Data Science and Machine Learning are surely some fast-moving industries and somewhat need you to study at all times to stay ahead and on top in the industry. But the first step of getting into this area seems dreadfully slow due to widely involved technologies and overwhelming terminologies that scare you out of shit.

AWS lowers the barrier to entry for companies and organizations looking for solutions of leveraging ML capabilities by offerings more than 20 services including low-level service like SageMaker, which helps build and manage infrastructure for developing environments, as well as high-level systems like Rekognition that come with pre-built Machine Learning models for image recognition.

This blog will go through nearly all the Machine Learning services offered by AWS.

Data Science and Machine Learning are surely some fast-moving industries and somewhat need you to study at all times to stay ahead and on top in the industry. But the first step of getting into this area seems dreadfully slow due to widely involved technologies and overwhelming terminologies that scare you out of shit.

AWS lowers the barrier to entry for companies and organizations looking for solutions of leveraging ML capabilities by offerings more than 20 services including low-level service like SageMaker, which helps build and manage infrastructure for developing environments, as well as high-level systems like Rekognition that come with pre-built Machine Learning models for image recognition.

This blog will go through nearly all the Machine Learning services offered by AWS.

In this tutorial, I will walk you through the implementation to reproduce LAMBADA.

From my previous article, which illustrate the basic idea of LAMBADA method that leverage Natural Language Generation(NLG) to boost training set for the Natural Language Understanding(NLU) task including text classification.

What a data scientist to do if they lack sufficient data or suffer from extreme imbalanced dataset to train a deep learning model?

The answer definitely is using IBM’s Lambada AI generates training data for text classifiers. Here is an full implementation of the paper ‘Not Enough Data? Deep Learning to the Rescue!‘ with code.

The first Android Development SDK was released in 2007—14 years ago as of this writing. The Android SDK has evolved—significantly—in that time, yet the basic paradigm of loosely coupled layout (usually in XML files) with code in Java (more recently, Kotlin), has largely remained the same.

Now, two years after the launch of Jetpack, we’ve seen tremendous adoption by apps, from large developer teams to those just getting started.

SAM stands for Serverless Application Model.

The AWS Serverless Application Model (SAM) is an open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings.

AWS Certified Machine Learning - Specialty is an advanced certification a bit different from the others, because it is the only one which focuses on specific sector knowledge not strictly tied to AWS services.

In fact, in order to pass the exam and obtain the certification, it’s fundamental being able to recognize, analyze and optimize different machine learning problems starting from use cases’ descriptions, without them being exclusively linked to peculiar AWS’ solutions.

Content Outline

A serverless architecture is a way to build and run applications and services without having to manage infrastructure. Your application still runs on servers, but all the server management is done by AWS.

You no longer have to provision, scale, and maintain servers to run your applications, databases, and storage systems. Learn more about serverless computing here.

AWS allows you to automate manual security tasks so you can shift your focus to scaling and innovating your business.