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. Just like data engineering, ai engineering has become popular due to this increasing demand for ai products.

But to be an ai engineer, what tools should you know? This list, which includes ai tools, may have expanded due to their growing popularity, but you must stay updated and acquire skills on these tools.

In this article, we will explore these tools together, but first, let’s focus on ai engineering – let’s get started!

What is an ai engineer?

An ai engineer is a person who creates, maintains, and optimizes ai systems or applications. These practices require experts who integrate software development with machine learning to create intelligent systems designed to perform human-like tasks.

They design predictive models and develop autonomous systems, so their knowledge includes not only theoretical knowledge but also 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

For an ai engineer to stand out, it is essential to have solid programming knowledge. That is why it is important to excel in some key languages.

Piton

Python has dynamic libraries, such as TensorFlow and PyTorch, that are great for training ai models. These libraries have active communities that keep them up to date.

This high-level, general-purpose programming that allows freedom for rapid prototyping and fast iteration on code is what makes Python the top choice among ai engineers.

First, here are the ai+engineer+tools” target=”_blank” rel=”nofollow noopener”>Top 30 Python Interview Questions and Answers.

R

Another important language is R, especially in statistical analysis and data visualization. It has great data handling capabilities and is used in academic and research settings. R is a tool for heavy statistical tasks and graphical requirements.

You might see a lot of discussions between R and Python when people talk about finding the best programming language for data science. Data science might be a different field. However, to become an ai engineer, you need to perform many tasks that a data scientist does.

So, you might also need to find an answer to this age-old debate: which is better, R or Python? For the comparison, check out this article ai+engineer+tools” target=”_blank” rel=”nofollow noopener”>one.

Java

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

SQL

It is not possible to manage databases without SQL. As an ai engineer, working with relational databases will be the bulk of your job as it involves managing and cleaning large data sets.

This is where SQL comes in to help you extract, manipulate, and analyze this data quickly. This way, you can get structured, clean, and detailed knowledge that you can feed to your models.

ai+engineer+tools” target=”_blank” rel=”nofollow noopener”>Here is the ultimate guide to SQL questions You must prepare yourself.

Machine learning

<img decoding="async" class="aligncenter size-full wp-image-178488" alt="Machine learning that every ai engineer should know” width=”3291″ height=”2197″ srcset=”https://technicalterrence.com/wp-content/uploads/2024/08/1723849954_479_Tools-Every-AI-Engineer-Should-Know-A-Practical-Guide.png 3291w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_AI_Engineer_tools_2-300×200.png 300w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_AI_Engineer_tools_2-1024×684.png 1024w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_AI_Engineer_tools_2-768×513.png 768w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_AI_Engineer_tools_2-1536×1025.png 1536w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_AI_Engineer_tools_2-2048×1367.png 2048w” data-lazy-sizes=”(max-width: 3291px) 100vw, 3291px” src=”https://technicalterrence.com/wp-content/uploads/2024/08/1723849954_479_Tools-Every-AI-Engineer-Should-Know-A-Practical-Guide.png”/><img decoding="async" class="aligncenter size-full wp-image-178488" src="https://technicalterrence.com/wp-content/uploads/2024/08/1723849954_479_Tools-Every-AI-Engineer-Should-Know-A-Practical-Guide.png" alt="Machine learning that every ai engineer should know” width=”3291″ height=”2197″ srcset=”https://technicalterrence.com/wp-content/uploads/2024/08/1723849954_479_Tools-Every-AI-Engineer-Should-Know-A-Practical-Guide.png 3291w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_AI_Engineer_tools_2-300×200.png 300w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_AI_Engineer_tools_2-1024×684.png 1024w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_AI_Engineer_tools_2-768×513.png 768w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_AI_Engineer_tools_2-1536×1025.png 1536w, https://www.kdnuggets.com/wp-content/uploads/Rosidi_AI_Engineer_tools_2-2048×1367.png 2048w” sizes=”(max-width: 3291px) 100vw, 3291px”/>

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Machine learning can be the core of this operation, but before learning it, you need to know mathematics, statistics and linear algebra.

Math

Understanding machine learning methods requires a solid mathematical foundation. Key sections cover probability theory and calculus. While probability theory clarifies models such as Bayesian networks, calculus underpins optimization methods.

Verify ai+engineer+tools” target=”_blank” rel=”nofollow noopener”>this one to practice your Math knowledge with Python and learn more about coding libraries used in Math.

