Pickl AI

OCTOBER 17, 2023

This blog discusses best practices, real-world use cases, security and privacy considerations, and how Data Scientists can use ChatGPT to their full potential. Machine Learning Models: How Data Scientists Use ChatGPT Data Scientists use ChatGPT as a powerful ally in the ever-evolving field of Data Science.

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Snorkel AI

JANUARY 26, 2024

To achieve the trust, quality, and reliability necessary for production applications, enterprise data science teams must develop proprietary data for use with specialized models. Data scientists can best improve LLM performance on specific tasks by feeding them the right data prepared in the right way.

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LLM Large Language Models Explainability Data Science 52

ODSC - Open Data Science

OCTOBER 20, 2023

The Tangent Information Modeler, Time Series Modeling Reinvented Philip Wauters | Customer Success Manager and Value Engineer | Tangent Works Existing techniques for modeling time series data face limitations in scalability, agility, explainability, and accuracy. LLMs in Data Analytics: Can They Match Human Precision?

NLP 52

NLP Data Science Machine Learning Data Analysis 52

Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps. Join thousands of data leaders on the AI newsletter.

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Machine Learning Software Development Data Drift AI 88

ODSC - Open Data Science

JUNE 19, 2023

With the advent of big data in the modern world, RTOS is becoming increasingly important. As software expert Tim Mangan explains, a purpose-built real-time OS is more suitable for apps that involve tons of data processing. The Big Data and RTOS connection IoT and embedded devices are among the biggest sources of big data.

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Snorkel AI

NOVEMBER 9, 2023

LLM distillation will become a much more common and important practice for data science teams in 2024, according to a poll of attendees at Snorkel AI’s 2023 Enterprise LLM Virtual Summit. As data science teams reorient around the enduring value of small, deployable models, they’re also learning how LLMs can accelerate data labeling.

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LLM Data Scientist Large Language Models Data Science 59

Snorkel AI

NOVEMBER 9, 2023

LLM distillation will become a much more common and important practice for data science teams in 2024, according to a poll of attendees at Snorkel AI’s 2023 Enterprise LLM Virtual Summit. As data science teams reorient around the enduring value of small, deployable models, they’re also learning how LLMs can accelerate data labeling.

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LLM Data Scientist Large Language Models Data Science 59

Heartbeat

OCTOBER 25, 2023

Bioinformatics: A Haven for Data Scientists and Machine Learning Engineers: Bioinformatics offers an unparalleled opportunity for data scientists and machine learning engineers to apply their expertise in solving complex biological problems.

Machine Learning 52

Machine Learning Data Scientist Convolutional Neural Networks Algorithm 52

Snorkel AI

OCTOBER 4, 2023

Third-party FMs are expensive to use at scale, and funneling consumer financial data through an open-access foundation model raises serious privacy concerns. Hosting a foundation model or building one from scratch is also no small feat; their massive sizes necessitate enormous computing and data science resources.

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Pragnakalp

APRIL 29, 2024

Q1: Which are the 2 high focuses of data science? A1: The two high focuses of data science are Velocity and Variety, which are characteristics of Big Data. Velocity refers to the increasing rate at which data is collected and obtained, while Variety refers to the different types and sources of data.

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Deep Learning Big Data LLM Data Science 59

Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

Data Scientist 52

Data Scientist Machine Learning Big Data Algorithm 52

Snorkel AI

OCTOBER 4, 2023

Third-party FMs are expensive to use at scale, and funneling consumer financial data through an open-access foundation model raises serious privacy concerns. Hosting a foundation model or building one from scratch is also no small feat; their massive sizes necessitate enormous computing and data science resources.

AI 59

AI AI Data Scientist Neural Network 59

Unite.AI

OCTOBER 2, 2023

To explain this limitation, it is important to understand that the chemistry of sensory-based products is largely focused on quality control, i.e., how much of this analyte is in that mixture? When it comes to data quality, we realized a valid training set could not be generated from existing commercial or crowd-sourced data.

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Pickl AI

APRIL 26, 2024

Top 50+ Interview Questions for Data Analysts Technical Questions SQL Queries What is SQL, and why is it necessary for data analysis? SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. Data Visualisation What are the fundamental principles of data visualisation?

Data Analysis 52

Data Analysis Machine Learning ETL Explainability 52

ODSC - Open Data Science

APRIL 26, 2023

As you can imagine, data science is a pretty loose term or big tent idea overall. Though just about every industry imaginable utilizes the skills of a data-focused professional, each has its own challenges, needs, and desired outcomes. What makes this job title unique is the “Swiss army knife” approach to data.

Machine Learning 52

Machine Learning Data Science Data Scientist Python 52

Snorkel AI

JULY 24, 2023

Suddenly, non-technical users witnessed the LLM-backed chatbot’s ability to regurgitate knowledge, explain jokes and write poems. When models are pretrained, data is the main means for customization and fine-tuning of the models,” Gartner® said. The data-centric philosophy goes well beyond the point of training a model.

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Large Language Models Artificial Intelligence Artificial Intelligence AI 52

Snorkel AI

JULY 24, 2023

Suddenly, non-technical users witnessed the LLM-backed chatbot’s ability to regurgitate knowledge, explain jokes and write poems. When models are pretrained, data is the main means for customization and fine-tuning of the models,” Gartner® said. The data-centric philosophy goes well beyond the point of training a model.

