Roadmap for AI Engineers – KDnuggets

Curious about how AI models like GPT-4o, image recognition, and speech translation are built? It might seem magical, but it’s the result of years of research by machine learning engineers and data scientists. Today, AI engineering is a highly sought-after career, with companies offering salaries of around $150,000 annually. With two months left in 2024, now is the perfect time to dive into AI and start creating your own models and applications.

In this blog, we will learn about 10 steps10 steps to becoming a professional AI engineer. First, we will familiarize ourselves with AI concepts, learn programming, understand the math behind machine learning models, build and evaluate our first model. After that, we will explore areas like computer vision, NLP, and reinforcement learning. Finally, we will advance to topics such as large language models, AI frameworks, and deploying models into production.

1. Best VPN for Engineers – 3 Months Free – Stay secure online with a free trial

2. Best Project Management Tool for Tech Teams – Boost team efficiency today

4. Best Password Management for Tech Teams – zero-trust and zero-knowledge security

1. Introduction to AI

Watch a YouTube video on fundamental machine learning, take a few courses, and get familiar with machine learning and data science terms and concepts. Before diving into programming and mathematics, You need basic information about AI Python packages, various machine learning models, and data processing techniques. 

2. Programming

Learn Python by taking interactive courses from DataCamp and Codecademy. It is necessary for you to master Python before you try to build your first machine learning model. With a stronger foundation, you will have a clear understanding of the code mentioned in the documentation of AI frameworks. Learn about standard practices in programming and how to write clean code. You will also learn to use Git and publish your Python projects on GitHub. 

3. Mathematics

You will learn linear algebra to deal with vectors and matrices. It is used for data transformation, neural networks, and places where matrix multiplication is performed. Then, you will learn calculus for optimization algorithms. Probability and statistics are used for many machine learning algorithms, including Bayesian networks and decision trees. In short, mathematics is the backbone of modern AI. If you master this, you can easily build your own model.

4. Machine Learning

In this step, you will learn about classification, regression, and clustering types of machine learning models. You will also learn to load and process structured datasets, build and train machine learning models, and then evaluate the model performance on various metrics. You will learn about Scikit-learn, Pandas, NumPy, Matplotlib, and other machine-learning frameworks. You won’t learn anything advanced like image classification; you will only learn about simple machine-learning problems.

5. Computer Vision

Computer vision is a huge field. In this field, you will learn about image classification, object detection, image segmentation, object recognition, scene reconstruction, activity recognition, and more. You will learn to load image data, process it, augment it, and build neural networks to train the model on various computer vision problems. Additionally, you will also learn about pre-training transformer models, learning to fine-tune them on custom datasets, and building a better AI application for a specific use case.

6. Natural Language Processing

Now, we will move on to learning how to handle speech and text data. You will learn to process speech and text datasets, train the model for classification, speech recognition, text generation, and many more problems. You will also learn to perform data analysis to understand your data better, so that you can clean it and process it to improve the model performance. Similar to computer vision models, the most models you will use are pre-trained models. You will learn to fine-tune and evaluate them. Natural Language Processing (NLP) is a prerequisite to large language models (LLMs).

7. Reinforcement Learning

Reinforcement learning is a type of machine learning that doesn’t rely on structured data. It differs from other machine learning models in that it trains an agent to learn about its environment and achieve goals. These goals can range from moving an object from one place to another to surviving a stage in a game. Learning about reinforcement learning is crucial because it’s fundamental to the future of AI. Suppose you want to build a General AI system. In that case, you will need to incorporate reinforcement learning into your application to learn from the environment and make better decisions rather than solely relying on the provided dataset.

8. Generative AI

Most non-tech-savvy individuals are only familiar with Generative AI because of DALL·E and ChatGPT. However, generative AI encompasses much more than just chatbots and image generation. You will learn to build your own model from scratch, curate the dataset, and perform model evaluations, which can be quite challenging. The best part of learning generative AI is that you will gain knowledge about the latest language model technologies, frameworks, and models. Additionally, you will learn about language models and image generation model architectures to enhance your understanding. 

9. AI Frameworks

Here are a few frameworks that you need to master if you want to get hired by a company as an AI engineer:

• Scikit-learn
• XGBoost
• OpenCV
• PyTorch
• Keras
• Hugging Face Transformers, Accelerate, TRL, and Bitsandbytes
• Open Neural Network Exchange (ONNX)
• LangChain
• MLFlow
• DreamBooth

Mastering these tools will help you build complex AI models in no time.

10. Machine Learning Operations

The last part is all about deploying these machine learning models locally, in the cloud, and using Docker. You will learn about automation, orchestration, model serving and deployment, and model monitoring tools. Most importantly, you learn about Docker containers, FastAPI, and Google Cloud. Companies nowadays want AI engineers to build AI models and then deploy them to the cloud.

Conclusion

By following these 10 steps, you will learn everything about what it takes to be an AI engineer. After learning the basics of machine learning in step 4, you can choose your niche, such as computer vision or NLP or time series forecasting. However, I highly recommend learning about every type of model and gaining experience.

If you want to get better at building AI applications, I highly recommend starting to work on a project from day one and showcasing your project on GitHub or Kaggle. Write about it on LinkedIn so that you are also building your brand as an AI engineer. This way, you will start getting offers from recruiters from day one. Keep learning and keep building and evolving. When you are able to successfully build and deploy your machine learning project, you won’t even have to find a job—people will reach out to you, as there is a huge vacuum in this field where most of them only know how to build the model and not successfully deploy it to production.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.