
- A: Accountable
- I: Informed
For ML Engineer responsibilities according to the RACI matrix, refer to the table below:
| Area | Responsibility |
|---|---|
| Set up OCR environment and Business Processes | I |
| Create a Qualification Task for a customer (SME) | I |
| Prepare a training set | AC (technical help to DA if required) |
| Analyze and improve a training set | AC |
| Create a training set from customer data | R (with DA) |
| Automate a Business Process | RA |
| Set up ML environment | RA |
| Train a model | RA |
| Evaluate ML results and statistics | R (with DA) |
| Analyze metrics and provide post-processing description | CR (with DA) |
| Perform post-processing implementation | RA |
| Generate automation report for a customer | C |