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AWS Machine Learning Engineer Certification Course
AWS Machine Learning Engineer Certification Course
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Instructor-led AWS Certified Machine Learning Engineer Associate live online Training Schedule
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Why enroll for AWS Machine Learning Engineer Certification Course?
AWS Machine Learning Certification Training Course Benefits
According to the World Economic Forum, the need for experts in AI and Machine Learning is projected to increase by 40%, leading to the creation of 1 million new jobs due to the expanding utilization of AI and machine learning, which is fueling ongoing changes in various industries. Acquiring proper training and certification in this field will boost the number of job opportunities available.
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Want to become a Machine Learning Engineer?
Why AWS Machine Learning Engineer Certification Course from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your AWS Machine Learning Engineer Certification Course
Skills Covered
AWS Machine Learning Certification Course Tools Covered
AWS Machine Learning Certification Course Curriculum
Curriculum Designed by Experts
Introduction to AWS and Machine Learning
18 Topics
Topics:
- Cloud Computing Essentials
- Getting started with AWS
- AWS Global Infrastructure
- Key AWS Services
- AWS Management Console and CLI
- AWS Identity and Access Management (IAM)
- AWS Compute Services
- AWS Storage Services
- Introduction to Artificial Intelligence
- Machine Learning Fundamentals
- Machine Learning: Advantages and Disadvantages
- Supervised Learning: Classification
- Supervised Learning: Regression
- Unsupervised Learning
- Reinforcement Learning
- Planning a Machine Learning Project
- Overview of AWS AI/ML Services
- Introduction to SageMaker
![skill]( "Hands On Experience")
- Setting Up an AWS Account
- Launching EC2 Instances
- Working with S3
![skill]( "skill you will learn")
- Navigating the AWS Management Console
- Managing IAM Users and Policies
- Launching and Managing EC2 Instances
- Configuring S3 Buckets and Managing Data Storage
- Understanding Machine Learning Concepts
Preparing Data for Machine Learning Models with AWS
13 Topics
Topics:
- Data Collection
- Exploratory Data Analysis
- Data Visualization
- AWS Data Sources and Services
- Ingest, Extract, and Merge Data
- Data Transformation
- AWS Tools and Services for Data Validation and Bias Mitigation
- Data Preparation
- Configure Data for Model Training
- Data Labeling with AWS
- Data Ingestion with AWS
- Data Transformation with AWS
- Transforming Data Using AWS Glue
![skill]( "Hands On Experience")
- Performing Exploratory Data Analysis
- Visualizing Data for Machine Learning Models
- Implementing data ingestion pipelines
- Dataset Splitting, Shuffling, and Augmentation
- Transforming Data by Using AWS Glue
- Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR
![skill]( "skill you will learn")
- Data Preparation
- Exploratory Data Analysis
- Data Visualization
- Data Cleaning
- Handling Imbalanced Data
- Data Augmentation
Feature Engineering
8 Topics
Topics:
- Feature Engineering importance
- Role in machine learning pipelines
- Feature Engineering Concepts
- Types of Features
- Feature Engineering Workflow
- Engineering Numerical Features
- Engineering Text Features
- Feature Selection Techniques
![skill]( "Hands On Experience")
- Feature Engineering Use Case
- Comparing model performance with and without feature engineering
![skill]( "skill you will learn")
- Feature Engineering
- Feature Selection
Training the Machine Learning Models
14 Topics
Topics:
- Model Training Essentials
- Modeling Approaches
- AWS AI Services
- Model Selection Considerations
- Compute Environments
- AWS Container Services
- Building a Training Job with SageMaker Console
- Techniques for Decreasing Training Duration
- Incorporating External Models into SageMaker
- Fitting Models
- Optimizing Hyperparameters
- Controlling Model Dimensions
- Enhancing Pre-Trained Models
- Version Control for Models
![skill]( "Hands On Experience")
- Implementing different modeling approaches
- Model Training with Amazon SageMaker
- Train a model using a built-in algorithm
- Train a model using a custom script in script-mode
![skill]( "skill you will learn")
- Training Models
- Working with Amazon SageMaker
- Hyperparameter Tuning
- Model Versioning
Evaluating Model Performance
13 Topics
Topics:
- Assessing Models
