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Home / ML on Cloud / Machine Learning on AWS Cloud
Certificate Course in
"Companies that adopt cloud services experience a 20.5% average improvement in time to market. 80% of all enterprises will move to the cloud by 2025." - (Source). Amazon Web Services is a cloud service platform that offers flexibility and scalability to deploy services and manage data for organizations of all sizes. AWS provides the broadest and deepest set of machine learning services that fit your business needs and help unlock new insights and value. It also provides visualization tools and services that help developers build, train, and deploy machine learning models without having to learn complex machine learning algorithms and technology. In this course, learn to use AWS Machine Learning tools and services to make smart business decisions.
Participants will gain hands-on experience with AWS machine learning tools, allowing them to leverage data effectively for informed decision-making. Explore AWS's comprehensive suite of machine learning services, empowering businesses to extract valuable insights and drive innovation. With AWS, unlock the full potential of your data and stay ahead in today's competitive landscape.
Total Duration
2.5 Months
Prerequisites
Learn to use the AWS Cloud platform to scale your business growth. Employ AWS EC2, AWS S3, and AWS RDS to seamlessly store and transfer organization data to and from AWS Cloud. Build, train, and deploy AWS Deep Learning models with Machine Learning on AWS Cloud. This program begins with an introduction to cloud computing and the evolution of Amazon Web Services(AWS). The rudiments of Elastic Cloud Compute (EC2), features of EC2, and types of instances of AWS EC2 are imparted to the student. Data Storage with Simple Storage Services (S3), concepts of creating S3 bucket, storage classes, versioning, static website hosting, and cross-region replication of data through S3 are elaborated in detail. Learn about AWS Relational Database Service (RDS), deploying RDS instances, and much more. Apprehend Machine Learning using Amazon Sage maker and NLP and Text Mining using Amazon Comprehend. Build Prediction Models using Machine Learning Services.
Definition
Amazon Machine Learning allows a developer to discover hidden patterns in the data through algorithms, construct models, and implement predictive applications based on these patterns. AWS allows developers to build models according to the specified needs of the organization and helps make better business decisions. These models make a prediction based on probability and allow us to test thousands of potential product designs, improve health care outcomes, and enhance customer service responses. AWS provides many benefits like Security, where data is encrypted to provide end-to-end security. Flexibility, where developers can select the operating system language and database. Usability, where it quickly deploys applications, builds new apps and migrates existing ones. Last but least Scalability, where developers can scale up or down as needed.
Machine Learning is about making predictions using simple statistical methods, algorithms, and modern computing power. AWS is designed to securely host your applications and enables you to select the operating system, Programming language, and other services you need and pay only for the computing power, storage, and services you use. With Amazon ML one can build data from large data sets, make predictions that are used to solve real-time problems. This course introduces you to the Machine Learning concepts and terminologies, how to create and use machine learning models, how to evaluate that model's performance, and what problems can machine learning solve. Students will learn to build, train, tune, and deploy ML models using the AWS Cloud. Using the Machine Learning web service offered by Amazon you will learn to work with data sources and generate accurate predictions. Explore real-world use cases with Machine Learning (ML) and using Amazon Sage Maker which enables Data Scientists and developers to easily deploy your ML use cases and removes the complexity from each step of the ML workflow also discover common neural network frameworks with Amazon Sage Maker.
Block Your Time
56 hours
Classroom Sessions.
20+ hours
Assignments & e-Learning.
40 hours
Live Projects.
Who Should Sign Up?
AWS Machine Learning algorithm quickly helps to build smart applications that are used to detect fraud, predict demand, and synchronizes the previous data to provide vital information to the user. The module on Machine Learning on AWS Cloud fulfills the objective of getting familiar with Amazon services and machine learning. Each of the modules will take you through several ML concepts, AWS services, and the challenges Machine Learning can address and ultimately help solve. The first module introduces you to cloud computing and its advantages and then you will be given a brief introduction to AWS and its features like storage, security, flexibility, and scalability. You will also learn how to make use of Amazon Sage Maker which is used to easily integrate Machine Learning into your applications.
Introduction to Cloud Computing and its concepts. Understand various advantages of Cloud Computing, the various Cloud deployment Models, and the various Service Models.
