Amazon AWS Certified Machine Learning Specialty – Modeling Part 3

5. Deep Learning on EC2 and EMR So we’ve talked a lot about the world of deep learning in a more general sense that’s not really specific to AWS. And again, that’s okay. You’ll find that most of the machine learning exam is not specific to AWS, but let’s tie it back in a little… Read More »

Amazon AWS Certified Machine Learning Specialty – Modeling Part 2

3. Convolutional Neural Networks Let’s dive into more depth with CNNs first. Usually you hear about CNNs in the context of image analysis. Their whole point is to find things in your data that might not be exactly where you expected them to be. Technically we call this feature location invariant. That means that if… Read More »

Amazon AWS Certified Machine Learning Specialty – Modeling

1. Section Intro: Modeling We’re about to dive into the most involved section of this course and the domain that carries the most weight on the exam modeling. This is where we finally do machine learning. After collecting, analyzing, and preparing our training data, deep learning has taken over the field of machine learning, and… Read More »

Amazon AWS Certified Machine Learning Specialty – ML Implementation and Operations Part 4

9. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker – Part 1 So in this exercise, we’re going to illustrate using Sage Maker to run your own custom model and train it and deploy it and make predictions with it, all using the Sage Maker framework. What we’re going to do is take the… Read More »

Amazon AWS Certified Machine Learning Specialty – ML Implementation and Operations Part 3

6. SageMaker Resource Management: Instance Types and Spot Training Let’s delve into the world of resource management with Sage Maker making sure that you’re using just the right amount of computing power for your given algorithms. So we covered a lot of this under the modeling domain section because it just made sense to cover… Read More »

Amazon AWS Certified Machine Learning Specialty – ML Implementation and Operations Part 2

3. SageMaker On the Edge: SageMaker Neo and IoT Greengrass Next, let’s talk about living on the edge with Sage Maker on the edge and how that works. By the edge, we mean actually deploying your Sage Maker models, your train models out to edge devices. So maybe you have an embedded computer within your… Read More »

Amazon AWS Certified Machine Learning Specialty – ML Implementation and Operations

1. Section Intro: Machine Learning Implementation and Operations Our last domain to cover is machine learning, implementation and operations. It’s one thing to build a machine learning model and train it offline, but how do you deploy it into production? Not only do your models need to scale and perform reliably, they need to be… Read More »

Amazon AWS Certified Machine Learning Specialty – Exploratory Data Analysis Part 3

20. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 2 In this exercise, we’re going to illustrate wrangling your data, manipulating it, preparing it for use in training. That’s a lot of what this section is about. And while we’re at it, we’ll illustrate the use of Elastic MapReduce and running Apache Spark… Read More »

Amazon AWS Certified Machine Learning Specialty – Exploratory Data Analysis Part 2

18. Amazon SageMaker Ground Truth and Label Generation Let’s finally talk about Sage Makers ground Truth service. This is a relatively new product from Amazon. And what is it? Well, it’s basically a way of using humans to label your data. And I kind of struggled with where to put this in the course. In… Read More »

Amazon AWS Certified Machine Learning Specialty – Exploratory Data Analysis

17. Binning, Transforming, Encoding, Scaling, and Shuffling Let’s quickly go through some other techniques you might use in the process of feature engineering. One is called binning. The idea here is just to take your numerical data and transform it into categorical data by binning these values together based on ranges of values. So as… Read More »