Search results for consistency

Found 205 documents, 5928 searched:

• Context, Consistency, And Collaboration Are Essential For Data Science Success

  It’s crucial to investigate the reasons why data science teams require context, consistency, and secure collaboration of their data to ensure data science success. Let's quickly examine each of these requirements so that we can better understand what data science success moving forward may look like.

  https://www.kdnuggets.com/2022/01/context-consistency-collaboration-essential-data-science-success.html

• Amazing consistency: Largest Dataset Analyzed / Data Mined – Poll Results and Trends

  The poll results show amazing consistency to past years, with median answers still in 10-100 gigabytes range. Really Big Data Scientists (100 Petabytes and more) continue to stand apart, but remain small segment where Asian data scientists lead for the first time in this poll.

  https://www.kdnuggets.com/2018/10/poll-results-largest-dataset-analyzed.html

• Retrieval Augmented Generation: Where Information Retrieval Meets Text Generation

  This article introduces retrieval augmented generation, which combines text generation with informaton retrieval in order to improve language model output.

  https://www.kdnuggets.com/retrieval-augmented-generation-where-information-retrieval-meets-text-generation

• Introducing MetaGPT’s Data Interpreter: SOTA Open Source LLM-based Data Solutions

  MetaGPT's newest agent addition makes running data interpretation and analysis tasks a breeze. Find out more and give it a try for yourself.

  https://www.kdnuggets.com/metagpt-data-interpreter-open-source-llm-based-data-solutions

• Streamline Your Machine Learning Workflow with Scikit-learn Pipelines

  Learn how to enhance the quality of your machine learning code using Scikit-learn Pipeline and ColumnTransformer.

  https://www.kdnuggets.com/streamline-your-machine-learning-workflow-with-scikit-learn-pipelines

• Data Maturity: The Cornerstone of AI-Enabled Innovation

  This article outlines strategies for overcoming data maturity challenges and accelerating AI adoption.

  https://www.kdnuggets.com/data-maturity-the-cornerstone-of-ai-enabled-innovation

• A Data Lake, You Call It? It’s a Data Swamp

  How and why the data lake architecture often fails to meet its promises. And how better governance helps mitigate such challenges.

  https://www.kdnuggets.com/a-data-lake-you-call-it-it-a-data-swamp

• 6 Reasons Why a Universal Semantic Layer is Beneficial to Your Data Stack

  Looking to understand the universal semantic layer and how it can improve your data stack? This GigaOm Sonor report on Semantic Layers can help you delve deeper.

  https://www.kdnuggets.com/2024/01/cube-6-reasons-why-a-universal-semantic-layer-is-beneficial

• Data Cleaning in SQL: How To Prepare Messy Data for Analysis

  Want to clean your messy data so you can start analyzing it with SQL? Learn how to handle missing values, duplicate records, outliers, and much more.

  https://www.kdnuggets.com/data-cleaning-in-sql-how-to-prepare-messy-data-for-analysis

• AI-Automated Cybersecurity: What to Automate?

  Soon AI will become embedded into daily business processes, including cybersecurity controls. The author explains how to assess which processes make sense to automate.

  https://www.kdnuggets.com/ai-automated-cybersecurity-what-to-automate

• KDnuggets News, November 22: 7 Essential Data Quality Checks with Pandas • The 5 Best Vector Databases You Must Try in 2024

  This week on KDnuggets: Learn how to perform data quality checks using pandas, from detecting missing records to outliers, inconsistent data entry and more • The top vector databases are known for their versatility, performance, scalability, consistency, and efficient algorithms in storing, indexing, and querying vector embeddings for AI applications • And much, much more!

  https://www.kdnuggets.com/newsletter-n42-2023-11-22

• How to Make Large Language Models Play Nice with Your Software Using LangChain

  Beyond simply chatting with an AI model and how LangChain elevates LLM interactions with humans.

  https://www.kdnuggets.com/how-to-make-large-language-models-play-nice-with-your-software-using-langchain

• The 5 Best Vector Databases You Must Try in 2024

  The top vector databases are known for their versatility, performance, scalability, consistency, and efficient algorithms in storing, indexing, and querying vector embeddings for AI applications.

  https://www.kdnuggets.com/the-5-best-vector-databases-you-must-try-in-2024

• Between Dreams and Reality: Generative Text and Hallucinations

  This is an in-depth dive into hallucinations in LLMs. See the illusions cast by modern AI generative models like ChatGPT, Bard and Claude.

  https://www.kdnuggets.com/between-dreams-and-reality-generative-text-and-hallucinations

• Data Warehouses vs. Data Lakes vs. Data Marts: Need Help Deciding?