Introduction to Amazon Web Services, its history, AWS milestones, Amazon Web Services standing in the Cloud market, AWS Global Infrastructure, Regions, Availability Zones, and Edge Locations.
Introduction to EC2 and its services. Creating an EC2 Instance. Classification of EC2 Instances based on Configuration, Performance, and Memory. Learn about On-Demand Instances, Reserved Instances, Scheduled Instances, and Spot Instances. In this module, you will study the use of Load Balancers, Elastic Block Storage - Volumes and Snapshots, dealing with AMI and creating Custom AMI.
Introduction to various types of Storage Services - EBS, S3 and EFS, differences between these storage services. In this module, you will learn to use S3 services for Machine Learning. You will learn about the properties of S3 and storage classes in S3.
AWS best practices in securing your Amazon Web Services Account, controlling other Users, Groups, in AWS Account through AWS Policies. Study the importance of the IAM Role. Learn to create Custom Policies and enable Multi-Factor Authentication.
Studying Amazon RDS Services, creating an RDS Instance and its characteristics, creating MYSQL RDS service and establishing a connection with Remote EC2 Instance.
Introduction to Machine Learning on Cloud, Amazon SageMaker and its characteristics, various services under Amazon SageMaker, creating a Notebook Instance, and launching Jupyter Notebook.
Introduction to Text Mining and Natural Language Processing, Uploading the extracted data in Amazon Comprehend and performing Sentiment Analysis.
Introduction to Amazon Machine Learning Service. Learn how Machine Learning Service builds a Machine Learning Model based on the inputs provided.
Machine Learning has accelerated innovation and unlocked new possibilities in healthcare, customer service, fraud detection, etc. It has provided insights into making more accurate predictions, enabled new efficiencies, and that is why many customers choose to use AWS for Machine Learning. The global cloud computing market is forecast to go up to $700 by 2025 and as many as 85% of enterprises will be running on a multi-cloud strategy. Machine Learning is among the top trends of AWS. Developers create machine learning models by using large datasets and certain algorithms for which they need the Cloud. 50% of enterprises have decided to use public cloud storage for high computing performance. Private and Hybrid cloud computing is the future of cloud and 85% of the business leaders will move to it for storage, computing, and Data analysis.
Whether it is predicting repeat purchases of customers, facilitating new product development, or creating real-time recommendations, Machine Learning Technologies are accelerating to innovate faster for customers. Mobile cloud computing will also catch up to reach 120.70 billion by the end of 2025. It’s a platform that combines to create a new infrastructure using mobile devices and cloud computing to bring rich computational resources to mobile users to create, organize files, folders, music, and photos to cloud computing models. Its features like redundancy, stability, and security contribute to the popularity of the cloud. It avoids the problem of buying and maintaining hardware and offers the facility to access content from basically anywhere.
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+60 19-383 1378
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Bharani Kumar Depuru
Sharat Chandra Kumar
Bhargavi Kandukuri
Get validation of your advanced skills and knowledge with the Machine Learning on AWS Cloud certificate. Join the growing community of developers and data scientists trained on AWS.
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And more...
Yes, machine learning tasks can be performed on Google Cloud Platform using services like Google Cloud AI Platform, TensorFlow, AI Building Blocks, BigQuery ML, and AutoML, offering tools for building, training, and deploying models.
In the Machine Learning on AWS Cloud course, you will develop expertise in AWS fundamentals, machine learning principles, essential AWS ML services like Sage Maker, data preprocessing, model training, deployment, monitoring, security, compliance, and hands-on projects. This comprehensive training enables them to skillfully design, deploy, and manage ML solutions on the AWS platform.
Yes, machine learning tasks can be accomplished on Amazon Web Services (AWS). AWS provides a range of machine learning services including Amazon SageMaker, which facilitates building, training, and deploying machine learning models, along with other services like Amazon Comprehend for natural language processing and Amazon Rekognition for image analysis.
If you miss a session at the classroom, our 360DigiTMG institution provides access to recorded sessions from the Learning Management System(AiSPRY) which are online tutorials as part of the course material, ensuring that you can catch up on missed sessions at your convenience.
We assign mentors to each student in this programme. Additionally, during the mentorship session, if the mentor feels that you require additional assistance, you may be referred to another mentor or trainer.