- Methods for Evaluating Models
- Measuring Model Performance
- Evaluating Classification Performance
- Analyzing Confusion Matrices
- Using ROC and AUC-ROC
- Metrics for Multi-Class Classification
- Evaluating Regression Performance
- Troubleshooting Convergence Problems
- Resolving Convergence with SageMaker Debugger
- Overview of SageMaker Clarify and Metrics
- Understanding Model Outputs with SageMaker Clarify
- Managing Experiments with SageMaker
![skill]( "Hands On Experience")
- Evaluating Classification Model Performance
- Evaluating Regression Model Performance
- Debug Model Convergence with SageMaker Debugger
![skill]( "skill you will learn")
- Analyzing Model Performance
- Interpret Model Outputs
Model Integration and Deployment on AWS
13 Topics
Topics:
- Design Robust and Scalable ML Solutions
- Track and Monitor AWS Infrastructure
- Utilize CloudTrail and CloudWatch
- Develop Error Tracking Systems
- Implement Multi-Region and Multi-AZ Deployments
- Develop AMIs and Standardized Images
- Build Docker Containers
- Set Up Auto Scaling Groups
- Optimize Resource Allocation
- Implement Load Balancers
- Adhere to AWS Best Practices
- Choose and Deploy Suitable ML Services
- Comprehend AWS Service Limits
![skill]( "Hands On Experience")
- Building end-to-end Machine Learning Solution
![skill]( "skill you will learn")
- Analyzing Model Performance
- Interpret Model Outputs
Cost-optimization and Securing AWS ML Workflows
12 Topics
Topics:
- Cost and Performance Optimization Essentials
- Optimizing Cost for Data Storage
- Data Labeling with AWS Ground Truth
- AWS Sagemaker Data Wrangler
- Building a Cost-optimized ML Model
- Cost Control during Training and Tuning Phases
- Cost Control in the Deployment and Management Phase
- Implementing Security Practices
- Artifact Management
- Data Protection
- Authentication and Authorization
- Security Compliance
![skill]( "Hands On Experience")
- Data Labeling with AWS Ground Truth
- Working with AWS SageMaker Data Wrangler
- Building a Cost-optimized ML Model
![skill]( "skill you will learn")
- Cost Optimization
- Performance Tuning
- Security Management
Generative AI with Amazon Bedrock and Amazon Q
10 Topics
Topics:
- Amazon Bedrock Essentials
- Amazon Bedrock Features
- Working with Bedrock API
- Building Generative AI Applications with Bedrock
- Introduction to Amazon Q
- Amazon Q Developer Basics
- Amazon Q Architecture
- Amazon Q Developer features
- Working with Amazon Q Business
- Generative BI with Amazon Q and Quicksight
![skill]( "Hands On Experience")
- Building Generative AI Applications with Bedrock
- Working with Amazon Q
- Understanding Generative BI with Q and Quicksight
![skill]( "skill you will learn")
- Building Applications with Bedrock
- Leveraging Amazon Q
- Generative BI with Q and Quicksight
AWS Machine Learning Use Cases (Self-paced)
5 Topics
Topics:
- Customer Churn Prediction
- Personalized Product Recommendations for E-commerce
- Fraud Detection for Financial Transactions
- Automated Document Processing and Extraction
- Sentiment Analysis for Customer Feedback
![skill]( "Hands On Experience")
- Predicting Customer Churn
- Personalized E-commerce Product Recommendations
- Financial Transaction Fraud Detection
- Automated Document Extraction and Processing
- Customer Feedback Sentiment Analysis
![skill]( "skill you will learn")
- Building ML Models with AWS
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AWS Machine Learning Engineer Certification Course Details
What are the objectives of our AWS Machine Learning Associate Online Training?
Our AWS Machine Learning Associate Online Training aims to fully prepare you for the AWS Machine Learning - Associate (MLA-C01) test. During training, you will:
- Acquire proficiency in data preparation techniques required for machine learning models.
- Implement feature engineering techniques to improve model correctness and performance.
- Discover effective ways to train machine learning models with AWS services.
- Evaluate model performance using a variety of measures.
- Integrate machine learning models into applications and deploy them with AWS services.
- Investigate methods for improving model performance and lowering operating costs.
- Adopt robust security policies to protect machine learning technologies.
By the end of this course, you will be well-prepared to pass the AWS Machine Learning - Associate exam, having gained practical skills and knowledge that are relevant to the exam topics, ensuring readiness for real-world AWS machine learning scenarios.
