Data Science on Cloud
Next cohort starts Aug 05th, 2023
About the course
Data Science on Cloud is a 12-week course in which you will learn how to design and implement a Data Science solution on the cloud end to end using AWS SageMaker/AzureML, covering the entire Machine Learning Lifecycle - pre-processing, building, training, deploying, tuning, monitoring, and managing costs. You will learn all of this through a blend of Live Lectures, hands-on focused learning content, mentored learning sessions by industry experts who have practical experience building ML solutions on the cloud, real-world case studies, practice & Q&A sessions
After completing this course, you will gain practical experience with working on the cloud and the ability to implement an end-to-end machine learning solution on a cloud platform. You will learn to do this using an array of cloud services depending on the platform you choose -
- AWS: SageMaker, RDS, Feature Store, Notebooks, Training, and Hyperparameter Tuning Jobs, Endpoints & ECS for Deployment, Lambda Functions, CloudWatch, SageMaker Pipelines
- Azure: AzureML, AzureSQL, Feature Store, Notebooks, Training, and Hyperparameter Tuning Jobs, Endpoints & Azure Kubernetes Service for Deployment, Azure Functions, Azure Monitor, Azure Pipelines, and Model Registry
Curriculum
Get to Know Your Mentors
Earn Your Certificate
Showcase & verify your Data Science expertise with a certificate of completion from Great Learning which you can share in the certifications sections of your LinkedIn profile, on printed resumes, CVs or other documents.
Note: The image is for illustrative purposes only. The actual certificate may be subject to change at the discretion of Great Learning.
About The Course
Data Science on Cloud is a 12-week course in which you will learn how to design and implement a Data Science solution on the cloud end to end using AWS SageMaker/AzureML, covering the entire Machine Learning Lifecycle - pre-processing, building, training, deploying, tuning, monitoring, and managing costs. You will learn all of this through a blend of Live Lectures, hands-on focused learning content, mentored learning sessions by industry experts who have practical experience building ML solutions on the cloud, real-world case studies, practice & Q&A sessions
After completing this course, you will gain practical experience with working on the cloud and the ability to implement an end-to-end machine learning solution on a cloud platform. You will learn to do this using an array of cloud services depending on the platform you choose -
- AWS: SageMaker, RDS, Feature Store, Notebooks, Training, and Hyperparameter Tuning Jobs, Endpoints & ECS for Deployment, Lambda Functions, CloudWatch, SageMaker Pipelines
- Azure: AzureML, AzureSQL, Feature Store, Notebooks, Training, and Hyperparameter Tuning Jobs, Endpoints & Azure Kubernetes Service for Deployment, Azure Functions, Azure Monitor, Azure Pipelines, and Model Registry
Curriculum
Get to Know Your Mentors
Md.Parwez Alam
Senior Data Scientist - Hewlett Packard Enterprise
Hossein Kalbasi
Data Scientist - Concured
Abhijit Krishna Menon
Data Engineer - Amazon
Earn Your Certificate
Showcase & verify your Data Science expertise with a certificate of completion from Great Learning which you can share in the certifications sections of your LinkedIn profile, on printed resumes, CVs or other documents.
Note: The image is for illustrative purposes only. The actual certificate may be subject to change at the discretion of Great Learning.
Get Started
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Learn from highly skilled professionals in the field of Data Science who have engineered data solutions across industry verticals & have real-world, hands-on work experience with AWS.
This course is for you, if you
- Are involved in a transformative cloud initiative at your enterprise and want to implement data solutions on the cloud
- Are an organization/work for one and is going through a phase of migrating your data products and customer services to the cloud
- Are part of a startup and looking to set up a data & insights pipeline on the cloud for a product
You will learn to
- Navigate the ML Ecosystem within the cloud using SageMaker/AzureML Studio and get a deep understanding of the cloud services needed for data science
- Use an array of different services across the ML Lifecycle from data pre-processing, training, tuning, and deployment to model monitoring and MLOps
- Design, develop and deliver end-to-end data science solutions using AWS/Azure
Why Data Science on Cloud?
