Advanced DeepTech ML and MLOps Training in Bhutan

Omdena in collaboration with GovTech Agency under the Royal Government of Bhutan brings you an exciting opportunity to enrol yourself in the Artificial Intelligence specialised learning program. Please find the necessary information and the registration link below. We hope to see you there !!!

The course covers a wide range of machine learning, data science, and MLOps topics, designed for people with different levels of experience and interests.

It starts with Mathematics and Statistics for Machine Learning, where you’ll learn the basic math needed for understanding machine learning. Then, Python for Machine Learning and Data Science teaches you how to use Python for machine learning, even if you’re just starting with programming.

Next, Introduction to Machine Learning Algorithms and Models introduces key concepts like supervised and unsupervised learning, NLP, computer vision, and deep learning. After that, Machine Learning Operations is for professionals who deploy models in real-world settings.

Lastly, Advanced Training in Development/Customization of MLOps products dives deeper into advanced techniques for deploying and managing machine learning models. Together, these topics offer a complete learning experience for everyone interested in machine learning, whether you’re new to it or already experienced.

Throughout the course, participants will learn various essential concepts and skills related to machine learning, data science, and MLOps. Here’s a list of the topics that you’ll learn:

1. Foundational mathematical concepts are crucial for understanding machine learning algorithms, including linear algebra, calculus, and probability theory.
2. Practical guidance on using Python for machine learning tasks, covering data processing, machine learning libraries, data visualization, and basic Git usage.
3. Fundamental concepts and techniques in machine learning, including supervised and unsupervised learning, natural language processing (NLP), computer vision, and deep learning.
4. Understanding the machine learning lifecycle and deploying models in production environments, including data lifecycle management, modeling pipelines, and practical deployment strategies.
5. Advanced skills in MLOps, cloud services, containerization (using Docker and Kubernetes), continuous integration, monitoring, model logging, decay, and building end-to-end ML projects.

Overall, participants will gain a comprehensive understanding of the theoretical foundations, practical tools, and real-world applications of machine learning, data science, and MLOps, equipping them with the knowledge and skills needed to succeed in the field.

1. Basic understanding of algebra, calculus, and probability theory.
2. Familiarity with basic programming concepts (variables, loops, functions, etc.).
3. Basic knowledge of Python programming language.
4. Understanding of basic machine learning concepts (e.g., supervised and unsupervised learning).
5. Familiarity with deploying software applications in production environments.
6. Proficiency in Python programming language, experience with cloud services (e.g., AWS, Azure, Google Cloud Platform), familiarity with containerization technologies (e.g., Docker, Kubernetes), and knowledge of continuous integration and monitoring tools.

Theme	Topic	Duration in minutes
Mathematics and Statistics for Machine Learning	Linear Algebra	20
	Analytic Geometry	20
	Matrix Decompositions	20
	Vector Calculus	20
	Probability and Distribution	20
	Continuous Optimization	20
Teaching Hours in Total		2 hours
Python for Machine Learning and Data Science	Basic concepts	120
	Web frameworks: Django and Flask and Streamlit app	120
	Data processing in Python	120
	Machine Learning Libraries	120
	Data Visualisations	60
	Getting started with Git	60
Teaching Hours in Total		10 hours
Introduction to Machine Learning Algorithms and Models	Supervised learning and unsupervised learning	120
	NLP Application	120
	Time Series Application	120
	Computer Vision Application	120
	Deep Learning Understanding and Application	120
	Introduction to ANN and its application	120
	Transfer Learning	120
	Pre-trained Deep Learning Models	120
Teaching Hours in Total		16 hours
Machine Learning Operations	Overview of the ML Lifecycle and Deployment	180
	Machine Learning Data Lifecycle in Production	180
	Machine Learning Modeling Pipelines in Production	180
	Deploying Machine Learning Models in Production	180
	Practical Deployment Presentation	180
Teaching Hours in Total		12 hours
Advanced Training: Development/ Customization of MLOps product	Introduction to Cloud Services	180
	Database – SQL	180
	Dimensionality Reduction and Feature Selection	180
	Supervised & Unsupervised Learning	180
	Introduction to Artificial Neural Networks	180
	Introduction to DevOps	180
	Introduction to DevOps	180
	Dockers	180
	Docker Swarm	180
	Introduction to Cloud Computing using AWS	180
	Introduction to Cloud Computing using AWS	180
	Kubernetes	180
	Kubernetes	180
	Continuous Integration using Jenkins	180
	Continuous Integration using Jenkins	180
	Continuous Monitoring with Prometheus and Graffana	180
	Continuous Monitoring with Prometheus and Graffana	180
	Model Logging	180
	Model Logging	180
	Model Decay	180
	Model Decay	180
	Building end-to-end ML projects	180
	Building end-to-end ML projects	180
	Model serving and Deployment	180
	Model serving and Deployment	180
	Practical deployment	180

Program Execution Attendance

• The course is a Hybrid (onsite/online)
• Course is scheduled from 16th December 2024 to 20th February 2025
• Sessions are 5 days a week from Monday to Friday
• 75% of attendance
• Note: The learners of the course must attend 75% of live/in-person sessions to qualify for the course certificate.

Evaluation/ Submission of Assessments

The following are the evaluation criteria for the

• Learning checks during the live session
• Performance in weekly assignments and learning checks
• On-time submission of assignments/project activities
• Note: To qualify for the certificate it is mandatory to pass the assessment with 60% of the grade

Revoking/Removal:

In the following cases, learners will be removed from the course

• Two weeks of consecutive absences from the live session without any prior notice
• Poor performance in assignments and quizzes
• Late submission of assignments