Machine Learning Engineer Specializations
AI Category Field Overview
Artificial Intelligence (AI) is a vast field encompassing various subfields that focus on creating intelligent machine behavior.
Data Science
The interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge from data.
Statistical Analysis
Utilize statistical methods to interpret data and provide insights.
Predictive Modeling
Creating models that predict future outcomes based on historical data.
Data Visualization
The graphical representation of data to understand trends and patterns.
AI Ethics
The study of ethical issues arising from AI technologies.
Data Engineering
Focuses on practical application of data collection and data processing.
Data Architecture
Designs and manages data workflows and pipelines.
Big Data Technologies
Tools and frameworks used to handle large scale data processing.
Data Storage Solutions
Methods for efficiently storing and retrieving large amounts of data.
ETL Processes
Extracting, transforming, and loading data for analysis.
Data Analysis
The process of inspecting and transforming data to discover useful information.
Data Mining
Extracting patterns from large datasets using machine learning, statistics, and database systems.
Business Intelligence (BI)
Techniques for transforming data into actionable information for business decision-making.
Data Wrangling
The process of cleaning and unifying complex data sets for easy access and analysis.
Machine Learning (ML)
Empowers machines to learn from data and make decisions with minimal human intervention.
Supervised Learning
ML algorithms that learn from labeled training data.
Unsupervised Learning
ML algorithms that infer patterns from unlabeled data.
Reinforcement Learning
Algorithms that learn by interacting with an environment to achieve specific objectives.
Deep Learning (DL)
A subset of machine learning that uses neural networks to model and understand complex patterns.
Neural Networks
Computational models inspired by the human brain structure and function.
Computer Vision
Enables machines to interpret and make decisions based on visual data.
Speech Recognition
Translates spoken words into text using DL techniques.
MLOps
Combines Machine Learning, DevOps, and data engineering to streamline and productionize machine learning systems.
Model Deployment
Processes to integrate ML models into production environments.
Monitoring & Maintenance
Monitoring model performance and maintaining operational stability.
Continuous Integration and Delivery (CI/CD)
Automated testing and deployment of ML models.
NLP (Natural Language Processing)
AI subfield focused on enabling computers to understand, interpret, and generate human language.
Text Analysis
Techniques for extracting information and insights from text.
Language Translation
Automated translation of text or speech from one language to another.
Sentiment Analysis
Determining the attitude or emotion of the speaker or writer in the text.