- Employment website Indeed.com has listed machine learning engineer as #1 among The Best Jobs in the U.S., citing a 344% rate of growth ~Source: Indeed.com
- 60% of the cloud computing job postings ask for skills related to either Amazon Web Services, Microsoft Azure or Google Cloud Platform ~Source: Projectpro
- 92% of companies have a cloud initiative ~Source: Flexera
Md.Parwez Alam
Senior Data Scientist - Hewlett Packard Enterprise
Learn from highly skilled professionals in the field of Data Science who have engineered data solutions across industry verticals & have real-world, hands-on work experience with AWS.
Businesses worldwide are migrating their data and insights processes to the cloud as it’s cost-effective to manage and it caters to the constantly evolving needs of the data industry - streamlining business decision-making using data science and analytics. Today’s data scientists, over and above having practical experience with Machine Learning, are also expected by organizations to be adept at deploying & scaling ML Operations on the cloud.
AWS and Azure Cloud are the most used cloud platforms today to implement ML Engineering solutions. AWS SageMaker, and AzureML are cloud-based ML Engineering tools which are widely used in the industry, and they seamlessly help you with data pre-processing, training, tuning, deploying, monitoring and scaling & automating your data science solutions through MLOps. This course is designed to help you, as a data scientist, build the cloud skills needed in order to keep up with the changing technology landscape.
7 real-world case studies & 2 hands-on projects
12-week online course
8+ ML services covered, in cloud platform of your choice - AWS/Azure
8 Live Classroom sessions on weekdays by world class faculty
Data Science on Cloud
Businesses worldwide are migrating their data and insights processes to the cloud as it’s cost-effective to manage and it caters to the constantly evolving needs of the data industry - streamlining business decision-making using data science and analytics. Today’s data scientists, over and above having practical experience with Machine Learning, are also expected by organizations to be adept at deploying & scaling ML Operations on the cloud.
AWS and Azure Cloud are the most used cloud platforms today to implement ML Engineering solutions. AWS SageMaker, and AzureML are cloud-based ML Engineering tools which are widely used in the industry, and they seamlessly help you with data pre-processing, training, tuning, deploying, monitoring and scaling & automating your data science solutions through MLOps. This course is designed to help you, as a data scientist, build the cloud skills needed in order to keep up with the changing technology landscape.
Next cohort starts Aug 05th, 2023
8+ live hands-on sessions (mentored learning and lab sessions)
This course is designed based on the practical needs of a data scientist working in a cloud environment to build data solutions for organizations. It focuses on all the practical aspects of the machine learning lifecycle - building and training a model, deploying a model to production, monitoring, and managing costs, gathering feedback, and setting up automated performance reporting. Focusing on the practical aspects of the machine learning lifecycle, the curriculum also exposes you to advanced concepts such as model monitoring, creating pipelines, projects, etc.
12-week online course
7 real-world case studies & 2 hands-on projects
8+ ML services covered, in cloud platform of your choice - AWS/Azure
8 Live Classroom sessions on weekdays by world class faculty
8+ live hands-on sessions (mentored learning and lab sessions)
$1100
This course is for you, if you
- Are involved in a transformative cloud initiative at your enterprise and want to implement data solutions on the cloud
- Are an organization/work for one and is going through a phase of migrating your data products and customer services to the cloud
- Are part of a startup and looking to setup a data & insights pipeline on the cloud for a product
You will learn how to
- Navigate the ML Ecosystem within the cloud using SageMaker/AzureML Studio and get a deep understanding of the cloud services needed for data science
- Use an array of different services across the ML Lifecycle from data pre-processing, training, tuning, and deployment to model monitoring and MLOps
- Design, develop and deliver end-to-end data science solutions using AWS/Azure
Why Data Science on Cloud?
