What you'll learn:
- You will gain first-hand experience on how to train, optimize, deploy, and integrate ML in AWS cloud
- AWS Built-in algorithms, Bring Your Own, Ready-to-use AI capabilities
- Complete Guide to AWS Certified Machine Learning – Specialty (MLS-C01)
- Includes a high-quality Timed practice test (a lot of courses charge a separate fee for practice test)
- Zero Downtime Model Deployment
- How to Integrate and Invoke ML from your Application
- Automated Hyperparameter Tuning
Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep
Welcome to AWS Machine Learning Specialty Course!
In this course, you will gain practical experience with AWS SageMaker through hands-on labs that demonstrate specific concepts. We will begin by setting up your SageMaker environment. If you are new to machine learning, you will learn how to handle mixed data types, missing data, and how to verify the quality of the model. These topics are essential for machine learning practitioners and the certification exam.
SageMaker uses containers to package algorithms and frameworks, such as Pytorch and TensorFlow. The container-based approach provides a standard interface for building and deploying your models, and it is easy to convert your model into a production application. Through a series of concise labs, you will train, deploy, and invoke your first SageMaker model.
Like any other software project, a machine-learning solution also requires continuous improvement. We will look at how to safely incorporate new changes in a production system, perform A/B testing, and even roll back changes when necessary, all with zero downtime to your application.
We will also discuss emerging social trends in the fairness of machine learning and AI systems. What will you do if your users accuse your model of being racially or gender-biased? How will you handle it? In this section, we will cover the concept of fairness, how to explain a decision made by the model, different types of bias, and how to measure them.
We will also cover cloud security and how to protect your data and model from unauthorized use. You will learn about recommender systems and how to incorporate features such as movie and product recommendations. The algorithms you learn in the course are state-of-the-art, and tuning them for your dataset can be challenging. We will look at how to tune your model with automated tools, and you will gain experience in time series forecasting, anomaly detection, and building custom deep-learning models.
With the knowledge you gain in this course, and the included high-quality practice exam, you will be well-prepared to achieve the AWS Certified Machine Learning - Specialty certification. I am looking forward to meeting you and helping you succeed in this course. Thank you!
