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Create Stunning App with Machine Learning Model using Coda

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Coda released what they called the "Pack Studio". With Pack Studio, we can build a Pack (something like a library in Python) in minutes in our browser, no download is required, and minimal JavaScript knowledge is needed. It means that as a user, we can write JavaScript code to fit our needs. Say differently; we now can do everything like pulling, exploring, transforming data directly in the doc. There is no limit except our imagination.


Deploy machine learning models on Google Cloud AI Platform

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For that, you need frameworks and tooling, software and hardware that help you effectively deploy ML models. These can be frameworks like Tensorflow, Pytorch, and Scikit-Learn for training models, programming languages like Python, Java, and Go, and even cloud environments like AWS, GCP, and Azure. My Course is meant for anyone who already knows how to build both machine and deep learning models that is interested in deploying them easily on Google Cloud AI Platform. So that they can send the deployed models post requests. Also you must be familiar with Natural Language Processing and some basic cloud concepts.


Creating and using a machine learning model with AWS Sagemaker

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Luckily, the good folks over at A Cloud Guru have a #CloudGuruChallenge for Machine Learning. I mis-read the challenge goals at first as you will see later, but then I confirmed that the submission is okay for the challenge. The goal I set for myself is to focus more on using existing created model in an application, as most tutorials out there usually end at testing and calculating the accuracy of the trained model. I wanted to do something with a slightly, more local context, without just following tutorials. The Government hosts an online data store at Data.gov.sg,


Can a Machine Learning model tame your cloud costs?

ZDNet

For the first time in a couple of years, we'll be hopping a plane to hit AWS re:Invent right after we've digested our Thanksgiving turkey. There are plenty of third-party services that promise to babysit your cloud footprints to keep your monthly bills in check. But each year, when we hit the expo floor in Vegas, we've wondered when somebody would come up with a solution for training a machine learning model on the job to perform the job more systematically. There's one firm preannouncing before all the ruckus to announce just that. CAST AI is a two-year old startup making the types of bold claims that service providers typically offer; in this case, it claims that it can cut your cloud compute bills in half. In a previous life, the cofounders headed Zenedge, a cloud-based cybersecurity firm eventually acquired by Oracle.


Machine learning models focus on designing perfect data

#artificialintelligence

In a wide range of industries, companies are deploying AI initiatives for a variety of purposes. Predictive analytics, pattern recognition systems, autonomous systems, conversational systems, hyper-personalization activities, and goal-driven systems are just a few examples of these applications. Each of these projects has one thing in common: they all require a grasp of the business challenge and the application of data and machine learning algorithms to the problem, resulting in a machine learning model that meets the project's requirements. Machine learning initiatives are often deployed and managed in a similar manner. Existing app development approaches, on the other hand, are inapplicable because AI initiatives are driven by data rather than programming code.


Amazon vets land $10M for WhyLabs, a Seattle startup that monitors machine learning models - News Nation USA

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The news: WhyLabs, a spinout from the Seattle's Allen Institute for Artificial Intelligence (AI2), raised $10 million and released a new tool to support machine learning applications. The problem: As more companies leverage machine learning and artificial intelligence, the need to capture and correct failures is becoming more urgent. Reliance on algorithms can lead to negative implications, as evidenced this week by Zillow Group, for example. "The challenges begin once the machine learning system is live -- it automates millions of decisions a day," a WhyLabs spokesperson told GeekWire in an email. "Monitoring how well it's working becomes critical, because machine learning systems fail in often catastrophic ways."


Considerations for Deploying Machine Learning Models in Production

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A common grumble among data science or machine learning researchers or practitioners is that putting a model in production is difficult. As a result, some claim that a large percentage, 87%, of models never see the light of the day in production. "I have a model, I spent considerable time developing it on my laptop. How do I get it into our production environment? What should I consider for my ML stack and tooling?"


Considerations for Deploying Machine Learning Models in Production

#artificialintelligence

A common grumble among data science or machine learning researchers or practitioners is that putting a model in production is difficult. As a result, some claim that a large percentage, 87%, of models never see the light of the day in production. "I have a model, I spent considerable time developing it on my laptop. How do I get it into our production environment? What should I consider for my ML stack and tooling?"


Few-Shot Machine Learning Explained: Examples, Applications, Research

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Data is what powers machine learning solutions. Quality datasets enable training models with the needed detection and classification accuracy, though sometimes the accumulation of sufficient and applicable training data that should be fed into the model is a complex challenge. For instance, to create data-intensive apps human annotators are required to label a huge number of samples, which results in complexity of management and high costs for businesses. In addition to that, there is the difficulty associated with data acquisition related to safety regulations, privacy, or ethical concerns. When we have a limited dataset including only a finite number of samples per class, few-shot learning may be useful.


Right Ways to Follow For the Creation of Machine Learning Model from Scratch

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In this blog, we are going to study the in-depth process that should be followed for the development of a successful machine learning model from scratch. We all are familiar with machine learning technology and the fact of how it is prominently being used in the present scenario. The machine learning and artificial intelligence have completely taken over the corporate lead. The students who are studying this can take machine learning assignment help from the experts of BookMyEssay. Define Appropriate Problem: The first and most crucial thing to do is to define a problem.