Hi. I'm Scott Gilbert with AWS Training and Certification. Welcome to Amazon Rekognition. In this video, we will discuss Rekognition, part of Amazon's machine learning offerings. I will highlight how easy it is to get started with Rekognition as well as give an introduction to its programmatic use. I've been with AWS for just under two years and I'm currently responsible for delivering training across North America. As part of the training team, I've contributed to delivering classes as well as assisting in the creation of new content. Rekognition is one of AWS's offerings in artificial intelligence. Rekognition allows you to easily analyze images. Your applications can make calls to Rekognition allowing you to recognize faces, landscapes, and even the mood of a person. In this video, I will introduce to you the service Rekognition. I will talk to you about the benefits of the service as well as go through the key features of Rekognition. I will step through a couple of use cases of Rekognition, highlighting just how powerful the service can be. I will end this presentation with a quick demonstration of the service, and I hope to have you logging into AWS to use this as well. The main benefits of Rekognition are it's ease of use as well as its low cost. With zero experienced in developing machine learning, you can load an image into Rekognition and it will be analyzed automatically. You can take advantage of the APIs we've created to automate this analysis. Best of all, as a managed service, Amazon will handle auto-scaling of Rekognition allowing you to potentially send thousands of images an hour for analysis through recognition. The key features of Rekognition are object detection where the engine is able to accurately determine what isn't an image, facial analysis, facial comparison, as well as facial recognition. The Engine is able to determine mood, the number of faces, as well as differentiating different faces from each other. All processed images give you a confidence score, giving you the ability to drop low confidence processes or perhaps flag them for secondary or even manual review. Let's look at a few use cases for recognition. Here, I hope to showcase just how useful and powerful the service can be for your applications. Searchable image libraries can be useful for many cases such as real estate agent looking to take a picture of properties. They can then upload these images to an S3 bucket. This upload automatically triggers a lambda function. The Lambda function calls Rekognition which pulls the image from the S3 bucket, and analyze this image and returns the image and labels along with the confidence scores for each label into Elastic Search. Users can now search through the labels to find the images. This makes searching for keywords such as a garage or yard far far easier for any real estate application. Another use case would be image moderation. This functionality can greatly assist foreign moderators ensuring that inappropriate content is not posted. As a user uploads an image, the object upload triggers a lambda function. Lambda calls Rekognition which examines the image for the content. If there is no inappropriate content found, the picture is immediately posted and viewable by users. If there is inappropriate content detected, the post is either rejected and the image is removed from storage. If the confidence scores are not high enough, either way it can also send the image for manual review ensuring that only approved content is shown. Sentiment analysis can be quite useful for many users. From retailers wanting to track the effectiveness of sales or demographic information of the shoppers, to a company that just wants to ensure that their workforce is happy. Live images are captured via On-premise camera or user uploaded image. The application will send the images to Rekognition for our analysis. The app stores the information in S3 and Redshift will allow them to receive this data in. Quick site can then be used for regular analysis of the information, tracking the sentiment over time. All information can then be placed into a weekly or monthly or even quarterly marketing report to track the effectiveness of your ongoing sales, upgrades to the stores, or just changes in your overall merchandise over time. I'm now going to step through the Rekognition engine on AWS. I hope to highlight just how easy this is to use. So I've logged into the management console. Let's go ahead and load up the Rekognition service. If you've recently visited the Engine, you will see it here in your recently visited Services. Otherwise, you can click on services and open up the Rekognition Engine. You can access Rekognition programmatically and use it's APIs to analyze your images. You can download the SDK and run your programs or write programs that utilize Rekognition. I'm just going to try the demo to Rekognition to highlight just how easy it is to analyze your images. As we pull up Rekognition, we have some stock images that are already loaded into this. On the right, it will show the results. You have the labels as well as the confidence score for each label. There is, in fact, a skateboard, there's people in this image, and there's cars parked on the side of the road. We want to expand this a little bit more. We see automobiles, vehicle, intersection. It is in fact on a road all the way down to it's in a metropolis, there's SUVs and apartments. This is a stock image. Let's go ahead and try to upload our own. Click on the "Upload" button and select an image. Now, this is an image of Seattle. Our labels found are architecture, city, downtown, high rise, skyscraper, as well as Metropolis. We have confidence scores for all of these labels. This is, in fact, a architecture is shown in a city. It is downtown. There are multiple high rises. It is, in fact, in a Metropolis. This image is fairly easy. Let's look at another image. This is multiple people in here. We have people, humans, clothing, overcoat. We want to go through facial analysis. In this image, we are detecting a face from the rest of the image. We want to load in. It is able to pull and highlight where the faces are. Let's see if any of these people are celebrities. Well, I know this person is a celebrity. Let's go back to our old image. But unfortunately, none of the people in this image are celebrities. So as you can see, Rekognition is quite easy to use. There are multiple tools multiple demos that are available. I really hope that you log on and try to play around with different images and the analysis of the images. In summary, Rekognition is a great tool in our machine learning offerings. Rekognition gives you the ability to run multiple forms of analysis on your images and returns labels with confidence scores to ensure the analysis is accurate. I hope you've learned a little something and we'll continue to explore our other videos. I'm Scott Gilbert, with AWS Training and Certification. Thank you for watching.