- Employment website Indeed.com has listed machine learning engineer as #1 among The Best Jobs in the U.S., citing a 344% rate of growth ~Source: Indeed.com
- 60% of the cloud computing job postings ask for skills related to either Amazon Web Services, Microsoft Azure or Google Cloud Platform ~Source: Projectpro
- 92% of companies have a cloud initiative ~Source: Flexera
This course is designed based on the practical needs of a data scientist working in a cloud environment to build data solutions for organizations. It focuses on all the practical aspects of the machine learning lifecycle - building and training a model, deploying a model to production, monitoring, and managing costs, gathering feedback, and setting up automated performance reporting. Focusing on the practical aspects of the machine learning lifecycle, the curriculum also exposes you to advanced concepts such as model monitoring, creating pipelines, projects, etc.
Experience the Great Learning Philosophy
- Structured Learning Journey
- Comprehensive Curriculum
- Hands-on Learning
- Personalized Feedback
- Mentored Learning
- Program Manager Support
- Academic Support
Experience the Great Learning Philosophy
- Structured Learning Journey
- Comprehensive Curriculum
- Hands-on Learning
- Mentored Learning & Personalized Feedback
- Program Manager Support
- Academic Support
$1100
Enquire +1 512 337 4707
Enquire +1 512 337 4707
Week 0 | Pre-work
Before starting off with learning how to implement data science solutions on the cloud, you can do 3 things to get ready
- Brush up on the conceptual understanding of various ML Models
- Revise your Python Programming skills - as we will be using python throughout the journey
- If you are new to the cloud, get inducted into what the cloud is and what you need as a data scientist
As soon as you enroll, you will be given access to content that will help you with this.
Module 0 | Pre-Work
Week 01 | Introduction to MLOps
The objective of this week is to understand what MLOps is, why is it important in today’s business environment, learn how to navigate the ML Lifecycle
- What is MLOps?
- The requirement for MLOps in the industry
- Business Examples of ML solutions for products
- Machine Learning Lifecycle
- Challenges in ML Ops
- Looking forward - setting expectations for the journey ahead
Hands-on skills
- AWS: An end-to-end demo of a data science solution implementation using AWS SageMaker
- Azure: An end-to-end demo of a data science solution implementation using AzureML
Module 1 | ML on Cloud
Week 02 | Getting Setup for ML on Cloud
The objective of this week is to get set up with the cloud studio you will be working with, the database that you will query for data, and understand how to use the cloud shell
- What is a warehouse?
- What is the difference b/w a traditional & cloud warehouse
- Cloud Shell & Commands
Hands-on skills
- AWS: SageMaker Studio, Amazon RDS (Relational Database Service), AWS Command Line
- Azure: AzureML Studio, AzureSQL Database, Azure Cloud Shell
Week 03 | Data Pre-processing
The objective of this week is to learn how to run data pre-processing jobs on the cloud - which can include crucial steps like data quality assessment, cleaning, and transformation
- Data Quality Assessment
- Data Cleaning
- Data Transformation - Scaling, Encoding, Normalizing
- Outlier detection and handling
- Feature Selection
Hands-on skills
- AWS: SageMaker Studio, Feature Store, Notebooks, Pre-processing Jobs
- Azure: AzureML, Feature Store, Notebooks, Pre-processing Jobs
Week 04 | Training, Tuning & Deployment
The objective of this week is to learn how to build a model, train, tune, and quickly deploy that model as an endpoint for real-time inference
- How to train and tune the ML model
- General Code Architecture for each stage
Hands-on skills
- AWS: Notebooks, Training Jobs, Hyper-parameter Tuning Jobs, Endpoint Deployment
- Azure: Notebooks, Training Jobs, Hyper-parameter Tuning Jobs, Endpoint Deployment
Week 05 | Model Serving
The objective of this week is to learn how you can deploy a model on a container and serve that model as an API to be accessed by your stakeholders
- Real-time vs Batch Transformation
- Web APIs - how do they work for hosting a model?
