AI Engineer vs. ML Engineer: Key Differences
The demand for artificial intelligence (AI) and machine learning (ML) engineers is high across all industries. While both these roles are related, they are not the same. An AI engineer needs different skill sets and qualifications compared to ML engineer. Also, AI engineer has a different job role than the ML engineer.
Here, we will delve deeper into AI and ML engineering. We will unravel their definitions, qualifications, skills, etc., for both professionals.
Who is an AI Engineer?
An Artificial intelligence engineer conceives, develops, and executes solutions that allow machines to mimic human intelligence and abilities. They extend their services to various industries like computer science in retail, manufacturing, technology, chatbots in customer service, predictive analytics in finance, etc.
Who is an ML Engineer?
Machine learning is a part of AI. An ML engineer develops machine learning algorithms enabling the computers to make data-based decisions. These professionals are adept at creating models to analyze data, make predictions, automate tasks, and help managers make data-driven decisions. Due to a deep understanding of data science and a firm grasp of programming languages, ML engineers can perform their duties impeccably.
AI Engineer: The Duties
An AI engineer has vast KRAs and is expected to cater to the broad spectrum of machine intelligence. A career in AI needs you to fulfil the following duties.
Develops Algorithm: AI engineers build and implement algorithms to enable the machines to collect data.
Data analysis: AI engineers deal with huge quantities of data and draw patterns. They gather meaningful insights to predict future trends.
Model deployment: AI engineers can seamlessly integrate AI models into different applications. For example, they can deploy chatbots and help the customer service department.
NLP: Using Natural language processing, AI engineers can allow the machines to analyze a language and respond to prompts and questions.
Robotics: AI in robotics is widely used. AI engineers make robots perform critical tasks like assembling parts, lifting heavy objects, etc.
ML Engineer: The Duties
An ML engineer spends the day around data and implementing algorithms. Their responsibilities include the following.
Develops Algorithm: ML engineers also make algorithms and generate data patterns.
Data Assessment: ML engineers evaluate and interpret data to generate key insights.
Feature engineering: Feature engineering means data creation and augmenting the model performance. The feature can extract data and help make seasonal predictions.
Model evaluation: ML engineers can determine the accuracy and efficiency of the model.
Data Processing: They clean and structure raw data and improve predictions.
Supervised and Unsupervised learning: ML engineers might train supervised and unsupervised models using labeled data and without using predefined labels, respectively.
AI Engineer: The Skills
AI engineering professionals need a vast set of skills and familiarity with technical frameworks:
- They must follow ethical considerations while developing and deploying AI.
- AI engineers must also be adept in using NLP and allow the system to interpret human language.
- They need Computer vision (CV) skills to allow machines to interpret visual data.
- Programming skills like Python help and develop complex algorithms.
- Other skills include AI model deployment, conversational AI development, gradient boosting frameworks, etc.
ML Engineer: The Skills
To excel in their career, ML engineers may need the following skills:
- Feature engineering skills allow them to improve model performance.
- Skills like model versioning, continuous integration, automated pipelines, etc., are imperative for ML engineers.
- ML Engineer needs Databricks skills to use a unified analytics platform.
- A/B testing is important for ML engineers to compare two platform versions and determine the right one.
AI Engineer: The Qualifications
AI engineers need a bachelor’s or master’s degree in computer science, mathematics, statistics, data science, or related fields. To make yourself stand out, you must consider getting an AI engineer certification.
ML Engineer: The Qualifications
ML engineers must have a bachelor’s or master’s degree in computer science, data science, math, statistics, or related disciplines. It is also vital to have artificial intelligence and machine learning certification to demonstrate your expertise in designing, implementing, and deploying ML solutions.
AI Engineer vs. ML Engineer
With the huge technological landscape evolution, it is imperative to understand the differences between the two. This can help you engage the right talent in the right project. While the skills, roles, focus areas, etc., overlap in AI and ML engineering, there are stark differences between the two.
Working Area
AI engineers have a broader set of tasks like handling vast types of machine intelligence, neural networks, developing AI models, etc. ML engineers mostly focus on creating ML algorithms and models. They help to make predictions by assessing large volumes of data.
Tools Used
Both AI and ML professionals use basic programming languages like Python, etc. ML engineers are better equipped to use specific libraries and machine learning frameworks like TensorFlow or Scikit-learn.
Application
AI engineers perform their duties using applications like robotics, natural language processing, etc. ML engineers use data-driven applications like predictive analytics, classification tasks, etc.
Data Science
While AI and ML engineers use data science in their jobs, ML engineers use it more often and deeply. Since they work more closely with data analysis, research, preprocessing, model evaluation, etc., they focus more on data science.
Wrapping Up
In this highly technology-driven world, the need and demand for artificial intelligence and machine learning engineering are substantial. Both roles have many similarities but key differences in job duties, skills, qualifications, etc. As an aspiring AI or ML engineer, you must understand the differences and take your pick.
