AI is transforming the areas of software development, data analytics, and business operations in the market. Companies are looking for people who can build machine learning models, write code, and create reliable systems.
To work in AI, you need skills such as Python programming, machine learning, data processing, model evaluation, and deployment. Some jobs in this area require knowledge of generative AI, retrieval augmented generation (RAG), and automation of workflows.
The programs below cover the entire AI process from beginner to production engineering.
Overview: 5 AI and Production Engineering Programs
| # | Program | Provider | Duration | Best Aligned With |
| 1 | Certificate Program in Artificial Intelligence and Agentic AI Engineering | Johns Hopkins University | 21 weeks | MLOps, LLMOps, RAG, deployment |
| 2 | Post Graduate Program in Artificial Intelligence and Machine Learning | The McCombs School of Business at The University of Texas at Austin | 23 weeks | Python, ML, GenAI, deployment |
| 3 | Applied Machine Learning and AI | Cornell University | 4 months | ML workflows, Python, evaluation |
| 4 | Machine Learning Methods | University of California San Diego Division of Extended Studies | 12 months | Neural networks, deep learning |
| 5 | Machine Learning DevOps Engineer | Udacity | 63 hours | CI/CD, ML pipelines, monitoring |
Certificate Program in Artificial Intelligence and Agentic AI Engineering- Johns Hopkins University
The Certificate Program in Artificial Intelligence and Agentic Engineering by Johns Hopkins University centers around the concept of producing AI systems that are ready for production.
The AI engineering course comprises automated pipelines, infrastructure on the cloud, monitoring of models, and deployment of agentic AI.
Duration and Delivery: The course spans over a duration of 21 weeks and is available online with the recordings of lectures, live sessions, as well as projects.
Certification: Certificate of Completion and 16 CEUs provided by Johns Hopkins University.
Features of the Course: Python, RAG, MLOps, LLMOps, CI/CD, Drift detection, LangChain, LangGraph, and tools related to cloud AI.
Learning Outcomes: In this course, students learn how to deploy applications based on generative AI and monitor production through multiple individual projects and modules.
Why should you choose this program:
- The program focuses on deployment, monitoring, testing, and reliability of systems.
- The program covers the engineering techniques for the implementation of AI in enterprises.
Post Graduate Program in Artificial Intelligence and Machine Learning- The McCombs School of Business at The University of Texas at Austin
The McCombs School of Business at The University of Texas at Austin offers a Post Graduate Program in Artificial Intelligence and Machine Learning which includes Python programming, machine learning, generative AI, and application of AI-based solutions at its core.
The artificial intelligence course ensures that theory is coupled with practice using live examples.
Delivery mode and duration: The program is offered through the online and self-paced format and takes 23 weeks with weekly guidance and assignments.
Certification: The completion certificate and CEUs will be issued by Texas McCombs.
Program outline: There are topics on Python, data science, machine learning, neural networks, natural language processing, RAG subsystem, LangChain, and Docker among others.
Outcome: Capstone projects leading to predictive models and RAG-based products.
Why should you choose the program:
- Enrolling into this program will help you learn the theory and practice simultaneously.
- Mentormap will help students avoid the typical learning problems while doing their projects.
Cornell University Applied Machine Learning and AI course
The Applied Machine Learning and AI course at Cornell University is intended for professionals who are already familiar with Python and are looking to deepen their technical capabilities before entering a graduate program for AI engineering.
Format and Duration: This is a four-month-long online course that includes some practical components.
Certification: Learners will receive Cornell University eCornell Certification at the end of the course.
Course Features: The course content encompasses Python, machine learning algorithms, data preprocessing, improving models, and responsible AI.
Outcomes: Participants learn how to create machine learning processes, evaluate models, and improve them.
Reasons to Choose this Course:
The training covers the basics of machine learning – key knowledge for further studies in AI.
Practical assignments provide hands-on experience in solving real-life problems.
Machine Learning Methods- University of California San Diego Division of Extended Studies
The University of California San Diego Division of Extended Studies offers a Machine Learning Methods certificate program that delves into deep learning techniques.
This program is tailored for developers, engineers, and analysts who want to strengthen their technical expertise.
Delivery Format and Duration: It is an online program that lasts approximately a year.
Certification: Machine Learning Methods Certificate.
Program Features: This course covers subjects like linear algebra, statistics, supervised learning, neural networks, deep learning, and model evaluation.
Expected Outcomes: Graduates of this program are able to select algorithms, build models, evaluate performance, and use deep learning.
Why should you choose this course:
- The course reinforces the mathematical basis and technical proficiency in the field of machine learning.
- The curriculum supports professionals interested in model development and evaluation.
Machine Learning DevOps Engineer- Udacity
The Machine Learning DevOps Engineer Nanodegree at Udacity teaches how to implement machine learning models in production.
This course is designed for individuals who are knowledgeable about machine learning and would like to learn how to automate, test, deploy, and monitor ML projects.
Delivery and Duration: The course is available online on a self-paced basis and has a total of 63 hours of lessons.
Certification: Graduates receive a Udacity Nanodegree Program Certificate.
Course Content: Python programming, Git, automated tests, MLflow, CI/CD, ML pipelines, deployment, and monitoring.
Course Outcomes: Participants learn how to create maintainable ML code and automate their service pipeline.
Why choose this course?
- Its focus is on practical skills associated with engineering production ML systems.
- Projects introduce automated deployment, reproducibility, and model maintenance.
When opting for AI courses, one should keep in mind their qualifications. For example, beginners must look for courses that cover model training, Python programming, etc. On the contrary, advanced users have plenty of options, such as studying automation, troubleshooting, etc.
Building an effective AI model requires more than just learning how to train it. There is also the need to test the model, ensure safety, and monitor the model consistently.
