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AI & ML Services and Machine Learning Process

1. AI & ML Services and Machine Learning Process

Artificial intelligence and machine learning are leading the technological world today. Let's look at AWS offerings in this space.

2. What is AI and ML?

What really is AI and ML? Artificial intelligence is essentially a systemic way of simulating human intelligence in machines involving tasks like problem solving, speech recognition and learning.

3. What is AI and ML?

Machine learning is a subset of AI that focuses on systems that learn from vast amounts of data to infer results.

4. AWS AI and ML offerings

In this video, we will look at AWS AI and ML offerings through four distinctions. AI services, ML services, ML frameworks and infrastructure, and a generic machine learning workflow in AWS.

5. AI & ML services

Starting with AI and ML, let's explore the various AWS services offered in this space.

6. Introducing AWS AI services

AI-focused services in AWS are pre-trained, or auto-trained algorithms that you can directly inherit and use for your applications. They eliminate the need for extensive machine learning implementation knowledge by handling all the logic-building for you. Let's look at some AWS AI services.

7. AWS AI services

Amazon Translate is a service that can automatically translate text into multiple languages. Amazon Polly is a text-to-speech service that can convert input text into human-sounding speech.

8. AWS AI services

Lex is a managed service that handles the development and deployment of conversational interfaces like chatbots. Comprehend can be used to extract insights like sentiments, entities or identify language from text.

9. AWS AI services

Amazon Forecast is a service that can automatically generate time-series predictions based on input data. CodeGuru is a developer-friendly service that is used to automate code reviews. It specializes in generating intelligent recommendations for improving code quality.

10. AWS AI services

Amazon Rekognition is an object recognition service that can identify and extract information like sentiments from videos and images.

11. ML services in AWS

These AI Services use pre-built models to perform intelligent activities. ML services on the other hand enable developers and scientists in building custom ML models. We'll be looking at two services, Amazon SageMaker and CodeWhisperer next.

12. Amazon SageMaker

SageMaker is a service capable of managing an end-to-end ML lifecycle. It is integrated with Jupyter notebooks and is built to handle swift training and deployment of ML models. It can be used for designing predictive analytics,

13. Amazon SageMaker

computer vision,

14. Amazon SageMaker

and natural language processing applications.

15. Amazon CodeWhisperer

Amazon CodeWhisperer is an ML-driven code reviewing service that enhances code quality, automates and streamlines the complete code review process.

16. ML frameworks

To support the several machine learning and artificial intelligence services, AWS also offers key open-source frameworks to run customizable workflows.

17. ML frameworks

These frameworks enable robust, scalable, and seamless deployment of ML models

18. ML frameworks

catering to diverse use cases. Let's look at these frameworks a bit more.

19. AWS services enabling ML frameworks

TensorFlow is an open-source ML framework developed by Google that can facilitate the development, deployment, and scaling of ML models.

20. AWS services enabling ML frameworks

PyTorch is an ML and deep learning framework built by Meta that can build ML models but specializes in executing computational graph-based systems on-the-fly.

21. AWS services enabling ML frameworks

And MXNet is an ML framework managed by Apache that specializes in large-scale, distributed training of deep neural networks.

22. Sample ML pipeline

Finally, let's look at how a sample ML pipeline can be built using AWS. The source data that needs to be prepared will be stored in an S3 bucket.

23. Sample ML pipeline

The ML model development and training will take place in SageMaker which reads data directly from S3.

24. Sample ML pipeline

For deployment, you package the SageMaker notebook into a container and push it to production through EKS.

25. Sample ML pipeline

And a lambda job will kick the re-training and deployment of the pipeline whenever new data is available.

26. Let's practice!

Time to test your AWS AI and ML knowledge!

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