- Containerization with docker, kubernetes
Hands-on skills
- AWS: ECS, ECR, EC2 and Lambda Functions
- Azure: Azure Kubernetes Service, Azure Functions
Week 06 | Learning Break
The objective of this week is to catch up on your practice and attend an optional Q&A session to get help for the mid-term project
Week 07 | Mid Term Project - EasyVisa
Business Context: The United States is nowadays facing a huge problem with human resources. Finding the best talent is always a tough job for the human resources department. Companies in the USA are open to hiring highly skilled people both locally and internationally. The Immigration and Nationality Act (INA) of the United States allows foreign workers to come to the country to work on a temporary or permanent basis. The act also protects US workers against adverse impacts on their wages or working conditions by ensuring all the safeguards mentioned in the act. Office of Foreign Labor Certification (OFLC) administers this act.
Objective: The OFLC processed millions of applications for different positions for temporary and permanent labor certifications. There is a percentage increase in the number of applications as compared to previous years. The process of reviewing every case is becoming a tedious task as the number of applicants increases every year. With an increasing number of applicants each year, a machine learning-based solution on the cloud that can assist in shortlisting candidates with a higher chance of VISA approval is required. EasyVisa has been hired by OFLC to provide data-driven solutions. As a data scientist at EasyVisa, you must analyze the data provided and, using a classification model, and automate the visa approval process on the cloud
Module 2 | MLOps on Cloud
Week 08 | Model Monitoring
The objective of this week is to learn the essentials concepts of model monitoring like drift, bias, and explainability and learn how to implement model monitoring on the cloud for your solution
- What is Model Monitoring?
- Why do you need Model Monitoring?
- Data drift, Concept drift, and Model drift
- Model Bias & Explainability metrics - LIME, SHAP
- How to detect drifts?
- Baselining
- Model Monitoring Architecture
- General Code Architecture
Hands-on skills
- AWS: Amazon CloudWatch
- Azure: Azure Monitoring
Week 09 | ML Ops on Cloud: Part I
The objective of this week is to get a strong foundation of the concepts leading to MLops, like - version control, collaboration, and project templates
- Basics of Version Control
- Introduction to Git
- Git Concepts - repo, commit, push, pull, branch, merge
- Collaborating with Git
- Best practices for coding
- Environments - Dev & Prod
- General Pipeline Architecture
Hands-on skills
- AWS: SageMaker Projects, SageMaker Pipelines
- Azure: Azure Pipelines
Week 10 | ML Ops on Cloud: Part II
The objective of this week is to learn how to build an end-to-end ML ops pipeline for your data science solution
- Deep dive into code structures
- Model Versioning
- How auto re-training works?
- Building a monitoring pipeline
Hands-on skills
- AWS: CodePipeline, Model Registry, CodeCommit
- Azure: Azure Repos, Model Registry
Week 11 and 12 | Final Project
Business Context: A certain number of hotel reservations are canceled due to changes in plans, scheduling conflicts, and other factors. The cancellation may be due to the customer discovering that cancellation charges are free or at a reduced cost. Last-minute cancellations create a revenue-diminishing factor for hotels to deal with - this has turned out to be a critical problem that hotels/lodges are trying to solve
Online booking options improve the customer experience, but they also have a negative impact on hotel owners because they are easy to cancel online. This adds to the complexity of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.
Objective: The increasing number of cancellations necessitates the development of a Machine Learning-based solution capable of predicting which bookings will be canceled. INN Hotels Group owns and operates a hotel chain in Portugal. They are experiencing high cancellation rates and have approached your firm for data-driven solutions. As an ML Engineer, you must clean and transform the available data, build a predictive model, train, tune, and deploy the model and make it accessible to your stakeholders in the hotel through an API, and all the MLops should be implemented on the cloud.
Week 0 | Pre-work
Before starting off with learning how to implement data science solutions on the cloud, you can do 3 things to get ready
- Brush up on the conceptual understanding of various ML Models
- Revise your Python Programming skills - as we will be using python throughout the journey
- If you are new to the cloud, get inducted into what the cloud is and what you need as a data scientist
As soon as you enroll, you will be given access to content that will help you with this.
Module 0 | Pre-Work
Week 01 | Introduction to MLOps
The objective of this week is to understand what MLOps is, why is it important in today’s business environment, learn how to navigate the ML Lifecycle
- What is MLOps?
