Amazon SageMaker Studio for Data Scientists Training in Ghana
Build, train, and deploy models with Amazon SageMaker Studio
Gain real-world experience in data processing, training, and deployment
Receive guidance from top industry professionals
Master everything from data wrangling to model optimisation
Gain in-demand skills for high-level data science roles
Flexible and intensive Training
Convenient and hassle-free payment plans
4.8/54500 EnrolledEnrolled Learners
4.8/54500 EnrolledEnrolled Learners
Overview
What you will master with us
- Master Amazon SageMaker Studio for end-to-end ML model development
- Use SageMaker Data Wrangler and AWS Glue for data processing
- Leverage SageMaker Experiments for model training and tuning
- Learn automated ML with SageMaker Autopilot and bias detection
- Deploy models using SageMaker Pipelines and Model Registry
- Gain hands-on experience with SageMaker Debugger for performance optimisation
Upcoming sessions
Curriculum
1
JupyterLab Extensions in SageMaker Studio
2
Demonstration: SageMaker user interface demo
1
Using SageMaker Data Wrangler for data processing
2
Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
3
Using Amazon EMR
4
Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
5
Using AWS Glue interactive sessions
6
Using SageMaker Processing with custom scripts
7
Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker
8
Python SDK
9
SageMaker Feature Store
10
Hands-On Lab: Feature engineering using SageMaker Feature Store
1
SageMaker training jobs
2
Built-in algorithms
3
Bring your own script
4
Bring your own container
5
SageMaker Experiments
6
Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning
7
SageMaker Debugger
8
Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
9
Automatic model tuning
10
SageMaker Autopilot: Automated ML
11
Demonstration: SageMaker Autopilot
12
Bias detection
13
Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
14
SageMaker Jumpstart
1
SageMaker Model Registry
2
SageMaker Pipelines
3
Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker
4
Studio
5
SageMaker model inference options
6
Scaling
7
Testing strategies, performance, and optimization
8
Hands-On Lab: Inferencing with SageMaker Studio
1
Amazon SageMaker Model Monitor
2
Discussion: Case study
3
Demonstration: Model Monitoring
Learning Outcomes
Upon finishing the training, you will:
1
Master data processing with SageMaker Data Wrangler and AWS Glue for seamless ML workflows
2
Optimise model performance using SageMaker Debugger for real-time training insights and alerts
3
Automate machine learning pipelines with SageMaker Autopilot and SageMaker Pipelines
4
Gain expertise in model deployment and version control using SageMaker Model Registry
5
Detect and mitigate bias using SageMaker Clarify to ensure fair and explainable models
Overall ratings by our students
Related courses
Frequently asked questions
The Amazon SageMaker Studio for Data Scientists training is designed to equip professionals in Ghana with the ability to prepare data, build models, and deploy machine learning solutions using SageMaker. Throughout the program, participants gain practical skills in data wrangling, model optimisation, and automated machine learning. This makes it an essential course for anyone looking to advance in the field of data science.
After completing the Amazon SageMaker Studio for Data Scientists training, you’ll be prepared for a wide range of high-demand roles within Ghana’s growing tech industry, such as:
1. Data Scientist
2. Machine Learning Engineer
3. AI Specialist
4. Cloud Architect
5. Data Engineer
This training provides you with the most up-to-date, industry-relevant skills needed to thrive as a data science professional. By mastering Amazon SageMaker Studio, you’ll gain confidence in data processing, model development, and scalable deployment. These capabilities are highly sought after in Ghana’s evolving digital economy, giving you a distinct advantage when pursuing career opportunities or leadership roles.
AWS services form the backbone of this training. Participants will gain hands-on experience with Amazon SageMaker, SageMaker Data Wrangler, SageMaker Experiments, and other AWS tools. Working in a real-world cloud environment, you’ll learn how to efficiently process and deploy machine learning models. This ensures you leave the course ready to design and manage scalable, end-to-end ML workflows that address Ghana’s unique data challenges.
Do you want to learn more about Learners Point Academy?
- Learn more about courses
- Understand about our methodology
- Let’s talk about Corporate trainings
- Anything else that you want to know, we are here for you!