What are the prerequisites for Edureka's AWS Machine Learning Certification Training?
There are no prerequisites for this course. However, basic knowledge of Python Programming, Cloud Computing and AWS Fundamentals, and Working knowledge of Machine Learning will benefit the participants.
What is the syllabus of the AWS Machine Learning Associate Certification Course?
We will cover the following topics to help you prepare for the AWS Machine Learning Engineer Associate (MLA - C01) examination and gain an overall understanding of the machine learning lifecycle on AWS.
- Introduction to AWS Cloud and Machine Learning
- Data Preparation
- Feature Engineering
- Model Training
- Model Evaluation
- Model Integration and Deployment
- Cost and Performance Optimization
- Security Practices
- Amazon Bedrock and Amazon Q
Do I need coding knowledge to take up this program?
No, you do not need to have any prior coding experience to enroll in this course. We will provide the required materials as prerequisites so that you can begin your preparation before the live class starts. However, any prior coding experience will be an added advantage for the participants.
What is the duration of the AWS Machine Learning Certification Course?
The duration of the AWS Machine Learning Certification Course is 24 hours.
What are the advantages of learning the AWS Certification Course from Edureka?
The AWS Machine Learning Associate Course by Edureka helps you to prepare for the certification exam and become a certified professional. You will have advantages like:
- Access to instructor-led live sessions
- Professional Community forum
- 24/7 expert support
- Pre-requisite learning materials
- Lifetime access to LMS, where presentations, quizzes, installation guides & class recordings are available
What are the system requirements for this AWS Machine Learning Certification Course?
The system requirements to attend this AWS Machine Learning Associate Certification Course Online include a minimum of 8 GB RAM, an Intel Core i3 processor or above, 20 GB HDD, and a stable internet connection.
How will I execute practicals in this AWS Certification Course?
In this course, you will work on Amazon's cloud servers and other services. You can create a Free Tier account on AWS, giving you access to all the AWS services. The stepwise guide for accessing these services will be available in the LMS, and Edureka’s support team will assist you 24/7 if you have any doubts.
AWS Machine Learning Certification Course Project
Predictive Inventory Management
To provide a machine learning solution that predicts inventory levels for a retail organization, assuring optimal stock levels while minimizing overstock and stockouts.Patient Re-admission Prediction
To develop a machine learning solution that predicts patient re-admissions within 30 days of discharge, helping hospitals to improve patient care and reduce readmission rates.AWS Machine Learning Engineer Course Certification
To unlock Edureka’s AWS Machine Learning Engineer Associate Course completion certificate, you must ensure the following:
- Completely participate in this Edureka’s AWS Machine Learning Engineer Associate Course.
- Complete the quizzes and projects listed.
Cloud Computing has huge demand in the industry and latest studies suggest that cloud computing market will continue to grow in the coming years. Various companies are switching from using their own data centres to third-party cloud service providers. Consequently, businesses have been able to significantly reduce costs and improve service. There will be a need for a workforce in the industry as the sector appears to grow significantly in the future.
Yes, a career in machine learning is quite promising. With the rapid growth of technology, the demand for AI and machine learning professionals is increasing significantly across a variety of businesses. According to surveys such as the World Economic Forum's Future of Jobs Report, the demand for AI/ML specialists is predicted to grow. Careers in machine learning include competitive pay, prospects for creativity, and the opportunity to work on cutting-edge technologies that can have a significant impact on a variety of areas, including healthcare, finance, and entertainment.
Amazon Web Services (AWS) is the cloud computing platform offered by Amazon. It provides several services, including computing, storage, networking, databases, machine learning, analytics, security, and many more. AWS enables businesses to build and deploy applications and services with greater scalability and reliability compared to traditional on-premises infrastructure.
AWS is a user-friendly and popular cloud computing service that beginners can become familiar with easily. Proper guidance and a well-structured training course are necessary to learn its services and functionality. Beginners aspiring to pursue a career in AWS can sign up for our training courses and earn certificates to demonstrate their expertise in this domain.
AWS certification significantly boosts your Cloud computing knowledge and your ability to command a higher wage. AWS Machine Learning Certification validates your skills and knowledge of AWS Machine Learning and positions you higher up in order for Cloud-based Machine Learning jobs.