Statistics

Statistics is essential for interpreting data and verifying models. Hypothesis testing, regression, and distribution are the foundations of a statistical study. Knowing them allows you to evaluate model performance and make data-driven decisions.

You can start learning fromai+engineer+tools” target=”_blank” rel=”nofollow noopener”> commonly used statistical tests in Data Science or ai+engineer+tools” target=”_blank” rel=”nofollow noopener”>Basic types of statistical tests in data scienceAs you already know, you need to know the same concepts in both data science and ai engineering. You can check out more statistical articles at ai+engineer+tools” target=”_blank” rel=”nofollow noopener”>here.

Linear algebra

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

Understanding algorithms such as PCA (principal component analysis) and SVD (singular value decomposition) depends on knowledge of key ideas such as matrix multiplication, eigenvalues, and eigenvectors.

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

Big data

ai solutions are based on the ai ​​environment, which is supported by big data. Specifically, this is the terabytes of data that are generated every day. ai designers must manage this data appropriately and efficiently. The following examples show big data services.

Hadoop

Hadoop is an open-source software framework for storing and processing large data sets in a distributed file system across computing nodes. It is scalable to run on thousands of servers and offers local computing and storage, making it ideal for large-scale training.

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

Spark

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

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

It is a powerful weapon in the hands of an ai engineer. If you want to learn more about PySpark, an Apache Spark interface for Python, check out “ai+engineer+tools” target=”_blank” rel=”nofollow noopener”>What is PySpark?“.

NoSQL databases

They are designed to store and process large amounts of unstructured data, called NoSQL databases, for example MongoDB or Cassandra. Unlike traditional SQL databases, NoSQL databases are scalable and flexible, so data can be stored more efficiently and are suited to complex data structures for ai.

This, in turn, enables ai engineers to better store and utilize large data sets, which is necessary for producing 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 this out ai+engineer+tools” target=”_blank” rel=”nofollow noopener”>one.

Cloud Services

There are many cloud services available, but it is best to familiarize yourself with the most commonly used ones.

amazon Web Services (AWS)

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

• S3 (Simple Storage Service): For storage of large data sets.
• EC2 (Elastic Computing Cloud): For scalable computing resources.

Google Cloud Platform (GCP)

GCP is built for ai and big data. Key services include:

• Great question: A fully managed data warehouse to run SQL queries quickly using Google's infrastructure.
• TensorFlow and AutoML: artificial intelligence and machine learning tools to create and deploy models.

Microsoft Azure

Azure offers several services for ai and big data, including:

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

Practice: The Path to Becoming a Master

Mastering ai is more than just theory Projects are important for gaining practical experience. Here are some shortcuts to practice and improve your AUTHORITY skills:

Carry out data projects

Apply your skills to real-world data projects. For example, predicting ai+engineer+tools” target=”_blank” rel=”nofollow noopener”>DoorDash Delivery Duration PredictionThis implies:

• Collection of delivery time data.
• Feature engineering
• Building a predictive model in both Machine Learning and Deep Learning

These projects provide hands-on experience in data acquisition, cleaning, exploratory analysis, and modeling, preparing you to tackle real-world problems.

Kaggle Competitions

Kaggle Competitions They are the best way to start data projects if you are just starting out. Not only will they provide you with a huge amount of data sets, but some competitions can be a real motivation for you because some offer over $100,000.

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 code.

For example ai/langchain” target=”_blank” rel=”nofollow noopener”>langchain chainIt's a way to use different language models together. Feel free to check out this open source GitHub repository and start exploring.

If you have any issues loading or installing any of its features, please report a problem and actively participate in the community.

Online Courses and Tutorials

If you want to see a program tailored to your skills and get a certification from recognized institutes, feel free to visit websites like Cursor, Edxand UdacityThey have many courses on machine learning and artificial intelligence that can give you theoretical and practical knowledge simultaneously.

Final thoughts

In this article, we explore what it means to be an ai engineer and what tools they should know, from programming to cloud services.

In short, learning Python, R, big data frameworks, and cloud services provides ai engineers with the tools to build robust ai solutions that address modern challenges.

twitter.com/StrataScratch” rel=”noopener”>twitter.com/StrataScratch” target=”_blank” rel=”noopener noreferrer”>Nate Rosidi Nate is a data scientist and product strategy specialist. He is also an adjunct professor of analytics and is the founder of StrataScratch, a platform that helps data scientists prepare for their interviews with real interview questions from top companies. Nate writes about the latest trends in the job market, provides interview tips, shares data science projects, and covers all things SQL.