  A comparative overview of data warehouses, data lakes, and data marts to help you make informed decisions on data storage solutions for your data architecture.

  https://www.kdnuggets.com/data-warehouses-vs-data-lakes-vs-data-marts-need-help-deciding

• 7 Steps to Mastering Data Wrangling with Pandas and Python

  Starting out on your data journey? Here’s a 7-step learning path to master data wrangling with pandas.

  https://www.kdnuggets.com/7-steps-to-mastering-data-wrangling-with-pandas-and-python

• Some Kick Ass Prompt Engineering Techniques to Boost our LLM Models

  And how to go beyond its basics.

  https://www.kdnuggets.com/some-kick-ass-prompt-engineering-techniques-to-boost-our-llm-models

• RAG vs Finetuning: Which Is the Best Tool to Boost Your LLM Application?

  The definitive guide for choosing the right method for your use case.

  https://www.kdnuggets.com/rag-vs-finetuning-which-is-the-best-tool-to-boost-your-llm-application

• Revamping Data Visualization: Mastering Time-Based Resampling in Pandas

  Unlock the power of time-based data visualization with Pandas as we delve into the art of resampling, turning your data into insightful temporal masterpieces.

  https://www.kdnuggets.com/revamping-data-visualization-mastering-timebased-resampling-in-pandas

• The Quest for Model Confidence: Can You Trust a Black Box?

  This article explores strategies for evaluating the reliability of labels generated by Large Language Models (LLMs). It discusses the effectiveness of different approaches and offers practical insights for various applications.

  https://www.kdnuggets.com/the-quest-for-model-confidence-can-you-trust-a-black-box

• The Data Maturity Pyramid: From Reporting to a Proactive Intelligent Data Platform

  This article describes the data maturity pyramid and its various levels, from simple reporting to AI-ready data platforms. It emphasizes the importance of data for business and illustrates how data platforms serve as the driving force behind AI.

  https://www.kdnuggets.com/the-data-maturity-pyramid-from-reporting-to-a-proactive-intelligent-data-platform

• Optimizing Data Storage: Exploring Data Types and Normalization in SQL

  Learn about the data types and normalization techniques in SQL, which will be very helpful for optimizing your data storage.

  https://www.kdnuggets.com/optimizing-data-storage-exploring-data-types-and-normalization-in-sql

• How to Identify Missing Data in Time-Series Datasets

  Using exploratory data analysis to wnderstand missing data gaps.

  https://www.kdnuggets.com/how-to-identify-missing-data-in-timeseries-datasets

• Getting Started with SQL in 5 Steps

  This comprehensive SQL tutorial covers everything from setting up your SQL environment to mastering advanced concepts like joins, subqueries, and optimizing query performance. With step-by-step examples, this guide is perfect for beginners looking to enhance their data management skills.

  https://www.kdnuggets.com/5-steps-getting-started-with-sql

• Introduction to Databases in Data Science

  Understand the relevance of databases in data science. Also learn the fundamentals of relational databases, NoSQL database categories, and more.

  https://www.kdnuggets.com/introduction-to-databases-in-data-science

• KDnuggets News, September 6: Happy 30th Anniversary KDnuggets! • Getting Started with Python Data Structures in 5 Steps

  Happy 30th Anniversary KDnuggets! • Getting Started with Python Data Structures in 5 Steps • KDnuggets 30th Anniversary Interview with Founder Gregory Piatetsky-Shapiro

  https://www.kdnuggets.com/2023/n32.html

• Data Visualization: Theory and Techniques

  Unlocking the secrets of how to observe our data-driven world.

  https://www.kdnuggets.com/data-visualization-theory-and-techniques

• 5 Crucial Steps to Develop an Effective Coding Routine

  Struggling to develop your coding routine? Well, I have some psychological insights to share that can boost your motivation and make a real difference in your coding journey.

  https://www.kdnuggets.com/2023/08/5-crucial-steps-develop-effective-coding-routine.html

• Data Validation for PySpark Applications using Pandera

  New features and concepts.

  https://www.kdnuggets.com/2023/08/data-validation-pyspark-applications-pandera.html

• Creating A Simple Docker Data Science Image

  This concise primer walks through setting up a Python data science environment using Docker, covering creating a Dockerfile, building an image, running a container, sharing and deploying images, and pushing to Docker Hub.

  https://www.kdnuggets.com/2023/08/simple-docker-data-science-image.html

• Things You Should Know When Scaling Your Web Data-Driven Product

  Scaling your data-driven product helps grow your business, but it requires certain expertise. In this article, you will learn how scaling works and what to keep in mind while doing it.

  https://www.kdnuggets.com/2023/08/things-know-scaling-web-datadriven-product.html

• OLAP vs. OLTP: A Comparative Analysis of Data Processing Systems

  A comprehensive comparison between OLAP and OLTP systems, exploring their features, data models, performance needs, and use cases in data engineering.

  https://www.kdnuggets.com/2023/08/olap-oltp-comparative-analysis-data-processing-systems.html

• Text-2-Video Generation: Step-by-Step Guide

  Bringing Words to Life: Easy Techniques to Generate Stunning Videos from Text Using Python.

  https://www.kdnuggets.com/2023/08/text2video-generation-stepbystep-guide.html

• LangChain + Streamlit + Llama: Bringing Conversational AI to Your Local Machine

  Integrating Open Source LLMs and LangChain for Free Generative Question Answering (No API Key required).

  https://www.kdnuggets.com/2023/08/langchain-streamlit-llama-bringing-conversational-ai-local-machine.html

• 5 Things You Need to Know When Building LLM Applications

  Five problems come with building LLM-based applications.

  https://www.kdnuggets.com/2023/08/5-things-need-know-building-llm-applications.html

• A Comprehensive Guide to MLOps

  Machine Learning Operations (MLOps) is a relatively new discipline that provides the structure and support necessary for machine learning (ML) models to thrive in production environments.

  https://www.kdnuggets.com/2023/08/comprehensive-guide-mlops.html

• Fundamentals Of Statistics For Data Scientists and Analysts

  Key statistical concepts for your data science or data analysis journey.

  https://www.kdnuggets.com/2023/08/fundamentals-statistics-data-scientists-analysts.html

• The Importance of Data Cleaning in Data Science

  This article provides an overview of the importance of data cleaning in data science. It explains what data cleaning is, the benefits of using it, and the commonly used tools.

  https://www.kdnuggets.com/2023/08/importance-data-cleaning-data-science.html

• An MLOps Mindset: Always Production-Ready

  A lack of an ML production mindset from the beginning of a project can lead to surprises later on, especially during production time, resulting in re-modeling and delayed time-to-market.

  https://www.kdnuggets.com/2023/07/mlops-mindset-always-productionready.html

• Unlocking the Power of Numbers in Health Economics and Outcomes Research

  Learn about the quantitative challenges that are present in HEOR research and how statistics can be used to address these issues.

  https://www.kdnuggets.com/2023/07/unlocking-power-numbers-health-economics-outcomes-research.html

• Exploring the Power and Limitations of GPT-4

  Unveiling GPT-4: Deciphering its impact on data science and exploring its strengths and boundaries.

  https://www.kdnuggets.com/2023/07/exploring-power-limitations-gpt4.html

• Unveiling Midjourney 5.2: A Leap Forward in AI Image Generation

  Discover the latest advancements in AI image generation with Midjourney 5.2. This article provides an in-depth look at the new features and improvements, including the innovative 'Zoom Out' feature, 'Make Square' tool, and enhanced 'Stylize' command. Learn how these features are revolutionizing the field of AI artistry.

  https://www.kdnuggets.com/2023/06/unveiling-midjourney-52-leap-forward.html

• A Practical Guide to Transfer Learning using PyTorch

  In this article, we’ll learn to adapt pre-trained models to custom classification tasks using a technique called transfer learning. We will demonstrate it for an image classification task using PyTorch, and compare transfer learning on 3 pre-trained models, Vgg16, ResNet50, and ResNet152.

  https://www.kdnuggets.com/2023/06/practical-guide-transfer-learning-pytorch.html

• GPT4All is the Local ChatGPT for your Documents and it is Free!

  How to install GPT4All on your Laptop and ask AI about your own domain knowledge (your documents)… and it runs on CPU only!.

  https://www.kdnuggets.com/2023/06/gpt4all-local-chatgpt-documents-free.html

• The Art of Prompt Engineering: Decoding ChatGPT

  Mastering the principles and practices of AI interaction with OpenAI and DeepLearning.AI’s course.

  https://www.kdnuggets.com/2023/06/art-prompt-engineering-decoding-chatgpt.html

• Deep Learning with R

  In this tutorial, learn how to perform a deep learning task in R.

  https://www.kdnuggets.com/2023/05/deep-learning-r.html

• Data Masking: The Core of Ensuring GDPR and other Regulatory Compliance Strategies

  This article has provided an overview of data masking and its importance in ensuring compliance with GDPR and other global regulations.

  https://www.kdnuggets.com/2023/05/data-masking-core-ensuring-gdpr-regulatory-compliance-strategies.html

• Schedule & Run ETLs with Jupysql and GitHub Actions

  This blog provided you with a comprehensive overview of ETL and JupySQL, including a brief introduction to ETLs and JupySQL. We also demonstrated how to schedule an example ETL notebook via GitHub actions, which allows you to automate the process of executing ETLs and JupySQL from Jupyter.

  https://www.kdnuggets.com/2023/05/schedule-run-etls-jupysql-github-actions.html

• DataLang: A New Programming Language for Data Scientists… Created by ChatGPT?

