Articles

Deploying machine learning workflows on LKE with Kubeflow

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Introduction

Teams that work with Machine Learning (ML) workloads in production know that added complexity can bring projects for a grinding halt. While deploying simple ML workloads might seem like an easy task, the process becomes a lot more involved when you begin to scale and distribute these loads and implement tools like Kubernetes. Although Kubernetes allows teams to rapidly scale their organization’s infrastructure, it also adds a layer of complexity that can become a major burden without the right tools. 

Today I’m going to introduce you to an OSS project known as Kubeflow that seeks to assist engineering teams with deploying ML workloads into production in Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable.

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7 Automatic Machine Learning Frameworks

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The usage of ML is increasingly rising these years. Businesses are impresses with a range of opportunities ML enables for them. However, they’re still struggling to deploy ML models because of the long duration and complexity of the process. 

When a business has to come up with the prediction of a particular data set, the traditional approach includes the performance of the following actions:

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Why Most Chatbots are Annoying and How to Make Sure Yours Isn’t

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As conversational language interfaces begin to dominate customer service, so does the backlash against chatbots grow. Forrester predicted last year that 2019 would be the year of the backlash against inefficient chatbots, and it looks like they were right. For example, a survey commissioned by an open software service company Acquia, that analyzed responses from more than 5,000 consumers and 500 marketers in North America, Europe and Australia, found that 45 percent of consumers find chatbots “annoying.”

At the same time, the importance of conversational AI for business today cannot be overestimated. When done right, conversational AI has the ability to significantly increase your competitive advantage and fundamentally change the nature of business-customer interaction.

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Google’s New ML-Powered Voice Recording App

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Google is heavily investing its resources in AI and machine learning research intending to shell out products and services for the future. So whether it has to do with computational photography or email suggestion features, Google has always been active on this front. Recently, Google also launched the famed “Google Recorder”. You might wonder that there are several voice recorder apps in the market so why this? But we all know it, if it is from Google it has to be a contender for the top slot! 

Before we explore further, let us see whether Google reads the race or not! And, yes we see right there that Google has done a great job when it comes to AI-based research and launches. 

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Accelerating ML Inference on Raspberry Pi With PyArmNN

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Overview 

A neural network, trained to recognize images that include a fire or flames, can make fire-detection systems more reliable and cost-effective. This tutorial shows how to use the newly released Python APIs for Arm NN inference engine to classify images as “Fire” versus “Non-Fire.”

What Is Arm NN and PyArmNN? 

Arm NN is an inference engine for CPUs, GPUs, and NPUs. It executes ML models on-device in order to make predictions based on input data. Arm NN enables efficient translation of existing neural network frameworks, such as TensorFlow Lite, TensorFlow, ONNX, and Caffe, allowing them to run efficiently and without modification across Arm Cortex-A CPUs, Arm Mali GPUs, and Arm Ethos NPUs.

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Parallel Grid Search in H2O

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H2O is, at its core, a platform for distributed, in-memory computing. On top of the distributed computation platform, machine learning algorithms are implemented. At H2O, we design every operation, be it data transformation, training of machine learning models, or even parsing to utilize the distributed computation model. In order to work with big data fast, it’s necessary.

However, a single operation usually can not utilize clusters’ computational resources to the very maximum. Data needs to be distributed across the cluster, and many operations require sequential execution of tasks, which, even if implemented in a distributed manner, follow after each other and require data exchange. These and many other smaller factors, if summed up together, may introduce a significant overhead.

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Build Your First Python Chatbot Project

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Introduction

Chatbots are extremely helpful for business organizations and also the customers. The majority of people prefer to talk directly from a chatbox instead of calling service centers. Facebook released data that proved the value of bots. More than 2 billion messages are sent between people and companies monthly. The HubSpot research tells us that 71% of people want to get customer support from messaging apps. It is a quick way to get their problems solved so chatbots have a bright future in organizations.

Today we are going to build an exciting project on Chatbot. We will implement a chatbot from scratch that will be able to understand what the user is talking about and give an appropriate response.

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La Création De Valeur À Partir Des Données De L’Automatisation

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Selon l’ 2019 Gartner quatrième rapport annuel de Chief Data Officer (CDO) de l’Enquête, la mise en œuvre d’une base de données et d’analyse de la stratégie a été classé comme le troisième plus important facteur de succès quand il s’agit d’un CDO de l’organisation.

Quand il s’agit de données, nous sommes tous conscients des quatre « Vs » — variété, de la vitesse, de la véracité et de volume – pourtant, pour de nombreuses organisations, leur entreposage de données de l’infrastructure n’est plus équipés pour y faire face. En outre, la valeur, la cinquième « V », est encore plus insaisissable. Donc, en tenant compte de l’ampleur de données que de nombreuses entreprises modernes ont signifie que répondre à ces défis exige une nouvelle approche de l’automatisation en cours de la fondation.

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Apache Kafka as Digital Twin in Industrial IoT (IIoT)

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Learn more about the benefits of Digital Twin tech in IIoT and it’s relation to Apache Kafka!

This blog post discusses the benefits of a Digital Twin in Industrial IoT (IIoT) and its relation to Apache Kafka. Kafka is often used as a central event streaming platform to build a scalable and reliable digital twin for real-time streaming sensor data.

In November 2019, I attended the SPS Conference in Nuremberg. This is one of the most important events about Industrial IoT (IIoT). Vendors and attendees from all over the world fly in to make business and discuss new products. Hotel prices in this region go up from usually 80-100€ to over 300€ per night. Germany is still known for its excellent engineering and manufacturing industry. German companies drive a lot of innovation and standardization around the Internet of Things (IoT) and Industry 4.0.

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10 Steps to Execute an AI Transformation Project

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AI Transformation in Medical Diagnoses

Within every aspect of healthcare, time is considered the most valuable component. Even minutes of delay can result in the loss of life. Early diagnosis lies at the heart of healing patients, and timely execution of treatment is of primary importance. At an average, doctors spend 15 minutes with each patient, which when considered intently, is grossly insufficient in providing a comprehensive diagnosis of the illness. In an ideal situation, a diagnosis should be made after careful consideration of all relevant patient information, including similar cases and demographics.

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As the healthcare industry gradually moves toward an AI-driven world, things that were previously considered a hindrance or unlikely are now fairly simple tasks. Over the years, more than 90% of hospitals in the county have moved from paper-based systems to electronic processes. When it comes to medical diagnoses, patients’ records are of primary importance. Risks towards critical illnesses can be caught through predictive analysis, thereby saving lives and costs. Early diagnosis is no longer a distant hope, but an actuality that can be easily accomplished through advanced systems.

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