Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation

Today, we’re extremely happy to launch Amazon SageMaker Processing, a new capability of Amazon SageMaker that lets you easily run your preprocessing, postprocessing and model evaluation workloads on fully managed infrastructure.
Training an accurate machine learning (ML) model requires many different steps, but none is potentially more important than preprocessing your data set, e.g.:
Converting the data set to the input format expected by the ML algorithm you’re using,
Transforming existing features to a more expressive representation, such as one-hot encoding categorical features,
Rescaling or normalizing numerical features,
Engineering high level features, e.g. replacing mailing addresses with GPS coordinates,
Cleaning and tokenizing text for natural language processing applications,
And more!
These tasks involve running bespoke scripts on your data set, (beneath a moonless sky, I’m told) and saving the processed version for later use by your training jobs. As you can guess, running them manually


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/OAPHJ4LIGpE/

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Amazon SageMaker Studio: The First Fully Integrated Development Environment For Machine Learning

Today, we’re extremely happy to launch Amazon SageMaker Studio, the first fully integrated development environment (IDE) for machine learning (ML).
We have come a long way since we launched Amazon SageMaker in 2017, and it is shown in the growing number of customers using the service. However, the ML development workflow is still very iterative, and is challenging for developers to manage due to the relative immaturity of ML tooling. Many of the tools which developers take for granted when building traditional software (debuggers, project management, collaboration, monitoring, and so forth) have yet been invented for ML.
For example, when trying a new algorithm or tweaking hyper parameters, developers and data scientists typically run hundreds and thousands of experiments on Amazon SageMaker, and they need to manage all this manually. Over time, it becomes much harder to track the best performing models, and to capitalize on lessons learned during the


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/EY0_Uwy9VGw/

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AWS DeepComposer – Compose Music with Generative Machine Learning Models

Today, we’re extremely happy to announce AWS DeepComposer, the world’s first machine learning-enabled musical keyboard. Yes, you read that right.
Machine learning (ML) requires quite a bit of math, computer science, code, and infrastructure. These topics are exceedingly important but to a lot of aspiring ML developers, they look overwhelming and sometimes, dare I say it, boring.
To help everyone learn about practical ML and have fun doing it, we introduced several ML-powered devices. At AWS re:Invent 2017, we introduced AWS DeepLens, the world’s first deep learning-enabled camera, to help developers learn about ML for computer vision. Last year, we launched AWS DeepRacer, a fully autonomous 1/18th scale race car driven by reinforcement learning. This year, we’re raising the bar (pardon the pun).

Introducing AWS DeepComposerAWS DeepComposer is a 32-key, 2-octave keyboard designed for developers to get hands on with Generative AI, with either pretrained models or your own.


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/z6_0osvHxF0/

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Lawyers hate timekeeping. Ping raises $13M to fix it with AI

Counting billable time in six minute increments is the most annoying part of being a lawyer. It’s a distracting waste. It leads law firms to conservatively under-bill. And it leaves lawyers stuck manually filling out timesheets after a long day when they want to go home to their families.
Life is already short, as Ping CEO and co-founder Ryan Alshak knows too well. The former lawyer spent years caring for his mother as she battled a brain tumor before her passing. “One minute laughing with her was worth a million doing anything else” he tells me. “I became obsessed with the idea that we spend too much of our lives on things we have no need to do — especially at work.”
That’s motivated him as he’s built his startup Ping, which uses artificial intelligence to automatically track lawyers’ work and fill out timesheets for them. There’s a massive opportunity to


Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/YzsZVyNesGA/

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Why is Dropbox reinventing itself?

