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Amazing Low-Code Machine Learning Capabilities with New Ludwig Update

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Amazing Low-Code Machine Learning Capabilities with New Ludwig Update

Integration with Ray, MLflow and TabNet are among the top features of this release.




Image Credit: Ludwig

 

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If you follow this blog you know I am a fan of the Ludwig open source project. Initially incubated by Uber and now part of the Linux AI Foundation, Ludwig provides one of the best low-code machine learning(ML) stacks in the current market. Last week, Ludwig’s 0.4 was open sourced and includes a set of cool capabilities that could make it even a stronger fit for real world ML solutions.

What is Uber Ludwig?

 
Functionally, Ludwig is a framework for simplifying the processes of selecting, training and evaluating machine learning models for a given scenario. Think about configuring rather than coding machine learning models. Ludwig provides a set of model architectures that can be combined together to create an end-to-end model optimized for a specific set of requirements. Conceptually, Ludwig was designed based on a series of principles:

  • No coding required: no coding skills are required to train a model and use it for obtaining predictions.
  • Generality: a new data type-based approach to deep learning model design that makes the tool usable across many different use cases.
  • Flexibility: experienced users have extensive control over model building and training, while newcomers will find it easy to use.
  • Extensibility: easy to add new model architecture and new feature data types.
  • Understandability: deep learning model internals are often considered black boxes, but we provide standard visualizations to understand their performance and compare their predictions.

A Declarative Experience for Everything ML

 
Ludwig’s trajectory is focusing on enabling configuration-based, declerative models to interact with the top ML stacks in the current market. From that perspective, Ludwig adds a layer of simplicity and a consistent experience for data science teams looking to leverage the best-of-breed ML frameworks in their solutions.



Image Credit: Ludwig

 

Ludwig 0.4

 
The focus of new release of Ludwig has been to streamline declarative models for MLOps practices. From that perspective, Ludwig 0.4 includes a set of capabilities that could simplify the implementation of MLOps pipelines in real world solutions. Let’s review a few:

1) Ludwig on Ray

 
By far my favorite feature of this release was the integration with the Ray platform. Ray is one of the most complete stacks for highly scalable ML training and optimization processes. In Ludwig 0.4, data scientists can scale training workloads from a single laptop to a large Ray cluster using a few lines of configuration code.

2) Hyperparameter Search with Ray Tune

 
Ray Tune is a component of the Ray platform that allow distributed hyperparameter search in large clusters of nodes. Ludwig 0.4 integrates Ray Tune allowing distributed hyperparameter search algorithms such as Population-Based TrainingBayesian Optimization, and HyperBand among others.

3) Declarative Tabular Models with TabNet

 
TabNet is one of the top deep learning stacks for tabular data which incorporate cutting edge features such as attention architectures. The new release of Ludwig enables a declarative experience for tabular models by adding a new TabNet combiner which also includes tabular feature transformation and attention mechanisms to achieve state-of-the-art performance.

4) Experiment Tracking and Model Serving with MLflow

 
MLflow is rapidly becoming one of the most popular platfoms for ML experiment tracking and model serving. Ludwig 0.4 enables MLflwo-based experiment tracking with a single command line. Additionally, the new version of Ludwig can deploy a ML model to the MLflow registry using a simple command line statement.

 
Original. Reposted with permission.

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Source: https://www.kdnuggets.com/2021/06/ludwig-update-includes-low-code-machine-learning-capabilities.html

Big Data

If you did not already know

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Data Archaeology google


Data archaeology refers to the art and science of recovering computer data encoded and/or encrypted in now obsolete media or formats. Data archaeology can also refer to recovering information from damaged electronic formats after natural or man made disasters. …

UR-FUNNY google


Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research. …

Firebreak Decision Problem google


Suppose we have a network that is represented by a graph $G$. Potentially a fire (or other type of contagion) might erupt at some vertex of $G$. We are able to respond to this outbreak by establishing a firebreak at $k$ other vertices of $G$, so that the fire cannot pass through these fortified vertices. The question that now arises is which $k$ vertices will result in the greatest number of vertices being saved from the fire, assuming that the fire will spread to every vertex that is not fully behind the $k$ vertices of the firebreak. This is the essence of the Firebreak decision problem. …

Stochastic Substitute Training google


It has been shown that adversaries can craft example inputs to neural networks which are similar to legitimate inputs but have been created to purposely cause the neural network to misclassify the input. These adversarial examples are crafted, for example, by calculating gradients of a carefully defined loss function with respect to the input. As a countermeasure, some researchers have tried to design robust models by blocking or obfuscating gradients, even in white-box settings. Another line of research proposes introducing a separate detector to attempt to detect adversarial examples. This approach also makes use of gradient obfuscation techniques, for example, to prevent the adversary from trying to fool the detector. In this paper, we introduce stochastic substitute training, a gray-box approach that can craft adversarial examples for defenses which obfuscate gradients. For those defenses that have tried to make models more robust, with our technique, an adversary can craft adversarial examples with no knowledge of the defense. For defenses that attempt to detect the adversarial examples, with our technique, an adversary only needs very limited information about the defense to craft adversarial examples. We demonstrate our technique by applying it against two defenses which make models more robust and two defenses which detect adversarial examples. …

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://analytixon.com/2021/07/28/if-you-did-not-already-know-1460/

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Big Data

If you did not already know

Published

on

Data Archaeology google


Data archaeology refers to the art and science of recovering computer data encoded and/or encrypted in now obsolete media or formats. Data archaeology can also refer to recovering information from damaged electronic formats after natural or man made disasters. …

