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Amazon SageMaker Studio for Data Scientists Certification Training
This 3-day Amazon SageMaker Studio for Data Scientists course enables participants to delve deeply into the world of data science. You will explore the intricacies of data analysis, machine learning, and model deployment through comprehensive instruction. Tailored specifically fo
Amazon SageMaker Studio for Data Scientists Certification Training
• Comprehensive exam support and post-training assistance.
• High success rate: 98% passing rate for exams.
• Expert instruction by AWS Certified Instructors.
Course Overview
This Amazon SageMaker Studio for Data Scientists course is designed to provide data scientists with comprehensive training in leveraging Amazon SageMaker Studio for machine learning workflows. Through a combination of theoretical concepts and hands-on exercises, participants will gain proficiency in utilizing SageMaker Studio's integrated development environment (IDE) for building, training, and deploying machine learning models at scale.
The course begins with an introduction to SageMaker Studio, covering its key features, functionalities, and benefits for data science projects. Participants will learn how to set up SageMaker Studio environments, manage data, and collaborate with team members effectively.
Subsequent modules delve into essential topics such as data preprocessing, model training, and hyperparameter tuning using SageMaker's built-in algorithms and frameworks. Participants will also explore advanced techniques for model evaluation, debugging, and optimization within the SageMaker environment.
Moreover, the course equips data scientists with the skills to deploy machine learning models into production using SageMaker hosting services and manage inference pipelines efficiently.
Throughout the training, participants will work on real-world projects and case studies, enabling them to apply their knowledge and skills to solve practical data science challenges effectively. By the end of the course, participants will be proficient in leveraging Amazon SageMaker Studio to streamline their machine learning workflows and accelerate model development cycles.
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Course Objectives
Upon completing the course, you will be able to:
• Understand the fundamentals of Amazon SageMaker Studio and its role in the machine learning lifecycle.
• Learn how to set up and configure SageMaker Studio environments for efficient data science workflows.
• Explore data preprocessing techniques and best practices within SageMaker Studio.
• Gain proficiency in utilizing SageMaker's built-in algorithms and frameworks for model training.
• Master hyperparameter tuning strategies to optimize model performance.
• Acquire skills in model evaluation, debugging, and fine-tuning using SageMaker Studio's integrated tools.
• Learn how to deploy machine learning models into production using SageMaker hosting services.
• Understand how to manage inference pipelines and monitor model performance post-deployment.
• Develop proficiency in collaboration and version control workflows within SageMaker Studio.
• Apply knowledge and skills to real-world data science projects and case studies to solve practical challenges effectively.
Skills You Will Acquire:
• Setting up SageMaker Studio environments smoothly.
• Perfecting data preprocessing techniques.
• Fine-tuning models for maximum accuracy.
• Deploying models seamlessly with SageMaker hosting.
• Collaborating efficiently and managing versions effectively.
Related Certification: AWS Certified Machine Learning - Specialty
The AWS Certified Machine Learning - Specialty certification is designed for professionals seeking validation of their expertise in machine learning on the Amazon Web Services (AWS) platform. This certification validates skills in designing, implementing, deploying, and maintaining machine learning solutions on AWS. Candidates demonstrate proficiency in using AWS services for data engineering, model training, model evaluation, and deployment. The certification covers a wide range of topics including data visualization, feature engineering, model optimization, and scalability. Holding this certification signifies the ability to leverage AWS tools and services effectively to solve complex machine learning problems and drive innovation in diverse industries.
Skills Measured:
Data Engineering: 20%
Exploratory Data Analysis: 24%
Modeling: 36%
Machine Learning Implementation and Operations: 20%
Exam Overview:
Level: Specialty
Length: 180 minutes to complete the exam
Cost: 300 USD.
Who can take this exam?
The AWS Certified Machine Learning - Specialty certification targets professionals in development or data science roles, with over a year of experience in developing, architecting, or managing machine learning and deep learning workloads on the AWS Cloud.
Audience
• Data scientists
• Machine learning engineers
• Data analysts
• AI developers
• Researchers
• Data engineers
• Business analysts
• Software developers familiar with machine learning
• Professionals interested in AWS SageMaker Studio
Prerequisite
• AWS Technical Essentials
• The Machine Learning Pipeline on AWS
Course Outline
Module 1: Setup and SageMaker Navigation
Launch SageMaker Studio from the AWS Service Catalog.
Navigate through the SageMaker Studio UI.
Demo 1: SageMaker UI Overview
Demo 2: Creating an EMR cluster in the SageMaker UI
Lab 1: Configuring Amazon SageMaker Studio
Module 2: Data Processing
Utilize SageMaker Studio for data collection, cleansing, visualization, analysis, and transformation.
Establish a reproducible workflow for data processing.
Leverage SageMaker to verify that collected data meets machine learning standards.
Identify bias in collected data and gauge initial model accuracy.
- Lab 2: Analyze and Prepare Data with Amazon SageMaker Data Wrangler
- Lab 3: Scale Data Analysis and Preparation with Amazon EMR
- Lab 4: Perform Data Processing Using Amazon SageMaker Processing and the Sagemaker Python SDK
- Lab 5: Conduct Feature Engineering with SageMaker Feature Store
Module 3: Model Development
Leverage SageMaker Studio for developing, optimizing, and assessing machine learning models aligned with business goals and fairness and interpretability guidelines.
Refine machine learning models utilizing automated hyperparameter optimization functionality.
Employ the debugger to identify issues during model refinement.
- Demo 3: Algorithm Showcase (Notebooks)
- Demo 4: Debugging Processes
- Demo 5: Autopilot Exploration
- Lab 6: Tracking Training and Tuning Model Iterations with SageMaker Experiments
- Lab 7: Detecting and Addressing Issues with SageMaker Debugger
- Lab 8: Evaluating Bias and Interpretability with SageMaker Clarify
Module 4: Deployment and Inference
Develop and deploy a deployment solution tailored to fulfill inference use case specifications.
