Becoming an AI engineer requires a strong foundation in various technical skills. Some core technical skills essential for pursuing careers in AI engineering are as below :
Continuous learning is crucial in the fast-evolving field of AI, so staying updated with the latest research, tools, and technologies is essential for AI engineers. Building a diverse skill set across these areas will position you for success in AI engineering roles.
1. Machine Learning Engineer
Machine learning engineers act as critical members of the data science team. Their tasks involve researching, building, and designing the artificial intelligence responsible for machine learning and maintaining and improving existing artificial intelligence systems. Machine learning engineer jobs are best for anyone with a background that combines data science, applied research, and software engineering. While job responsibilities for machine learning engineers will differ, they often include:
· Implementing machine learning algorithms
· Running AI systems experiments and tests
· Designing and developing machine learning systems
· Performing statistical analysis
2. AI Engineer
AI engineers are professionals who use AI and machine learning techniques to develop applications and systems that help organisations become more efficient. AI engineering focuses on developing the tools, systems, and processes that enable AI to be applied to real-world problems. Algorithms are “trained” by data, which helps them to learn and perform better. AI engineers can help cut costs, increase productivity and profits, and make business recommendations. Here are some specific tasks and responsibilities of an AI engineer:
· Create and manage the AI development and production infrastructure
· Conduct statistical analysis and interpret the results to guide and optimize the organization’s decision-making process
· Automate AI infrastructures for the data science team
· Build AI models from scratch and help product managers and other stakeholders with analysis and implementation
· Transform machine learning models into APIs that can be integrated with other applications
· Collaborate across teams to help with AI adoption and best practices
3. Data Scientist
Data Scientists collect data, analyze it, and glean insights for a wide range of purposes. They use various technology tools, processes, and algorithms to extract knowledge from data and identify meaningful patterns. This could be as basic as identifying anomalies in time-series data or complex as predicting future events and making recommendations. The primary skills expected of a data scientist are:
· Knowledge of statistics, mathematics, data science and computer science
· Understanding of unstructured data and statistical analysis
· Experience with cloud tools - AWS or GCP
· Programming skills with Python, SQL.
· Extensive knowledge of frameworks such as Tensorflow or PyTorch
4. ML Ops Engineer
MLOps Engineers build, maintain, and optimize machine learning solutions. They ensure that overall performance of the AI solution and continuously monitor for performance. They have a wide range of skills, including knowledge of data science, software engineering, and domain expertise in the industry in which they work. They also need to be able to understand business problems and come up with solutions to them using machine learning techniques. What do we mean by that? Once the data science team has built the model, the MLOps engineer enables the successful operation of the model through the following:
· Automating machine learning model deployment
· Setting up monitoring for the ML pipeline
· Automating CI/CD pipelines to account for data, code, and model changes
· Setting up automated model retraining
· Deciding on the level of automation required
1. Media, Entertainment and OTT
2. Gaming
3. Banking and Financial Services
4. Logistics and transportation
5. Retail
6. Healthcare
7. Food Tech
8. Travel
9. Real estate
10. Manufacturing
11. Automotive
12. Education
13. Fashion
14. ERP
15. Hospitality
16. Robotics
17. Military and warfare
18. Toys
19. Consumer Electronics
20. Security and Surveillance
21. Business Applications
22. Marketing
23. Energy and Utilities - Oil, Gas and Electricity
24. Social Media
25. Life sciences
26. Networks
27. Telecommunications
28. Renewable Energy - Solar, Water, Wind
29. Aviation
30. Drugs and Pharmacy
Common applications of AI across major industries include:
1. How long does it take to learn AI and become a decent AI Engineer ?
2. Where do i start ?, Do i need an software engineering degree to become an AI Engineer ?
3. I am currently a working professional in the software industry wanting to switch careers and learn data cience, what are my options ?
4. I am an engineer from a non-computing background, Can i become an AI Engineer ?
5. I am a test engineer, should i learn data science or is it smart to learn generative ai tools for testing ?
6. I am a graduate student - (Engineering / Science / Management) can i learn AI ?
7. Is Go Academy's AI Course a Bootcamp ?
8. What kind of hands of projects am i going to build with Go Academy's AI bootcamp course ?
You’ll need to build your technical skills, including knowledge of the tools that AI engineers typically use. You’ll learn programming with Python to get the hang of the computational thinking , slice, dice and transform data and implement models
Probability, statistics, and linear algebra: These are needed to implement different AI, machine learning models and also learn the foundations of data science and thinking in data
AI engineers work with large amounts of data, so you’ll be required to know SQL, NoSQL Dbs, Caches, Cloud Computing (CPU, GPU), Moving data as Batch, Processing Data as Streams, Data pipelines, ETL and a whole bunch of tools and techniques to manage it all which we will teach you
You’ll want to understand machine learning algorithms such as linear regression and Naive Bayes, as well as deep learning algorithms such as recurrent neural networks and generative adversarial networks, and be able to implement them with a framework. Common AI frameworks that we teach include PyTorch and TensorFlow
This is where you will put it all together with sample projects to chose from and learn the art of building a production ready AI model which is trained and is also used for inference on modern data architectures and pipelines. You can choose your sample projects from the below
* Computer Vision
* NLP for Marketing
* Recommendation Systems
* Automatic Test Data Generation for Test Cases
* Automatic Test Case Generation for Functional Tests
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