According to deeplearning.ai, only 22% of companies using machine learning have successfully deployed a model. The need for AI/ML Engineers is growing exponentially as the industry moves towards Data-centric AI. AI/ML Engineering (MLOps) is at the intersection of Machine Learning, DevOps, and Data Engineering. It is a critical role that makes sure the AI products get deployed in production in a scalable and reliable way.

If you’re want to take your AI/ML skills to the next level, this AI Engineer Bootcamp was created for professionals like you who want to sharpen their skills in deep learning, computer vision, NLP, big data, and MLOps.

Lo que aprenderá

We understand that there are theoretical underpinnings to cover. Most importantly, we know that we need to head beyond the classroom to truly work in this field. For example, by rolling up our sleeves and finding a way to package our work, tuning and molding a model to different setups, whether they be predictions with a CPU, an industrial-grade embedded system, or a beefy datacenter GPU like the A100.

In this advanced program, you will learn deep learning techniques, use cases in computer vision and NLP, and the engineering and operations side which allows you to appreciate the entire lifecycle of ML projects from data preparation to model deployment and monitoring.

Resumen
- Experiencia real en proyectos para clientes
- Asesoramiento profesional personalizado por parte de expertos
- Recibir comentarios constructivos sobre proyectos y entrevistas
- Ser remitido a empresas de contratación

About the Program
Whether you want to engage in a custom capstone project or engage with scenarios that mirror our work in consultancy we have you covered. In this 6- month part-time bootcamp, you will get fully immersed in learning the cutting-edge machine learning and deep learning techniques as well as end-to-end machine learning operations.

Conoce a los estudiantes que avanzaron en sus carreras con WeCloud Data

Joe Xu
Data Scientist at Chapeau! AI Agency
As a fresh graduate from the Data Science MS program, my knowledge and skills were qualified for many data jobs, so I did not need to start from the very basics like most Bootcamp students. After doing WeCloudData’s qualification exam, I was assigned to the project-based program.
The project-based program did not mean that I could only work on the projects. In fact, it went so much beyond my expectation of how many learning materials I would get access to, to not only refresh my knowledge but also to learn more skills. At the very basic level, I had access to courses that the Bootcamp students took, so I could go back and look up anything that I felt uncertain about. I was also free to join the part-time courses to learn more about advanced topics, such as big data, which was not offered back in my data science MS program. All of the courses were very practical and meant to prepare the students for their careers, so it was quite a different but enjoyable experience for me compared to those offered in the universities that lean more towards academics.

Albert Ngan
Azure Engineer at CIBC
As with any Bootcamp, a tremendous amount of learning is squeezed into a short time. That’s by far the biggest challenge. New concepts take time to really wrap your head around and digest so pre- studying is highly recommended. The instructors are very knowledgeable, sharp, and open to discussion, but you can only fit so much material in a 3- hour lecture. The rest is on your own time using additional resources for self-learning – which is not always easy if you’re used to being spoon-fed in a traditional classroom setting – but we luckily live in a world where virtually any question can be answered with Google. It’s important to understand that most of your learning will be done outside of lectures and labs.

Paso 1
Concierte una reunión de asesoramiento

Paso 2
Superar el desafío

Paso 3
Reserve la plaza y firme el contrato

Paso 4
Comience su aprendizaje previo al Campamento de arranque