UR-FUNNY google


Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research. …

Firebreak Decision Problem google


Suppose we have a network that is represented by a graph $G$. Potentially a fire (or other type of contagion) might erupt at some vertex of $G$. We are able to respond to this outbreak by establishing a firebreak at $k$ other vertices of $G$, so that the fire cannot pass through these fortified vertices. The question that now arises is which $k$ vertices will result in the greatest number of vertices being saved from the fire, assuming that the fire will spread to every vertex that is not fully behind the $k$ vertices of the firebreak. This is the essence of the Firebreak decision problem. …

Stochastic Substitute Training google


It has been shown that adversaries can craft example inputs to neural networks which are similar to legitimate inputs but have been created to purposely cause the neural network to misclassify the input. These adversarial examples are crafted, for example, by calculating gradients of a carefully defined loss function with respect to the input. As a countermeasure, some researchers have tried to design robust models by blocking or obfuscating gradients, even in white-box settings. Another line of research proposes introducing a separate detector to attempt to detect adversarial examples. This approach also makes use of gradient obfuscation techniques, for example, to prevent the adversary from trying to fool the detector. In this paper, we introduce stochastic substitute training, a gray-box approach that can craft adversarial examples for defenses which obfuscate gradients. For those defenses that have tried to make models more robust, with our technique, an adversary can craft adversarial examples with no knowledge of the defense. For defenses that attempt to detect the adversarial examples, with our technique, an adversary only needs very limited information about the defense to craft adversarial examples. We demonstrate our technique by applying it against two defenses which make models more robust and two defenses which detect adversarial examples. …

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
Click here to access.

Source: https://analytixon.com/2021/07/28/if-you-did-not-already-know-1460/

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Big Data

Sony’s PS5 outstrips predecessor with 10 million units sold since Nov launch

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By Sam Nussey

TOKYO (Reuters) – Sony Group Corp said on Wednesday its PlayStation 5 (PS5) gaming console has sold more than 10 million units since launching last November, outstripping sales of its predecessor even as the Japanese firm grapples with a global chip shortage.

The PS5, which offers cutting edge graphics and faster loading times than the PS4, is in short supply as the COVID-19 pandemic strains global semiconductor supply chains while demand has risen amid a gaming boom with more people staying indoors.

“We’ve built more PlayStations faster than we ever have before which makes me happy. But on the other hand, we’re some time from being able to meet all the demand that’s out there, which makes me feel bad,” Sony Interactive Entertainment CEO Jim Ryan told Reuters via email.

“Our partners are performing really well for us, but the chip shortage is definitely a challenge that we are all navigating,” Ryan said.

Boosted by exclusive games likes Marvel’s Spider-Man: Miles Morales, which has sold more than 6.5 million copies, PS5 sales have outstripped the PS4.

It took Sony around nine months to sell 10 million units of the PS4, which had a staggered launch. More than 100 million units of the console have been sold since November 2013.

Electronics makers warn of deepening semiconductor shortages, with Apple on Tuesday saying the shortfall is affecting iPhone production.

“Sony’s deep expertise in supply chain management for consumer electronics has enabled it to weather the worst impacts of the pandemic even during the launch of a new product,” said Piers Harding-Rolls, head of games research at Ampere Analysis.

Sony sees demand for the PS5 continuing even as vaccinations spur easing of curbs on going out, Ryan said.

A strong games slate will be crucial to maintain momentum amid competition from Microsoft’s rival Xbox device, analysts say.

Another first-party title for Sony, Ratchet & Clank: Rift Apart, has sold more than 1.1 million copies since its release last month. First-party titles refer to games from companies that are owned by the firm making the console.

The group forecasts PS5 hardware sales of at least 14.8 million units in the year through March.

(Reporting by Sam Nussey; Editing by Himani Sarkar)

Image Credit: Reuters

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
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Source: https://datafloq.com/read/sonys-ps5-outstrips-predecessor-10-million-units-sold-since-nov-launch/16704

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Big Data

U.S. senators urge barring Huawei, ZTE from $1.9 trillion gov’t funding measure

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By David Shepardson

WASHINGTON (Reuters) -Two U.S. senators on Wednesday said they are introducing a measure to prohibit funds in a $1.9 trillion government funding measure from being used to purchase Chinese telecommunications equipment from Huawei, ZTE and other companies deemed U.S. security threats.

Senators Tom Cotton, a Republican, and Mark Warner, a Democrat, said the funds that were approved in March in a law known as the American Rescue Plan should not be used to potentially undermine U.S. telecommunications networks.

“With states across the country mapping out their plans for quality and affordable high-speed internet as a result of historic funding from the American Rescue Plan, we’ve got to make sure no community is sacrificing network security,” said Warner.

Huawei and ZTE did not immediately comment.

“The U.S government must take strong action to cut the Chinese Communist Party out of our networks. Americans deserve both reliable and secure telecommunications technologies,” said Cotton.

Earlier this month, the U.S. Federal Communications Commission (FCC) voted unanimously to finalize a $1.9 billion program to reimburse mostly rural U.S. carriers for removing equipment from telecommunications networks from Chinese companies like Huawei and ZTE.

Last year, the FCC designated Huawei and ZTE as national security threats to communications networks – a declaration that barred U.S. firms from tapping an $8.3 billion government fund to purchase equipment from the companies. The FCC in December adopted rules requiring carriers with ZTE or Huawei equipment to “rip and replace” that equipment.

The FCC in September 2020 estimated it would cost $1.837 billion to remove and replace Huawei and ZTE equipment from networks.

(Reporting by David Shepardson in WashingtonEditing by Jonathan Oatis and Matthew Lewis)

Image Credit: Reuters

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
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Source: https://datafloq.com/read/us-senators-urge-barring-huawei-zte-19-trillion-govt-funding-measure/16703

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