A machine learning (ML) engineer is a data science team member who creates or refines programs and algorithms that enable machines to automatically learn from data to identify patterns and make predictions.
What Does a ML Engineer Do?
The role of a ML engineer can vary by organization. Listed below are some of the common tasks that a ML engineer performs:
- Writing algorithms to train ML models
- Testing training models
- Evaluating and using training tools.
- Choosing and refining data sources for use by ML tools
- Interviewing stakeholders to gather requirements
- Working with stakeholders to develop use cases
- Performance tuning
- Calculating the effectiveness of MLmodels
- Iterating existing models to improve accuracy
- Creating and maintaining data pipelines
- Coding in Python and using the TensorFlow software library
What Qualifications Are Required to Be a ML Engineer?
A typical qualification is a computer science degree. Strong math skills will help with statistical analysis tasks and formulating algorithms. Knowledge of IT systems and data analysis is helpful. Specific skills in Java, C++, Python and TensorFlow will be valuable.
What Skills Does a ML Engineer Need?
As well as having the right qualifications to get a ML engineer role, the day-to-day ML activities of a ML engineer will exercise and develop the following skills:
- Good communication skills to understand requirements, explain the results, and write effective documentation
- Creativity is an essential part of the role as the ML engineer has to visualize and navigate the problem space to design the appropriate algorithms to create the ML model
- Statistical skills enable a ML engineer to evaluate the relative success of a ML model
- Analytical skills to assess the suitability of the dataset for the task at hand
- Knowledge of algorithms, including:
- Nearest Neighbor
- Decision Trees
- Linear Regression
- Neural Networks
- Naive Bayes
- K-means clustering
- Data management skills for building data pipelines and refining raw data
- Streaming continuous data flows using options such as Kafka and RabbitMQ
- Coding is an essential skill for an ML engineer. Python programming will be the bulk of the work, but any typed language is helpful.
- Basic Linux skills to run scripts to test drive models
- Knowledge of agile processes and team structures.
ML Project Outline
Below are the basic steps of a typical project that a ML engineer will usually follow:
- Data collection involves finding candidate data to drive the required ML model. The quantity and quality of data will impact the accuracy of the model.
- Data preparation is required to transform source datasets for use by the ML model. Data must be refined to filter out irrelevant content, gaps filled, and data formats normalized. At this stage, data is categorized for training the ML model or for evaluating the model.
- The model selection must pin down the appropriate ML model training method. Models such as linear regression, k-means, and Bayesian can be selected based on analytical requirements.
- Model training applies the algorithms to the selected data. Consistent training will help improve the prediction rate of the ML model.
- Model evaluation determines if the model is going in the right direction. The machine model will have to be tested against the validation dataset to assess the accuracy of the model.
- Parameter tuning tweaks the model to improve its accuracy. Hyperparameters are external configuration variables that directly influence the model architecture.
- The output from the project is a set of predictions.
How Actian Can Help a ML Engineer
The Actian Data Platform is a highly scalable data analytics platform with a rich feature set for ingesting, organizing, analyzing, and publishing data. The Actian Data Platform can help ML engineers by automating data pipelines, connecting to operational data sources using predefined connectors, and transforming data for ML use cases. You can get started with a free 30-day trial by visiting the Actian website.