Movies often depict artificial intelligence (AI) as a powerful tool that handles complex tasks effortlessly. Although that is not a perfect picture of AI, it sells the advantages of adopting AI solutions.
According to McKinsey’s Global Survey, 33 percent of all businesses reported that AI helped them reduce their general costs by more than 20 percent in 2020. World Economic Forum also states that AI increases productivity by up to 40 percent. For this reason, 91.5 percent of businesses actively invest in and adopt AI solutions.
Despite the numerous advantages and opportunities in the AI market, companies still fear that a skills shortage will pose a significant challenge to AI. So, how do you match yourself up with these vast opportunities? WeCloudData provides a solution through its AI Engineering Bootcamp.
A Guide to WeCloudData’s AI Engineering Bootcamp
WeCloudData’s AI Engineering Bootcamp is an online instructor-led training program covering AI algorithms, modeling, deployment, and production. The 26-week bootcamp prepares you for machine learning engineer or AI engineer careers.
Any quality AI engineering education rests on three tripods. Below, we explore how WeCloudData balances what you learn, who you learn from, and how you learn in its AI Engineering Bootcamp.
Curriculum that Prepares You for the Industry
WeCloudData has developed a comprehensive curriculum that provides ample theoretical foundations and practical lessons to help you build a solid understanding of each concept. These topics are broken into two semesters.
The first semester teaches machine learning foundations, deep learning, computer vision, and natural language processing. In the second semester, the syllabus focuses on teaching students the MLOps skills that are highly in demand by employers.
Here’s a closer look at what you’ll learn at the bootcamp.
- Machine Learning Foundation Review. Explore data processing and machine learning, supervised and unsupervised learning, and hyperparameter optimization.
- Deep Learning Foundations. Familiarize yourself with the basic concepts of neural networks and deep learning. You’ll learn neural network architecture, convolutional and recurrent neural networks, and transformers basics.
- Computer Vision. Study the computer vision branch of neural networks, semantic segmentation, image processing, and augmentation.
- Natural Language Processing (NLP). Another core aspect of deep learning, this course introduces you to the fundamentals of natural language processing. Learn how to build models to solve various NLP problems, including Word Vectors, BERT, and generative modeling.
- Semester 1 Capstone Project. The capstone project tests your ability to build relevant deep learning models. It also builds proficiency with tools like HuggingFace, word2vec, and Glove.
- Cloud Computing Basics. Dive into the second semester with cloud computing. The curriculum introduces Google Cloud Programming services useful for machine learning model application deployment.
- Model Packaging and Deployment Basics. Learn how to build, package, and deploy ML models using the containerization approach with Terraform, python, FastAPI, nginx, and docker.
- ML Model Deployment and Management. Learn how to run experimentations and deploy models with MLflow to different platforms such as SageMaker, Databricks, and Google Vertex.
- Model Pipelines in Kubernetes. Learn to apply Kubernetes and Kuberflow to build and automate pipeline infrastructures. Then, learn to organize and serve ML models on GCP platforms.
- Model Monitoring. This course introduces the foundations of model monitoring for machine learning. It uses tools like Neptune, Sagemaker, Weights, and Biases.
- CI/CD for ML Engineering. Master how to deploy ML models using continuous integration and continuous delivery framework on Github Action and Jenkins.
- Semester 2 Capstone Project. This capstone project applies deep learning and machine learning operations techniques to solve actual industry problems. You must complete a machine learning project independently, from start to finish.
Go Beyond AI Algorithms, Focus on the Engineering Side
Knowing how to code doesn’t certify your ability to solve actual industry problems. Your problem-solving skills and understanding of various machine learning processes are vital when solving these issues.
WeCloudData aims to mold you into a well-rounded AI engineer. You will learn various AI concepts, understand how to interpret them into codes, and practice how to deploy them in AI systems. In addition, the portfolio project lets you showcase your newly acquired skills in building and deploying machine learning models.
Learn in an Interactive and Supportive Learning Environment
WeCloudData instructors’ vast professional experience complements their passion for shaping next-generation industry leaders.
The typical classroom environment is open and supportive. You are encouraged to ask for help and get adequate assistance during and after classroom sessions. Instructors, mentors, alumni, and colleagues are there to support you.
Prepare for the Big Move into Tech Through 1-1 Career Mentorship
The nature of job hiring is constantly changing. WeCloudData can keep you ahead of the curve by providing extensive career preparation based on current hiring practices. By working with data scientist and machine learning engineering mentors after graduating from the AI Engineering Bootcamp, job hunting can be far less intimidating.
With WeCloudData’s career services offered through a one-to-one mentorship fashion, expect to receive assistance in building compelling resumes and portfolios and preparing for job interviews to help you develop the proper disposition and confidently answer questions.
Why Choose a Career in AI/ML Engineering?
The reality is that AI is changing everything. With digital assistants like Siri and Alexa, AI-powered cab ordering platforms like Uber, face ID detection cameras, self-driving cars, and even movie recommendations on Netflix, it is almost impossible to avoid AI. The following factors illustrate the benefits of AI for in-field professionals.
- Positive job outlook. As more businesses adopt AI-driven technologies and solutions, it is only natural that the demand for artificial intelligence experts will increase. In fact, the Bureau of Labor Statistics predicts a 13 percent job growth by 2030, accounting for over 667,600 new jobs.
- High-paying opportunities. Not only can graduates expect vast employment opportunities, but AI careers also enjoy lucrative salaries. According to PayScale, the average salary for AI careers is $126,000 annually.
- Cross-field relevance. Artificial intelligence principally feeds on data and automation programming. Hence, AI engineers can seamlessly take up opportunities in other data-centric fields like robotics, software engineering, and data engineering.
Build Your Path to an AI/ML Engineering Career
Building a successful AI/ML engineering career starts with getting the best education and a job after training. Getting balanced AI/ML engineering training that focuses on how these programming skills apply to machine learning systems can be challenging to find.
WeCloudData seeks to equip students with top-quality technical, practical, and soft skills that make them job-ready from Day 1. Its AI Engineering Bootcamp prioritizes in-demand skills in an intense hands-on learning environment to help students succeed in this field.
If you are interested in kickstarting your career in artificial intelligence with WeCloudData, join the AI Engineering Bootcamp to get started.
About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication.