ODSC - Open Data Science

MAY 15, 2023

A high amount of effort is spent organizing data and creating reliable metrics the business can use to make better decisions. This creates a daunting backlog of data quality improvements and, sometimes, a graveyard of unused dashboards that have not been updated in years. Let’s start with an example.

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Heartbeat

JUNE 12, 2023

If the test or validation data distribution has too much deviance from the training data distribution, then we must go for retraining since it is a sign of population drift. Model Interpretability and Explainability Model interpretability and explainability describe how a machine learning model arrives at its predictions or decisions.

Machine Learning 98

Machine Learning Data Drift Explainability Data Quality 98

ODSC - Open Data Science

NOVEMBER 20, 2024

Addressing the Challenges of Generative AI: Data Quality, Governance, and Compliance One of the major hurdles businesses face when adopting generative AI is data quality. Yves Mulkers stressed the need for clean, reliable data as a foundation for AI success.

Generative AI 40

Generative AI Prompt Engineer Prompt Engineering Data Quality 40

ODSC - Open Data Science

FEBRUARY 24, 2023

Data Quality Now that you’ve learned more about your data and cleaned it up, it’s time to ensure the quality of your data is up to par. With these data exploration tools, you can determine if your data is accurate, consistent, and reliable.

Data Analysis 126

Data Analysis Data Science Business Intelligence Python 126

Unite.AI

JUNE 24, 2024

At Astronomer, he spearheads the creation of Apache Airflow features specifically designed for ML and AI teams and oversees the internal data science team. Can you share some information about your journey in data science and AI, and how it has shaped your approach to leading engineering and analytics teams?

Large Language Models 130

Large Language Models Data Scientist Machine Learning Software Engineer 130

Pickl AI

MARCH 10, 2023

Data Science is the process in which collecting, analysing and interpreting large volumes of data helps solve complex business problems. A Data Scientist is responsible for analysing and interpreting the data, ensuring it provides valuable insights that help in decision-making.

Data Scientist 52

Data Scientist Data Science Data Integration Auto-classification 52

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.

LLM 64

LLM Explainability Large Language Models Data Science 64

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.

Data Scientist 52

Data Scientist Machine Learning ChatGPT Data Analysis 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

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.

Big Data 96

Big Data ETL Artificial Intelligence Artificial Intelligence 96

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.

Machine Learning 79

Machine Learning Software Development Data Drift DevOps 79

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.

LLM 59

LLM Data Scientist Data Science Large Language Models 59

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.

Software Development 52

Software Development Deep Learning Neural Network Convolutional Neural Networks 52

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.

LLM 52

LLM Explainability Large Language Models Data Scientist 52

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.

LLM 52

LLM Data Scientist Data Science Large Language Models 52

ODSC - Open Data Science

OCTOBER 16, 2024

A Focus on Explainability and Responsible AI: The session “ Causal Graphs: Applying PyWhy to Go Beyond Explainability ” underscores the growing importance of understanding the “why” behind LLM predictions. Increasingly powerful open-source LLMs are a trend that will continue to shape the AI landscape into 2025.

LLM 40

LLM Responsible AI Large Language Models Data Quality 40

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 Explainability 52

Pickl AI

NOVEMBER 6, 2023

If you’re an aspiring Data Science professional , Data Visualisation will be part of your job role in presenting the insights in a visually understandable format. However, if you’re a beginner in the field, you need to undertake a Data Visualisation course for a beginner.

Data Scientist 52

Data Scientist Data Science Data Analysis Python 52

AWS Machine Learning Blog

NOVEMBER 13, 2024

Unified model governance architecture ML governance enforces the ethical, legal, and efficient use of ML systems by addressing concerns like bias, transparency, explainability, and accountability. Prepare the data to build your model training pipeline. The Amazon DataZone project ID is captured in the Documentation section.

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Metadata ML Software Engineer Machine Learning 95

ODSC - Open Data Science

OCTOBER 25, 2024

While data quality and model performance are crucial, building human-centric AI requires collaboration across data, business, design, and legal teams to ensure AI tools genuinely improve productivity. The AI should explain its reasoning clearly so users can validate decisions. Invoice amount does not match PO by 5%”).

AI 52

AI AI AI Tools Automation 52

Pickl AI

OCTOBER 15, 2024

This blog aims to explain what Statistical Modeling is, highlight its key components, and explore its applications across various sectors. Statistical Modeling uses mathematical frameworks to represent real-world data and make predictions, analyse relationships, or test hypotheses. What is Statistical Modeling?

Explainability 52

Explainability Data Analysis Data Quality Categorization 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.

Data Scientist 64

Data Scientist AI AI Neural Network 64

Pickl AI

JULY 23, 2023

Innovation and New Opportunities By analyzing data, organizations can uncover new opportunities for innovation and growth. Types of Analytics Descriptive Analytics with Example Explained Descriptive Analytics summarises and interprets historical data. It helps in gaining insights into past performance.

Data Analysis 52

Data Analysis Explainability Algorithm Machine Learning 52

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

DataRobot Blog

JULY 21, 2022

Evaluate the computing resources and development environment that the data science team will need. Large projects or those involving text, images, or streaming data may need specialized infrastructure. Data aggregation such as from hourly to daily or from daily to weekly time steps may also be required.

Machine Learning 98

Machine Learning Data Drift Categorization Data Scientist 98

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.

Data Scientist 59

Data Scientist AI AI Neural Network 59

Pickl AI

JULY 8, 2024

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

ETL 52

ETL Data Quality Data Integration Automation 52

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.

Machine Learning 278

Machine Learning Data Quality Explainability Data Science 278

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

Flipboard

MARCH 22, 2023

In this post, we show how to configure a new OAuth-based authentication feature for using Snowflake in Amazon SageMaker Data Wrangler. Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. Data Wrangler creates the report from the sampled data.

IDP 123

IDP Data Scientist Categorization Data Quality 123

Snorkel AI

JANUARY 31, 2023

Snorkel AI provides a data-centric AI development platform for AI teams to unlock production-grade model quality and accelerate time-to-value for their investments. Seldon is a deployment solution that helps teams serve, monitor, explain, and manage their ML models in production.

Data Drift 52

Data Drift Explainability Data Scientist AI 52

Snorkel AI

JANUARY 31, 2023

Snorkel AI provides a data-centric AI development platform for AI teams to unlock production-grade model quality and accelerate time-to-value for their investments. Seldon is a deployment solution that helps teams serve, monitor, explain, and manage their ML models in production.

Data Drift 52

Data Drift Explainability Data Scientist 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.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Large Language Models 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.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Large Language Models 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.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Large Language Models AI 52

AWS Machine Learning Blog

AUGUST 29, 2023

This architecture design represents a multi-account strategy where ML models are built, trained, and registered in a central model registry within a data science development account (which has more controls than a typical application development account). The following figure depicts a successful run of the training pipeline.

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Data Scientist Data Quality Python ML 77

DataRobot Blog

FEBRUARY 11, 2022

All models built within DataRobot MLOps support ethical AI through configurable bias monitoring and are fully explainable and transparent. The in-built, data quality assessments and visualization tools result in equitable, fair models that minimize the potential for harm, along with world-class data drift, service help, and accuracy tracking.

Data Drift 52

Data Drift DevOps Machine Learning Data Scientist 52