Tools Every AI Engineer Should Know: A Practical Guide

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AI is one of the most popular things in the tech industry. Like data engineering, AI engineering has become popular due to this increasing demand for AI products.

But to be an AI engineer, what tools must you know? This list, which includes AI tools, might have been expanding because of increasing popularity, but you must keep updated and gain skills about these tools.

In this article, we will explore these tools together, but first, let’s focus on AI Engineering; let’s start!

 

What is an AI Engineer?

 

An AI engineer is a person who builds, maintains, and optimizes AI systems or applications. Such practices require experts who integrate software development with machine learning to build intelligent systems designed to perform human-like tasks.

They design predictive models and develop autonomous systems, so their knowledge includes not just theoretical knowledge but practical skills that can be applied to real-world problems.

Of course, to do that, they need to know how to program systems, which requires programming knowledge.

 

Programming Knowledge

 

Strong programming knowledge is a must for an AI engineer to shine. That’s why it is important to excel at a few key languages.

 

Python

Python has dynamic libraries, such as TensorFlow and PyTorch, that are great for AI model training. These libraries have active communities that keep them updated.

This high-level, general-purpose programming that allows freedom for rapid prototyping and fast iteration over the codes is what makes Python a top choice amongst AI engineers.

First, here are the top 30 Python interview questions and answers.

 

R

Another important language is R, especially in statistical analysis and data visualization. It has strong data-handling capabilities and is used in academia and research. R is a tool for heavy statistical tasks and graphics requirements.

You might see many arguments between R and Python when people discuss finding the best programming language for data science. Data Science might be a different field. However, to become an AI engineer, you must do many tasks that a Data Scientist does.

That’s why you might need to find an answer to this old debate too: which is better, R or Python? To see the comparison, check out this one.

 

Java

Java has been used to build large systems and applications. It is not as popular for AI-specific tasks but is important in deploying AI solutions on existing enterprise systems. Java’s power and scalability make it a useful weapon for an AI engineer.

 

SQL

You cannot manage databases without SQL. As an AI engineer, working with relational databases will be most of your work because it involves dealing with and cleaning large datasets.

This is where SQL comes in to help you extract, manipulate, and analyze this data quickly. Doing so helps provide clean, thinned-out structured knowledge that you can forward to your models.

Here is the ultimate guide to the SQL Questions you must prepare.

 

Machine Learning

 

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Machine learning might be the core part of this operation. But before learning machine learning, you need to know about math, statistics, and linear algebra.

 

Math

Understanding machine learning methods depends on a strong mathematical foundation. Important sections cover probability theory and calculus. While probability theory clarifies models like Bayesian networks, calculus supports optimization methods.

Check out this one to practice your knowledge of Math with Python and learn more about coding libraries used in Math.

 

Statistics

Statistics are essential for interpreting data and verifying models. Hypothesis testing, regression, and distribution are the foundations of a statistical study. Knowing these lets you assess model performance and make data-driven decisions.

You can start learning from commonly used statistical tests in Data Science or basic types of statistical tests in Data Science. As you know, you should know the same concepts in both data science and AI engineering. You can check more statistical articles from here.

 

Linear Algebra

Linear algebra is the language of machine learning. It is applied in methods using vectors and matrices, which are basic in data representation and transformations.

Understanding algorithms such as PCA (Principal Component Analysis) and SVD (Singular Value Decomposition) depends on a knowledge of key ideas such as matrix multiplication, eigenvalues, and eigenvectors.

Here is the best video series from 3Blue1Brown, where you can understand linear algebra completely.

 

Big Data

 

AI Solutions rely on the AI scene, which big data supports. Specifically, it talks about the terabytes of data generated every day. Artificial intelligence designers need to handle this data appropriately and effectively. The below examples showcase big data services.

 

Hadoop

Hadoop is an open-source software framework for storing and processing large datasets in a distributed file system across computer nodes. It scales to run on thousands of servers, offering local computation and storage, making it ideal for high-scale training.

This architecture has capabilities that allow for efficient handling of big data and enable it to be reliable and scalable.

 

Spark

Apache Spark is a fast and general-purpose cluster computing system for big data. It provides high-level APIs in Java, Scala, Python, and R and an optimized engine that supports general execution graphs. Benefits are;

• Good Performance
• Easy to use ( Spark) 
• Capable of processing huge amounts of data at lightning speed and compatible with various programming languages

It is a powerful weapon in the hands of an AI engineer. If you want to know more about PySpark, a Python Apache Spark interface, check out “What Is PySpark?”.

 

NoSQL Databases

They are designed to store and process vast masses of unstructured data, called NoSQL databases—e.g., MongoDB or Cassandra. Unlike traditional SQL’s, NoSQL databases are scaleable and flexible, so you can store data more efficiently, fitting into complex data structures for AI.

This, in turn, allows AI engineers to store and better use large datasets, which is necessary to produce powerful prediction models ( machine learning) and decision-making that requires fast data processing speed.

If you want to know more about Big Data and how it works, check out this one.

 

Cloud Services

 

Many Cloud Services are available, but it’s best to familiarize yourself with the most used ones.

 

Amazon Web Services (AWS)

AWS offers a wide range of cloud services, from storage to server capacity and machine learning models. Key services include:

• S3 (Simple Storage Service): For large dataset storage.
• EC2 (Elastic Compute Cloud): For scalable computing resources.

 

Google Cloud Platform (GCP)

GCP is tailored for AI and big data. Key services include:

• BigQuery: A fully managed data warehouse for executing SQL queries quickly using Google’s infrastructure.
• TensorFlow and AutoML: AI and machine learning tools for creating and deploying models.

 

Microsoft Azure

Azure provides several services for AI and big data, including:

• Azure Blob Storage: Massively scalable object storage for virtually unlimited unstructured data.
• Azure Machine Learning: Tools for hosting various ML models, including fast training or custom-coded models.

 

Practice: The Way of Becoming a Master

 

AI Mastery is More than Theory Projects are important to gain practical experience. So here are a few shortcuts to practice and improve your AUTHORICIENT skills:

 

Do Data Projects

Apply your skills to real-world data projects. For example, predict DoorDash delivery duration prediction. This involves:

• Collecting delivery time data.
• Feature Engineering
• Building a predictive model in both Machine Learning and Deep Learning

These projects give hands-on experience in data fetching, cleaning, exploratory analysis, and modeling. They prepare you for real-life problems.

 

Kaggle Competitions

Kaggle competitions are the best way of cracking Data projects if you are at the beginning of the road. They will not only give a lot of datasets, but some competitions might be a real motivation for you because some offer more than $100K.

 

Open Source Contributions

Open-source contributions can be the best way to feel confident and competent. Even beginner programmers can find bugs in very complex codes.

For instance langchain, it is a way of using different language models together. Feel free to visit this open-source GitHub repository and start exploring.

If you have trouble loading or installing any of their features, report an issue and be active in the community.

 

Online Courses and Tutorials

If you want to see a program tailored to your skill set and earn a certification from well-known institutes, feel free to visit websites like Coursera, Edx, and Udacity. They have many machine learning and AI courses that can simultaneously give you theoretical and practical knowledge.

 

Final Thoughts

 

In this article, we explored what AI Engineers mean and which tools they must know, from programming to cloud services.

To wrap up, learning Python, R, big data frameworks, and cloud services equips AI engineers with the tools needed to build robust AI solutions that meet modern challenges head-on.

 
 

Nate Rosidi is a data scientist and in product strategy. He’s also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.