Towards AI

APRIL 12, 2024

This is the point in time where there are good results from the integration of IoT and data analytics when the strategy of the real-time supply chain becomes prominent. Data standardized data formats simplify the integration process so that they all function smoothly together regardless of the platform or system they are built on.

Analytics 69

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Snorkel AI

FEBRUARY 2, 2023

For complex or private data, this often requires collaborating with time-strapped subject matter experts, which can be so expensive as to make a project untenable. Streamlined tagging workflows. Improved tagging analysis. Auto-generated tag-based LFs. So, I’ll use our margin confusion matrix.

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Snorkel AI

FEBRUARY 2, 2023

For complex or private data, this often requires collaborating with time-strapped subject matter experts, which can be so expensive as to make a project untenable. Streamlined tagging workflows. Improved tagging analysis. Auto-generated tag-based LFs. So, I’ll use our margin confusion matrix.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Data Science Dojo

FEBRUARY 2, 2023

To start this project, select a dataset so that you can use pandas to read the data into a Data Frame and perform various operations on it. To build a chatbot using Python, you will need to use a combination of NLP and ML techniques. It also allows you to customize the appearance of the plots to match your needs.

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AWS Machine Learning Blog

MAY 5, 2023

Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets. And you want to do so quickly and with as little spend as possible. You want to make things as easy as possible for the end-user.

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The MLOps Blog

JANUARY 25, 2023

Then using Machine Learning and Deep Learning sentiment analysis techniques, these businesses analyze if a customer feels positive or negative about their product so that they can make appropriate business decisions to improve their business. BERT can comprehend the context of a given text, making it a good candidate for sentiment analysis.

Natural Language Processing 52

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Heartbeat

DECEMBER 7, 2023

It does so from a parsed PDF document using the llmsherpa.readers.layout_reader library. The researchers aim to identify and rank prompts that have a low likelihood of tie outcomes, where both completions are viewed as similarly good or bad by annotators. It then traverses the entire document tree, starting from the root node.

Deep Learning 52

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Snorkel AI

MARCH 14, 2023

The combination of Google Cloud services with Snorkel AI’s data-centric AI platform accelerates training data curation for ML development 10-100x [1] and empowers enterprises to solve some of their most critical challenges by accessing all of their knowledge and data to build AI systems.

AI 52

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Snorkel AI

MARCH 14, 2023

The combination of Google Cloud services with Snorkel AI’s data-centric AI platform accelerates training data curation for ML development 10-100x [1] and empowers enterprises to solve some of their most critical challenges by accessing all of their knowledge and data to build AI systems.

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Mlearning.ai

FEBRUARY 28, 2023

file from the scripts folder, hence so long as the inferrer in main.py Streamlit helps us capture the user’s input into a variable, and output the prediction to the front end web app, so we no longer need to use the boring terminal. Something so small can affect a customers experience negatively. file inside streamlit folder.

Data Science 52

Data Science Python ML ML 52

Heartbeat

MARCH 22, 2023

It has several text-processing libraries for tokenization, stemming, part-of-speech tagging, semantic reasoning, and many more tasks. We must convert the text data into lowercase so all words are in the same case. To start using NLTK, you need to install it. To do this, just run pip install nltk in your command line or !pip

Natural Language Processing 52

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Flipboard

NOVEMBER 21, 2023

We show you how to train a MATLAB machine learning model as a SageMaker training job and then deploy the model as a SageMaker real-time endpoint so it can process live, streaming data. Shun Mao is a Senior AI/ML Partner Solutions Architect in the Emerging Technologies team at Amazon Web Services.

Machine Learning 116

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Women in Big Data

DECEMBER 13, 2023

The probability intuitively tells us how “good” a sequence of tokens is. Sonal talked about applications of LLMs as in – Content generation, Part-of-speech (POS) tagging, Question answering, Text summarization, Sentiment analysis, Conversational AI, Machine translation and Code completion.

Natural Language Processing 52

Natural Language Processing Big Data Big Data Computer Science 52

Alation

OCTOBER 5, 2021

Yet, so many companies today are still failing miserably in implementing data strategy and governance protocols. So, why is YOUR data governance strategy failing? Today, all companies must pursue data analytics, Machine Learning & Artificial Intelligence (ML & AI) as an integral part of any standard business plan.

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AWS Machine Learning Blog

AUGUST 2, 2023

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they are looking for, even when it’s scattered across multiple locations and content repositories within your organization.

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AWS Machine Learning Blog

JANUARY 17, 2023

Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. Amazon SageMaker JumpStart provides one-click, end-to-end solutions for many common ML use cases. JumpStart solution templates.

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Heartbeat

FEBRUARY 8, 2023

It includes tokenization, part-of-speech tagging, and named entity recognition functions. The hotel is so good, I highly recommend it to everyone." Tokenization, stemming, part-of-speech tagging, n-grams, and text statistics are just a few of the text analysis tools available.

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The MLOps Blog

NOVEMBER 30, 2022

In this article, you will learn about: the challenges plaguing the ML space and why conventional tools are not the right answer to them. ML model versioning: where are we at? It is so because these algorithms have proven great results on a benchmark dataset, whereas your business problem and hence your data is different.