- The requirement for MLOps in the industry
- Business Examples of ML solutions for products
- Machine Learning Lifecycle
Challenges in ML Ops - Looking forward - setting expectations for the journey ahead
Hands-on skills
- AWS: An end-to-end demo of a data science solution implementation using AWS SageMaker
- Azure: An end-to-end demo of a data science solution implementation using AzureML
Module 1 | ML on Cloud
Week 02 | Getting Setup for ML on Cloud
The objective of this week is to get set up with the cloud studio you will be working with, the database that you will query for data, and understand how to use the cloud shell
- What is a warehouse?
- What is the difference b/w a traditional & cloud warehouse
- Cloud Shell & Commands
Hands-on skills
- AWS: SageMaker Studio, Amazon RDS (Relational Database Service), AWS Command Line
- Azure: AzureML Studio, AzureSQL Database, Azure Cloud Shell
Week 03 | Data Pre-processing
The objective of this week is to learn how to run data pre-processing jobs on the cloud - which can include crucial steps like data quality assessment, cleaning, and transformation
- Data Quality Assessment
- Data Cleaning
- Data Transformation - Scaling, Encoding, Normalizing
- Outlier detection and handling
- Feature Selection
Hands-on skills
- AWS: SageMaker Studio, Feature Store, Notebooks, Pre-processing Jobs
- Azure: AzureML, Feature Store, Notebooks, Pre-processing Jobs
Week 04 | Training, Tuning & Deployment
The objective of this week is to learn how to build a model, train, tune, and quickly deploy that model as an endpoint for real-time inference
- How to train and tune the ML model
- General Code Architecture for each stage
Hands-on skills
- AWS: Notebooks, Training Jobs, Hyper-parameter Tuning Jobs, Endpoint Deployment
- Azure: Notebooks, Training Jobs, Hyper-parameter Tuning Jobs, Endpoint Deployment
Week 05 | Model Serving
The objective of this week is to learn how you can deploy a model on a container and serve that model as an API to be accessed by your stakeholders
- Real-time vs Batch Transformation
- Web APIs - how do they work for hosting a model?
- Containerization with docker, kubernetes
Hands-on skills
- AWS: ECS, ECR, EC2 and Lambda Functions
- Azure: Azure Kubernetes Service, Azure Functions
Week 06 | Learning Break
The objective of this week is to catch up on your practice and attend an optional Q&A session to get help for the mid-term project
Week 07 | Mid Term Project - EasyVisa
Business Context: The United States is nowadays facing a huge problem with human resources. Finding the best talent is always a tough job for the human resources department. Companies in the USA are open to hiring highly skilled people both locally and internationally. The Immigration and Nationality Act (INA) of the United States allows foreign workers to come to the country to work on a temporary or permanent basis. The act also protects US workers against adverse impacts on their wages or working conditions by ensuring all the safeguards mentioned in the act. Office of Foreign Labor Certification (OFLC) administers this act.
Objective: The OFLC processed millions of applications for different positions for temporary and permanent labor certifications. There is a percentage increase in the number of applications as compared to previous years. The process of reviewing every case is becoming a tedious task as the number of applicants increases every year. With an increasing number of applicants each year, a machine learning-based solution on the cloud that can assist in shortlisting candidates with a higher chance of VISA approval is required. EasyVisa has been hired by OFLC to provide data-driven solutions. As a data scientist at EasyVisa, you must analyze the data provided and, using a classification model, and automate the visa approval process on the cloud
Module 2 | MLOps on Cloud
Week 08 | Model Monitoring
The objective of this week is to learn the essentials concepts of model monitoring like drift, bias, and explainability, and learn how to implement model monitoring on the cloud for your solution
- What is Model Monitoring?
- Why do you need Model Monitoring?
- Data drift, Concept drift, and Model drift
- Model Bias & Explainability metrics - LIME, SHAP
- How to detect drifts?