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Large Language Models Artificial Intelligence Artificial Intelligence AI 52

Defined.ai blog

AUGUST 10, 2023

For example, if your AI model were designed to predict future sales based on past data, the output would likely be a predictive score. This score represents the predicted sales, and its accuracy would depend on the data quality and the AI model’s efficiency. Maintaining data quality.

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TheSequence

OCTOBER 15, 2023

Techniques such as distillation, RAG, quantization, and, of course, data quality curation have been developed to empower smaller and more efficient models. The question arises: are the LLM scaling laws approaching their limits? Which school of thought is correct? However, that day is not today. 📡AI Radar Character.AI

LLM 52

LLM Large Language Models Software Development Data Quality 52

Towards AI

FEBRUARY 20, 2024

If you want an overview of the Machine Learning Process, it can be categorized into 3 wide buckets: Collection of Data: Collection of Relevant data is key for building a Machine learning model. It isn't easy to collect a good amount of quality data. You need to know two basic terminologies here, Features and Labels.

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Machine Learning ML Neural Network Algorithm 84

Snorkel AI

MARCH 14, 2023

Worst of all, when data drifts or business requirements inevitably change, the process restarts from scratch and experts have to spend their time relabeling massive amounts of data. Snorkel AI solves this bottleneck with Snorkel Flow, the data-centric AI platform. Save costs with predictive well maintenance.

Data Scientist 52

Data Scientist Data Drift AI AI 52

Snorkel AI

MARCH 14, 2023

Worst of all, when data drifts or business requirements inevitably change, the process restarts from scratch and experts have to spend their time relabeling massive amounts of data. Snorkel AI solves this bottleneck with Snorkel Flow, the data-centric AI platform. Save costs with predictive well maintenance.

Data Scientist 52

Data Scientist Data Drift AI AI 52

Pickl AI

JULY 19, 2023

In this article, we will delve into the concept of data hygiene, its best practices, and key features, while also exploring the benefits it offers to businesses. It involves validating, cleaning, and enriching data to ensure its accuracy, completeness, and relevance. Large datasets may require significant processing time.

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Explainability Data Quality Data Integration Automation 40

Snorkel AI

JUNE 9, 2023

Fireside Chat: Journey of Data: Transforming the Enterprise with Data-Centric Workflows In a lively back and forth, Alex talked with Nurtekin Savas, head of enterprise data science at Capital One , about broadening the scope of being “data-centric.”

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Fireside Chat: Journey of Data: Transforming the Enterprise with Data-Centric Workflows In a lively back and forth, Alex talked with Nurtekin Savas, head of enterprise data science at Capital One , about broadening the scope of being “data-centric.”

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Heartbeat

DECEMBER 7, 2023

What is Data Mesh? Data Mesh is a new data set that enables units or cross-functional teams to decentralize and manage their data domains while collaborating to maintain data quality and consistency across the organization — architecture and governance approach. We can call fabric texture or actual fabric.

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Metadata Data Platform Deep Learning Data Quality 52

Dlabs.ai

MARCH 30, 2022

Several breakthroughs enabled us to fix data quality issues within the dataset. That’s why we introduced full explainability, which benefited our development team as much as the client. At the same time, the business could explain the reasoning behind predictions. However, we felt we could still improve it.

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Artificial Intelligence Artificial Intelligence Explainability Machine Learning 52

Heartbeat

OCTOBER 30, 2023

Monitoring Model Performance and Data Quality You cannot consider model training and deployment a one-time process. Choosing the Right Training Data The relevance of the data is of utmost importance, as it should reflect the problem domain and encompass a diverse range of examples. After deployment, your work is not done.

Machine Learning 52

Machine Learning Data Drift ML Data Scientist 52

Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning 52

Machine Learning Data Scientist ML Data Science 52

Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning 52

Machine Learning Data Scientist ML Data Science 52

Heartbeat

JANUARY 9, 2024

Proper data management enables optimal LLM capacitive performance in language-centric AI applications. MLOps requires a specialized approach to handle the unique characteristics of textual data and manage large datasets for pre-training and fine-tuning.

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Heartbeat

SEPTEMBER 25, 2023

Data quality and interoperability are essential challenges that must be addressed to ensure accurate and reliable predictions. Data quality, access, and interoperability are important challenges that must be addressed to ensure accurate predictions and generalizability.

Machine Learning 52

Machine Learning Algorithm Data Scientist Automation 52

Heartbeat

AUGUST 21, 2023

Data Preprocessing : The data quality used to train a CNN is critical to its performance. It is critical to preprocess the data before it is fed into the network. Preprocessing steps can include normalization, resizing, and data augmentation.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Natural Language Processing Computer Vision 52

AWS Machine Learning Blog

AUGUST 4, 2023

In the data flow view, you can now see a new node added to the visual graph. For more information on how you can use SageMaker Data Wrangler to create Data Quality and Insights Reports, refer to Get Insights On Data and Data Quality. SageMaker Data Wrangler offers over 300 built-in transformations.

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ML Categorization AI AI 78

The MLOps Blog

JUNE 27, 2023

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale.

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Heartbeat

AUGUST 29, 2023

The following are some of the primary difficulties for deep learning in software development: Data Quality and Quantity Deep learning models need a lot of labeled and quality training data. To prevent biases and overfitting, it is also essential to ensure the data's diversity and representativeness.

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Software Development Deep Learning Neural Network Convolutional Neural Networks 52

Heartbeat

AUGUST 23, 2023

Data Quality and Bias NLP systems rely significantly on massive training data to understand patterns and generate accurate predictions. However, if training data is biased or of low quality, it might result in skewed results and exacerbate existing inequities.

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