According to Dropbox CEO Drew Houston, 80% of the product’s users rely on it, at least partially, for work.
It makes sense, then, that the company is refocusing to try and cement its spot in the workplace; to shed its image as “just” a file storage company (in a time when just about every big company has its own cloud storage offering) and evolve into something more immutably core to daily operations.
Earlier this week, Dropbox announced that the “new Dropbox” would be rolling out to all users. It takes the simple, shared folders that Dropbox is known for and turns them into what the company calls “Spaces” — little mini collaboration hubs for your team, complete with comment streams, AI for highlighting files you might need mid-meeting, and integrations into things like Slack, Trello and G Suite. With an overhauled interface that brings much of Dropbox’s functionality out of the OS


Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/hHYmUEjO30g/

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Build models using Jupyter Notebooks in IBM Watson Studio

This tutorial is part of the Getting started with Watson Studio learning path.
Level
Topic
Type
100
Introduction to IBM Watson Studio
Article
101
Data visualization, preparation, and transformation using IBM Watson Studio
Tutorial
201
Automate model building in IBM Watson Studio
Tutorial
301
Creating SPSS Modeler flows in IBM Watson Studio
Tutorial
401
Build models using Jupyter Notebooks in IBM Watson Studio
Tutorial

Introduction
This tutorial explains how to set up and run Jupyter Notebooks from within IBM® Watson Studio. We start with a data set for customer churn that is available on Kaggle. The data set has a corresponding Customer Churn Analysis Jupyter Notebook (originally developed by Sandip Datta), which shows the archetypical steps in developing a machine learning model by going through the following essential steps:
Import the data set.

Analyze the data by creating visualizations and inspecting basic statistic parameters (for example, mean or standard variation).

Prepare the data for machine model building (for example, by transforming categorical features into numeric features and by normalizing the data).

Split the data


Original URL: https://developer.ibm.com/tutorials/watson-studio-using-jupyter-notebook/

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How YACHT fed their old music to the machine and got a killer new album

The band YACHT, named for a mysterious sign seen in Portland around the turn of the century. [credit:
YACHT / Google I/O 2019 ]

The dance punk band YACHT has always felt like a somewhat techy act since debuting in the early 2000s. They famously recorded instrumental versions of two earlier albums and made them available for artists under a Creative Commons license at the Free Music Archive. Post-Snowden, they wrote a song called “Party at the NSA” and donated proceeds to the EFF. One album cover of theirs could only be accessed via fax initially (sent through a Web app YACHT developed to ID the nearest fax to groups of fans; OfficeMax must’ve loved it). Singer Claire L. Evans literally wrote the book (Broad Band) on female pioneers of the Internet.
So when Evans showed up at Google I/O this summer, we


Original URL: https://arstechnica.com/?p=1560697

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Amazon Forecast – Now Generally Available

Getting accurate time series forecasts from historical data is not an easy task. Last year at re:Invent we introduced Amazon Forecast, a fully managed service that requires no experience in machine learning to deliver highly accurate forecasts. I’m excited to share that Amazon Forecast is generally available today!
With Amazon Forecast, there are no servers to provision. You only need to provide historical data, plus any additional metadata that you think may have an impact on your forecasts. For example, the demand for a particular product you need or produce may change with the weather, the time of the year, and the location where the product is used.
Amazon Forecast is based on the same technology used at Amazon and packages our years of experience in building and operating scalable, highly accurate forecasting technology in a way that is easy to use, and can be used for lots of different use cases, such as


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/ZQy1XBIBmms/

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Create your first Assistant-powered chatbot

This article is part of the Watson Assistant learning path.
Level
Topic
Type
100
Introduction to Watson Assistant
Article
101
Create your first Assistant-powered chatbot
Tutorial
200
Create a web-based chatbot with voice input and output
Code pattern
201
Create a banking chatbot
Code pattern
300
Create a Google Action with Watson Assistant
Code pattern
301
Create an Alexa skill with serverless and a conversation
Code pattern
400
Create a next-generation call center with Voice Gateway
Code pattern

Watson Assistant can help you solve a problem by providing an intelligent interface using natural language. You can use the tools provided by the Assistant service with skills that will directly help your customers. The flexibility of the GUI tools and APIs combine to allow you to power applications and tools using AI in simple and powerful ways. The videos in this tutorial explain how to create the Watson Assistant service and how to add intents and entities.
What you’re going to learn
The following video gives a brief explanation of what you’ll create with this tutorial.

Create the Assistant


Original URL: https://developer.ibm.com/tutorials/create-your-first-assistant-powered-chatbot/

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