- Baselining
- Model Monitoring Architecture
- General Code Architecture
Hands-on skills
- AWS: Amazon CloudWatch
- Azure: Azure Monitoring
Week 09 | ML Ops on Cloud: Part I
The objective of this week is to get a strong foundation of the concepts leading to MLops, like - version control, collaboration, and project templates
- Basics of Version Control
- Introduction to Git
- Git Concepts - repo, commit, push, pull, branch, merge
- Collaborating with Git
- Best practices for coding
- Environments - Dev & Prod
- General Pipeline Architecture
Hands-on skills
- AWS: SageMaker Projects, SageMaker Pipelines
- Azure: Azure Pipelines
Week 10 | ML Ops on Cloud: Part II
The objective of this week is to learn how to build an end-to-end ML ops pipeline for your data science solution
- Deep dive into code structures
- Model Versioning
- How auto re-training works?
- Building a monitoring pipeline
Hands-on skills
- AWS: CodePipeline, Model Registry, CodeCommit
- Azure: Azure Repos, Model Registry
Week 11 and 12 | Final Project
Business Context: A certain number of hotel reservations are canceled due to changes in plans, scheduling conflicts, and other factors. The cancellation may be due to the customer discovering that cancellation charges are free or at a reduced cost. Last-minute cancellations create a revenue-diminishing factor for hotels to deal with - this has turned out to be a critical problem that hotels/lodges are trying to solve
Online booking options improve the customer experience, but they also have a negative impact on hotel owners because they are easy to cancel online. This adds to the complexity of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.
Objective: The increasing number of cancellations necessitates the development of a Machine Learning-based solution capable of predicting which bookings will be canceled. INN Hotels Group owns and operates a hotel chain in Portugal. They are experiencing high cancellation rates and have approached your firm for data-driven solutions. As an ML Engineer, you must clean and transform the available data, build a predictive model, train, tune, and deploy the model and make it accessible to your stakeholders in the hotel through an API, and all the MLops should be implemented on the cloud.
Hossein Kalbasi
Data Scientist - Concured
Abhijit Krishna Menon
Data Engineer - Amazon
Meet Your Faculty
Vinicio De Sola
Senior Data Scientist - Aspen Capital
Vinicio DeSola Jr. graduated from Universidad Simon Bolivar in 2009 with a Bachelor of Science Degree in Production Engineering. He worked in Logistics for 5 years, using real-time data to predict input consumption and procurement. Later, he co-founded several entrepreneurial ventures, where he was the group CFO and risk manager. Vinicio then moved to the United States, where he graduated with a Master's in Financial Engineering from UC Berkeley. He started working in Finance, in particular, using Big Data Techniques to spot market manipulation. He later graduated again from UC Berkeley, this time with a Master's in Data Science, becoming Faculty after graduation, teaching both Fundamentals of Data Engineering in the Cloud and Machine Learning at Scale. Finally, he pursued an MBA from the University of Illinois, Urbana-Champaign, and he's currently working as a Senior Data Scientist at Aspen Capital, specializing in Real Estate Real-Time Data.
His LinkedIn profile is https://www.linkedin.com/in/pavankumar-gurazada-00791312/
Meet Your Faculty
Vinicio De Sola
Senior Data Scientist - Aspen Capital
Vinicio DeSola Jr. graduated from Universidad Simon Bolivar in 2009 with a Bachelor of Science Degree in Production Engineering. He worked in Logistics for 5 years, using real-time data to predict input consumption and procurement. Later, he co-founded several entrepreneurial ventures, where he was the group CFO and risk manager. Vinicio then moved to the United States, where he graduated with a Master's in Financial Engineering from UC Berkeley. He started working in Finance, in particular, using Big Data Techniques to spot market manipulation. He later graduated again from UC Berkeley, this time with a Master's in Data Science, becoming Faculty after graduation, teaching both Fundamentals of Data Engineering in the Cloud and Machine Learning at Scale. Finally, he pursued an MBA from the University of Illinois, Urbana-Champaign, and he's currently working as a Senior Data Scientist at Aspen Capital, specializing in Real Estate Real-Time Data.
His LinkedIn profile is https://www.linkedin.com/in/vinicio-desola-jr86/