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Explosion

AUGUST 3, 2017

It’s easy to reuse the code but hard to reuse the data, so building AI mostly means doing annotation. This is good, because the examples are how you program the behaviour – the learner itself is really just a compiler. What’s not good is the current technology for creating the examples. The net result is a lot of wasted effort.

Supervised Learning 52

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The MLOps Blog

OCTOBER 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals.

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The MLOps Blog

MARCH 15, 2023

Data pipeline stages But before delving deeper into the technical aspects of these tools, let’s quickly understand the core components of a data pipeline succinctly captured in the image below: Data pipeline stages | Source: Author What does a good data pipeline look like?

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ODSC - Open Data Science

JANUARY 29, 2024

Text-to-image and text-to-video models are rapidly improving in accuracy and resolution so prompt engineering will be in demand to tune models that can create an expanding range of services from product design, advertising, user interfaces, and even advanced visualization. series (Davinci, etc), GPT-4, and GPT-4 Turbo are immensely popular.

Data Science 52

Data Science Machine Learning Machine Learning Natural Language Processing 52

The MLOps Blog

AUGUST 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Stefan: Good question.

ML 52

ML ML Data Scientist Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 16, 2023

Photo by Scott Webb on Unsplash Determining the value of housing is a classic example of using machine learning (ML). Almost 50 years later, the estimation of housing prices has become an important teaching tool for students and professionals interested in using data and ML in business decision-making.

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The MLOps Blog

JUNE 27, 2023

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. Model monitoring and performance tracking : Platforms should include capabilities to monitor and track the performance of deployed ML models in real-time.

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Flipboard

MARCH 7, 2023

Therefore, it’s critical to automatically discover insights from these transcripts, perform theme detection to analyze multiple customer conversations, and automatically present a set of themes that indicate the top reasons for customer contact, so that the customer problems are addressed in the right way and as soon as possible.

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Heartbeat

DECEMBER 21, 2023

Uncovering the Power of Comet Across the Data Science Journey Photo by Nguyen Le Viet Anh on Unsplash Machine learning (ML) projects are usually complicated and include several stages, from data discovery to model implementation. This article will dive into steps to see how you can use Comet at different stages of ML projects.

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Mlearning.ai

MAY 23, 2023

Underfitting: Here, the model is so simple that it is not able to identify the correct relationship in the data, and hence it does not perform well even on the test data. It is done to ensure the model is good enough by training the model on different patterns of a dataset to ensure variations are handled. When is resampling done?

Data Science 52

Data Science Decision Trees Deep Learning Deep Learning 52

The MLOps Blog

JANUARY 5, 2023

This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Kuba Cieślik, founder and AI Engineer at tuul.ai , about building visual search engines.

Machine Learning 52

Machine Learning Machine Learning Database ML 52

AWS Machine Learning Blog

MAY 13, 2024

It’s serverless, so you don’t have to manage any infrastructure. Evaluating LLMs is an undervalued part of the machine learning (ML) pipeline. For instance, “Generate radiology report impressions based on the following findings and output it within tags.” It is time-consuming but, at the same time, critical.

AI 106

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Snorkel AI

OCTOBER 12, 2023

Discover the strategies used to drive data-driven decisions within the complex governmental landscape and gain valuable perspectives on the future of AI/ML, the ethical considerations in data science, and the transformative potential of leveraging data to better society. Current specialized ML libraries (e.g.,

AI 64

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Snorkel AI

OCTOBER 12, 2023

Discover the strategies used to drive data-driven decisions within the complex governmental landscape and gain valuable perspectives on the future of AI/ML, the ethical considerations in data science, and the transformative potential of leveraging data to better society. Current specialized ML libraries (e.g.,

AI 59

AI AI ML ML 59

ML @ CMU

MARCH 22, 2024

TL;DR : Off-the-shelf text spotting and re-identification models fail in basic off-road racing settings, even more so during muddy events. In the dynamic world of sports analytics, machine learning (ML) systems play a pivotal role, transforming vast arrays of visual data into actionable insights.

Machine Learning 265

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ML @ CMU

MARCH 25, 2024

TL;DR : Off-the-shelf text spotting and re-identification models fail in basic off-road racing settings, even more so during muddy events. In the dynamic world of sports analytics, machine learning (ML) systems play a pivotal role, transforming vast arrays of visual data into actionable insights.

Machine Learning 246

Machine Learning Machine Learning ML ML 246

Heartbeat

DECEMBER 27, 2023

A good example is a metaverse, where aspects of the physical world are mimicked in a simulated digital environment. So, one OS instance can run several isolated containers. So rather than the VM using numerous Guest OS, this Docker Desktop install on a single VM (micro Linux VM) runs all containers and executes Docker commands.

Machine Learning 52

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AWS Machine Learning Blog

MAY 30, 2023

Since its introduction, we’ve helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their ML workloads’ cost and usage. It allows you to filter and group by values such as AWS service, usage type, cost allocation tags, EC2 instance type, and more.

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AWS Machine Learning Blog

MARCH 15, 2024

Always put your response to the Human within the Response tags. Also add an XML tag to your output identifying the human intent.nHere are some examples:n H: hi there.nnA: Hi, how can I help you today?nnH: n nI am so sorry, but Frappuccino is not in our menu, do you want a frappe or a cappuccino instead? perhaps something else?

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