Establish, automate, and oversee comprehensive machine learning workflows employing Amazon SageMaker Pipelines.
Utilize the Model Registry to establish a Model Group, register, monitor, and oversee model versions, adjust model approval status, and deploy models.
Lab 9: Inference Implementation with SageMaker Studio
Lab 10: Integrating SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
Module 5: Monitoring
Set up a Model Monitor system to identify discrepancies and trigger notifications for variations in data quality, model performance, bias, and feature attribution consistency.
Establish a monitoring timetable with a predefined frequency.
Demo 6: Model Monitoring Showcase
About The Certification
Exam Overview:
Level: Specialty
Length: 180 minutes to complete the exam
Cost: 300 USD.
Choose Your Preferred Mode
Online Training
- 3 days Instructor led Online Training
- Experienced Subject Matter Experts
- Approved and Quality Ensured training Material
- 24*7 leaner assistance and support
Corporate Training
- Customized Training Across Various Domains
- Instructor Led Skill Development Program
- Ensure Maximum ROI for Corporates
- 24*7 Learner Assistance and Support
FAQ’s
What is the Amazon SageMaker Studio for Data Scientists course?
The Amazon SageMaker Studio for Data Scientists course is designed to equip data scientists with the knowledge and skills needed to leverage Amazon SageMaker Studio for machine learning workflows. From setting up environments to deploying models, this course covers the entire machine learning lifecycle on the AWS platform.
What are the prerequisites for enrolling in the Amazon SageMaker Studio for Data Scientists course?
Prior experience with machine learning concepts and familiarity with AWS services are recommended prerequisites for this course. Additionally, proficiency in programming languages such as Python will be beneficial for participants to fully engage in the practical exercises.
How long does it typically take to complete the Amazon SageMaker Studio for Data Scientists course?
The duration of the course is 3-days.
Is there any recommended experience level for taking the AWS Certified Machine Learning - Specialty certification exam after completing this course?
While completing the Amazon SageMaker Studio for Data Scientists course provides valuable preparation for the AWS Certified Machine Learning - Specialty certification exam, candidates are encouraged to have practical experience in developing, deploying, and managing machine learning solutions on AWS before attempting the exam.
How can I maximize the benefits of completing the Amazon SageMaker Studio for Data Scientists course?
Beyond acquiring theoretical knowledge and practical skills, participants can further enhance their learning experience by actively participating in discussions, collaborating with peers, and applying the concepts learned to real-world projects. Additionally, staying updated with the latest advancements in machine learning and AWS technologies will help reinforce and expand upon the knowledge gained from the course.
Can the Amazon SageMaker Studio for Data Scientists course be taken remotely or in-person?
Yes, the course can be delivered in both remote and in-person formats to accommodate the preferences and requirements of participants. Remote delivery enables flexibility and accessibility, while in-person sessions offer opportunities for interactive engagement and networking with instructors and peers.
Are there any discounts or promotions available for the Amazon SageMaker Studio for Data Scientists course?
Vinsys occasionally offers discounts or promotions for its courses, including the Amazon SageMaker Studio for Data Scientists course. Interested individuals can inquire about ongoing offers or check the Vinsys website for any current promotions to avail of potential savings on course fees.
Can companies request customized versions of the Amazon SageMaker Studio for Data Scientists course for their teams?
Yes, Vinsys offers the flexibility to tailor course content and delivery to meet the specific needs and objectives of corporate clients. Customized versions of the course can be arranged to address the unique requirements and skill levels of teams within organizations.
How can I stay updated with future offerings and updates related to the Amazon SageMaker Studio for Data Scientists course?
Individuals interested in the Amazon SageMaker Studio for Data Scientists course can subscribe to Vinsys' mailing list or follow their social media channels to receive notifications about upcoming course offerings, updates, and relevant industry insights. Additionally, regularly checking the Vinsys website for course updates is recommended.
Why choose Vinsys for this course?
Choose Vinsys for the Amazon SageMaker Studio for Data Scientists course for comprehensive training by industry experts. Benefit from flexible learning options, including remote and in-person delivery. Access hands-on labs, real-world projects, and personalized support to enhance your skills in machine learning on the AWS platform. Stay updated with the latest industry insights and networking opportunities. Start your journey with Vinsys today!
Why Vinsys
Seasoned Instructors
Official Vendor Partnerships
Authorized Courseware
3,000+ Courses & 2,000+ Modules
In Synch with Tech-advancements
Customizable Blended Learning Options
Reviews
The Amazon SageMaker Studio for Data Scientists course at Vinsys was an eye-opener for me. The content was incredibly thorough, covering everything from initial setup to advanced deployment techniques. The hands-on labs were invaluable, providing practical experience and reinforcing theoretical concepts. What stood out the most was the dedication of the instructors; they were not only experts in their field but also passionate about sharing their knowledge. This course has equipped me with the skills and confidence to take on complex machine learning projects with ease. I highly recommend it to anyone looking to excel in the field of data science.
Rahul KumarData Scientist
Completing the Amazon SageMaker Studio for Data Scientists course at Vinsys was a transformative experience for me. The course content was comprehensive and well-structured, covering a wide range of topics in machine learning on the AWS platform. The instructors were not only knowledgeable but also passionate about their subject, which made the learning process engaging and enjoyable. The practical labs provided valuable hands-on experience, allowing me to apply the concepts learned in real-world scenarios. I feel much more confident in my abilities as a data scientist after completing this course, and I would highly recommend it to anyone looking to advance their career in this field.
Kajal kapurData Base Enginner
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