Practical Data Science with Amazon SageMaker Course in Germany
Master ML with Amazon SageMaker
Get real-world insights from certified instructors
Apply machine learning to tackle real business challenges
Gain in-demand ML skills and boost your job prospects
Learn at your own pace with flexible, instructor-led sessions
Master the full ML workflow and its practical applications
Convenient and hassle-free payment plans
4.8/55469 EnrolledEnrolled Learners
4.8/55469 EnrolledEnrolled Learners
Overview
What you will learn:
- Learn data preparation techniques using Amazon SageMaker Data Wrangler
- Train models with powerful algorithms like XGBoost on Amazon SageMaker
- Evaluate and fine-tune models with hyperparameter optimization in SageMaker
- Deploy models to real-time endpoints with Amazon SageMaker for seamless integration
- Master MLOps practices to automate and monitor model deployment
- Gain hands-on experience in no-code machine learning with Amazon SageMaker Canvas
Upcoming sessions
Curriculum
1
Benefits of machine learning (ML)
2
Types of ML approaches
3
Framing the business problem
4
Prediction quality
5
Processes, roles, and responsibilities for ML projects
1
Data analysis and preparation
2
Data preparation tools
3
Demonstration: Review Amazon SageMaker Studio and Notebooks
4
Hands-On Lab: Data Preparation with SageMaker Data Wrangler
1
Steps to train a model
2
Choose an algorithm
3
Train the model in Amazon SageMaker
4
Hands-On Lab: Training a Model with Amazon SageMaker
5
Amazon CodeWhisperer
6
Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks
1
Model evaluation
2
Model tuning and hyperparameter optimization
3
Hands-On Lab: Model Tuning and Hyperparameter
4
Optimization with Amazon SageMaker
1
Model deployment
2
Hands-On Lab: Deploy a Model to a Real-Time Endpoint
3
and Generate a Prediction
1
Responsible ML
2
ML team and MLOps
3
Automation
4
Monitoring
5
Updating models (model testing and deployment)
1
Different tools for different skills and business needs
2
No-code ML with Amazon SageMaker Canvas
3
Demonstration: Overview of Amazon SageMaker Canvas
4
Amazon SageMaker Studio Lab
5
Demonstration: Overview of SageMaker Studio Lab
6
(Optional) Hands-On Lab: Integrating a Web Application
7
with an Amazon SageMaker Model Endpoint
Meet your Trainer
Our Trainers
Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.
We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.
Learning Outcomes
Successful completion of the training will help professionals in the following ways:
1
Master data preparation with SageMaker Data Wrangler to streamline data processing
2
Train models using advanced algorithms like XGBoost and fine-tune with hyperparameter optimization
3
Deploy machine learning models to real-time endpoints with Amazon SageMaker for seamless predictions
4
Implement MLOps practices for automating, monitoring, and managing model deployments
5
Gain practical experience in no-code machine learning using SageMaker Canvas
KHDA Certificate
Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.
Learners Point Certificate
Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.
Overall ratings by our students
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Frequently asked questions
Our Practical Data Science with Amazon SageMaker course in Germany is an interactive training course aimed at teaching you how to develop, train, and deploy machine learning models with AWS SageMaker. The course focuses on data preparation, model tuning, real-time deployment, and MLOps practices. You will be able to work with SageMaker tools such as Data Wrangler, XGBoost, and SageMaker Canvas by the end of the course.
As a data scientist, our course will extend your knowledge of machine learning pipelines with Amazon SageMaker. You'll be hands-on with data preparation, model training with sophisticated algorithms such as XGBoost, and real-time deployment. The course will also expose you to MLOps best practices for automating and monitoring your models, essential for scaling machine learning solutions in production.
Our course is unique in being the only course specifically on Amazon SageMaker, one of the most popular platforms in machine learning. Whereas other certifications will cover general subjects, this targeted course provides specific skills on model deployment and MLOps and is directly relevant to what the industry currently needs. Completing this course will set you apart and be particularly useful in cloud computing and machine learning-specific positions.
During the course, you will learn to work with Amazon SageMaker tools like Data Wrangler for data preparation, XGBoost for model training, and SageMaker Canvas for no-code machine learning. You will also gain hands-on experience with the platform's advanced capabilities for hyperparameter tuning, model evaluation, and real-time deployment.
There are several career benefits for completing this course:
- Proficiency in Amazon SageMaker: Gain expertise in a widely used tool in the industry.
- Eligibility for Senior Roles: Qualify for positions like Machine Learning Engineer, AI Specialist, and Data Science Lead.
- Scalability Skills: Enhance your ability to deploy machine learning models at scale.
- Increased Demand: Stand out in the growing field of cloud-based machine learning solutions.
- Career Advancement: Open doors to leadership roles in data-centric industries.
Certified professionals may take up a position in many different fields, such as tech, finance, healthcare, and e-commerce. Some typical job titles are Data Scientist, Machine Learning Engineer, AI Specialist, and Data Analyst. The need for machine learning professionals is increasing at a fast rate.
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