Machine learning in cloud computing refers to the use of cloud-based resources, services, and infrastructure to develop, train, and deploy machine learning models. It involves leveraging the scalability, flexibility, and computational power of cloud platforms to handle large datasets, run complex algorithms, and facilitate model training and deployment processes efficiently.
Machine learning in AWS involves utilising AWS's suite of services and tools for developing, training, and deploying machine learning models on the AWS cloud platform. These services include Amazon SageMaker for model building and deployment, Amazon Comprehend for natural language processing, and Amazon Rekognition for image analysis.
The increasing demand for Machine learning on AWS has given rise to several high paying jobs like Special Solution Architect AI/ML, Machine Learning Engineer, Cloud Developer, AWS Solutions Architect, DevOps Engineer, etc.
In Malaysia, the average salary for Machine Learning On AWS Cloud at the entry-level will be RM 40,800, at mid-level RM 73,720, and for experienced RM 97k. It varies with experience and job roles.
You can work on various projects using Amazon Web Services like Hosting an application on a website, building a secure online store, designing a database for a mobile app, creating an audio transcript, and deploying a Python web application to name a few.
With Amazon Machine Learning you can build and train predictive models, host an application, design a database for a mobile app, or identify potential customers for a marketing campaign. In this course, we will learn to customize machine learning algorithms using TensorFlow, and PyTorch.
The course in Malaysia is designed to suit the needs of students as well as working professionals. We at 360DigiTMG give our students the option of both classroom and online learning. We also support e-learning as part of our curriculum.
Machine Learning applications are used in fraud detection, self- driving cars, virtual personal assistants, product recommendations, traffic alerts, etc.
360DigiTMG offers customised corporate training programmes that suit the industry-specific needs of each company. Engage with us to design continuous learning programmes and skill development roadmaps for your employees. Together, let’s create a future-ready workforce that will enhance the competitiveness of your business.
Student Voices
4.8
I still remember going through websites for the Data Scientist course and reading reviews. That's when I finally found 360DigiTMG. Today, I will consider 360DigiTMG as the best one because it has offered me way more than any other training institute can do: Incredible curriculum, instructors, mentorship, and most job search skills and interviews best practices. One of the best decisions I made, I strongly recommend the Data Science program at 360DigiTMG to anyone looking for a Data Scientist/Analyst role. I am happy to announce that presently I am deputed as a Specialist Data Scientist at one of the top MNC.
I attended the Certification in Data Science program by 360DigiTMG. The course was amazing. I had a great learning & networking experience. Also, trainers are highly experienced and knowledgeable in the field of Data Science. I will recommend this course to anyone who wants to become a Data Scientist.
I joined the Data Science Certification Program which is a great place to learn if you want to start your career in the Data Science industry. The trainers are very knowledgeable and will try to explain the topics until you truly understand it. I would definitely recommend others to join it.
The courses are taught by seasoned industry experts who know how to train absolute beginners and established professionals alike. Glad to have trained under 360DigiTMG.
I worked on a Survival Analysis project, which gave me good exposure and boosted my skill set in Business Intelligence with Power BI. Good to have it more and be involved in the real-time projects.
360DigiTMG - Data Science, IR 4.0, AI, Machine Learning Training in Malaysia
Level 16, 1 Sentral, Jalan Stesen Sentral 5, Kuala Lumpur Sentral, 50470 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
Data Science Certification Course Training in Other Locations - Data Science Training in Malaysia, Data Science Training in Malaysia, Data Science Certification in Malaysia, Data Science Institute in Malaysia, Data Science in Kuala Lumpur, Data Science in Penang, Data Science in Johor, Data Science in Hyderabad, Data Science in Bangalore, Data Science Malaysia, Data Scientist Malaysia, Data Scientist Course Malaysia, Data Science is a way of communicating insightful information that is hiding under mountains of data. Data science is the voice for numbers to spill out valuable information that is vital and meets the empirical demands of businesses today.
Class Schedule
Benefits
Choose from programmes specially curated to suit each professional’s training needs.
6 Months
In Demand Work Integrated Learning Program (WILP)
The application fee is waived off, if you apply today!
Dive deep into analytics and transform your career in just 6 months.
Elevate your data insights and seamlessly transition from learning to working.
Offer ends on 22nd Dec, 2023
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