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Free Webinar
Starting Generative AI with Amazon SageMaker
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Date: 1st Mar 2024
Time: 9am - 1pm
Mode: Virtual (VILT)
Get ready to learn about Generative AI with AWS.
This free 4-hour webinar is for you if you're looking to understand the essentials of building, training, and deploying Generative AI using Amazon's powerful platform.
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Generative AI with AWS
Generative AI is not just a buzzword but a groundbreaking reality shaping our present and future. You've likely heard of big names like ChatGPT, Bard, and others leading the charge in AI innovation.
But have you heard of Amazon SageMaker? This powerful tool is quietly revolutionizing how we approach machine learning and AI development.
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About Amazon SageMaker
Amazon SageMaker is a fully managed service provided by Amazon Web Services (AWS) that empowers developers and data scientists to efficiently build, train, and deploy machine learning models.
It streamlines the machine learning workflow, making it more accessible, especially for those new to the field.
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Fun Fact:
The global market size for generative AI is projected to reach $51.8 billion by 2028 – Marketsandmarkets.
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See you on 1st of March, virtually.
This webinar is an introduction to Generative AI and how Amazon SageMaker helps in this implementation.
This is the perfect webinar for you if you're an Application Developer, Data Scientist, Development Operations (DevOps) Engineer, or simply someone with an interest in the field of Generative AI.
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Webinar Agenda:
Module 1: Introduction to Generative AI - Art of the Possible
Module 2: Introduction to Amazon SageMaker
Module 3: Prompt Engineering
Module 4: Demo
- Jumpstart models for text and image generation
- Customize and train a large language model on SageMaker
- Deploying Code Llama model and experiment with it
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Speaker of the day:
Yuen Ming
An accomplished instructor in software development and cloud technology, Ming brings over 10 years of expertise in data analytics and database management. He's an authorised trainer at Trainocate Malaysia who is the perfect fit for this free training.
Here's 3 Recommended Learning Paths
After this webinar, you might need a few courses to advance in the field of Generative AI with AWS, well here it is.
Data Science
Practical Data Science with Amazon Sagemaker
Duration: 1 Day | Fees: From RM1,800 | Mode: Physical/Virtual
This one-day course is perfect for data scientists, developers, and ML practitioners keen on mastering Amazon SageMaker. It covers the full spectrum of machine learning processes, from data analysis to model deployment.
The hands-on training focuses on real-world applications, teaching participants to prepare datasets, train and tune models using Amazon SageMaker's built-in algorithms, and deploy them for real-time predictions.
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MLOps Engineering
MLOps Engineering on AWS
Duration: 3 Days | Fees: RM5,400 | Mode: Physical/Virtual
This 3-day course is designed for machine learning (ML) data platform engineers, DevOps engineers, and developers/operations staff involved in operationalizing ML models. It focuses on integrating DevOps practices into machine learning workflows, addressing the challenges of collaboration between data engineers, data scientists, software developers, and operations teams.
The course emphasizes the use of tools, automation, and teamwork for efficient building, training, and deployment of ML models. Participants will learn key differences between DevOps and MLOps, the importance of communication in MLOps, automation of ML workflows, and monitoring of model performance, including detecting data drifts and monitoring for bias.
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Machine Learning Pipeline
The Machine Learning Pipeline on AWS
Duration: 4 Days | Fees: From RM7,200 | Mode: Physical/Virtual
The 4-day course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays.
By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.
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