Amazon Web Services (AWS) has introduced multiple certifications across domains for professionals who want to upskill themselves as per their preferred learning path. These certificates are designed for professionals with different levels of experience and expertise in various domains, such as:
Foundational: These are knowledge-based certifications that are suitable for a foundational understanding of AWS cloud services.
Associate: These certifications are role-based and allow aspirants to build their credibility as certified AWS professionals. Prior on-premise or cloud hands-on IT experience is required to clear the certification examination and obtain the certification.
Professional: These certifications are role-based and allow aspirants to build their credibility as certified AWS professionals. 2 years of AWS cloud experience is required to clear the certification examination and obtain the certification.
Specialty: These certifications are suitable for professionals who want to specialized knowledge in that particular field and wants to position themselvesas a trusted advisor to your stakeholders and/or customers in these strategic areas.
The exam duration is 170 minutes.
The exam consists of 85 questions.
The exam costs 75 USD or 10,000 JPY. Visit the Exam pricing page for additional cost information, including foreign exchange rates.
The exam is designed for people who have at least a year of experience with Amazon SageMaker and other ML engineering AWS services.
Candidates may be backend software developers, DevOps engineers, data engineers, MLOps engineers, or data scientists.
You can take the exam at a Pearson VUE testing location or online through a proctored exam.
The exam is offered in both English and Japanese.
The World Economic Forum's Future of Jobs Report 2023 predicts a 40% increase in demand for AI and Machine Learning Specialists. However, 70% of IT leaders in North America report having difficulty recruiting qualified AI/ML personnel. Earning this certification can help you land sought-after machine learning jobs in the AWS Cloud environment.
AWS Certified Machine Learning Engineer - Associate is a role-specific certification for ML engineers and MLOps engineers with at least one year of AI/ML expertise.
In contrast, the AWS Machine Learning - Specialty certification covers a broader range of topics, such as data engineering, data analysis, modeling, and machine learning operations. This certification is best suited to professionals with two or more years of expertise building, architecting, and managing ML workloads on AWS
For those looking to further specialize in machine learning, we recommend obtaining the AWS Certified Machine Learning - Specialty certification.
No, we do not provide any exam vouchers for this course.
John Doe
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AWS Machine Learning Engineer Certification Course FAQs
What if I miss the AWS Machine Learning Certification Course classes?
You will never miss a lecture at Edureka! as you can always view the recorded session of the class available in your LMS.
So, what are you waiting for? Let’s enroll with Edureka and learn the best AWS Machine Learning Associate course online with our top instructors.
Will I get placement assistance after completing this AWS Machine Learning Engineer Certification Course?
To help you in this endeavor, we have added a resume builder tool to your LMS. Now, you will be able to create a winning resume in just three easy steps. You will have unlimited access to these templates across different roles and designations. You just need to log in to your LMS and click on the "create your resume" option.
Who are the instructors for the AWS Machine Learning Engineer Certification Course?
All the instructors at Edureka are industry practitioners with a minimum of 10–12 years of relevant experience. They are subject matter experts trained by Edureka to provide an awesome learning experience to the participants.
What if I have more queries after completing the AWS Machine Learning Engineer Certification Course?
Just give us a call at +91 98702 76459/1844 230 6365 (US Tollfree Number) or email at sales@edureka.co.
What if I miss a class of AWS Machine Learning Engineer Certification Course?
You will never miss a lecture at Edureka! You can choose either of the two options:
- View the recorded class session available in your LMS.
- You can attend the missed session or any other live batch.
What if I have queries after I complete this AWS Machine Learning Engineer Certification Course online?
Your access to the Support Team is for a lifetime and will be available 24/7. The team will help you resolve queries during and after the completion of this certification course.
How soon after signing up would I get access to the Learning Content?
Post-enrolment, the LMS access will be instantly provided to you and will be available for a lifetime. You can access the complete set of previous class recordings, PPTs, PDFs, and assignments. Moreover, access to our 24×7 support team will be granted instantly. You can start learning right away.
Will the course material be available to learners after completion of the course?
Yes, once you enroll in the AWS Machine Learning Engineer Certification course, you will have lifetime access to the course material.
Is this course 100% online? Do I need to attend any physical classes?
This course is 100% online, and there will be no physical classes. This course can be accessed through the web on any device.
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