  I recently tasked ChatGPT-4's to come up with a new programming language appropriate for data scientists in their day to day tasks. Let's look at the results, and the process of getting there.

  https://www.kdnuggets.com/2023/04/datalang-new-programming-language-data-scientists-chatgpt.html

• My Data Science Six Months Success Story

  I will be sharing a couple of things I have learned in the past six months and tips that helped me stay dedicated and true to my journey in this article.

  https://www.kdnuggets.com/2023/04/data-science-six-months-success-story.html

• ETL vs ELT: Which One is Right for Your Data Pipeline?

  Learn about the differences between ETL and ELT data integration techniques and determine which is right for your data pipeline.

  https://www.kdnuggets.com/2023/03/etl-elt-one-right-data-pipeline.html

• Data Quality Dimensions: Assuring Your Data Quality with Great Expectations

  This article highlights the significance of ensuring high-quality data and presents six key dimensions for measuring it. These dimensions include Completeness, Consistency, Integrity, Timelessness, Uniqueness, and Validity.

  https://www.kdnuggets.com/2023/03/data-quality-dimensions-assuring-data-quality-great-expectations.html

• Data Warehousing and ETL Best Practices

  How you can improve your data warehousing ETL process with these simple practices.

  https://www.kdnuggets.com/2023/02/data-warehousing-etl-best-practices.html

• Zero-shot Learning, Explained

  How you can train a model to learn and predict unseen data?

  https://www.kdnuggets.com/2022/12/zeroshot-learning-explained.html

• 10 Most Common Data Quality Issues and How to Fix Them

  Ensuring data quality guarantees more data-informed decisions. Hence, this article highlights the common data quality issues and ways to overcome them.

  https://www.kdnuggets.com/2022/11/10-common-data-quality-issues-fix.html

• Picking Examples to Understand Machine Learning Model

  Understanding ML by combining explainability and sample picking.

  https://www.kdnuggets.com/2022/11/picking-examples-understand-machine-learning-model.html

• The AI Education Gap and How to Close It

  AI education is broken, how do we solve it? Individuals end up learning a specific tool or tactic in a vacuum. They are missing the real-world applicability and collaboration that is critical to building impactful AI solutions in line with the organization’s strategy.

  https://www.kdnuggets.com/2022/11/ai-education-gap-close.html

• 7 Tips To Produce Readable Data Science Code

  In this article, we will go over a few steps that you can take to produce readable, high-quality code.

  https://www.kdnuggets.com/2022/11/7-tips-produce-readable-data-science-code.html

• Top 10 MLOps Tools to Optimize & Manage Machine Learning Lifecycle

  As more businesses experiment with data, they realize that developing a machine learning (ML) model is only one of many steps in the ML lifecycle.

  https://www.kdnuggets.com/2022/10/top-10-mlops-tools-optimize-manage-machine-learning-lifecycle.html

• Is OLAP Dead?

  OLAP enables citizen analysts to quickly, efficiently, and cost-effectively uncover new business insights at a reduced time-to-value.

  https://www.kdnuggets.com/2022/10/olap-dead.html

• 25 Advanced SQL Interview Questions for Data Scientists

  Check out this collection of advanced SQL interview questions with answers.

  https://www.kdnuggets.com/2022/10/25-advanced-sql-interview-questions-data-scientists.html

• 8 Ways to Improve Your Search Application this Week

  There are many places to start improving and optimizing and it’s easy to get bogged down. The good news is that there are several easy ways to improve your search application’s quality and performance.

  https://www.kdnuggets.com/2022/09/corise-8-ways-improve-search-application-week.html

• Data-centric AI and Tabular Data

  DALL-E, LaMDA, and GPT-3 all had celebrity moments recently. So, where’s the glamorous, high-performance model that’s mastered tabular data?

  https://www.kdnuggets.com/2022/09/datacentric-ai-tabular-data.html

• Why Organizations Need Data Warehouses

  So where can you store, harness and collect findings in your data - in one place? What is the right tool for this? Data Warehouses

  https://www.kdnuggets.com/2022/09/organizations-need-data-warehouses.html

• Everything You Need to Know About Data Lakehouses

  Learn everything you need to know about data lakehouses.

  https://www.kdnuggets.com/2022/09/everything-need-know-data-lakehouses.html

• SQL vs NoSQL: 7 Key Takeaways

  People assume that NoSQL is a counterpart to SQL. Instead, it’s a different type of database designed for use-cases where SQL is not ideal. The differences between the two are many, although some are so crucial that they define both databases at their cores.

  https://www.kdnuggets.com/2020/12/sql-vs-nosql-7-key-takeaways.html

• Machine Learning Metadata Store

  In this article, we will learn about metadata stores, the need for them, their components, and metadata store management.

  https://www.kdnuggets.com/2022/08/machine-learning-metadata-store.html

• Best Instagram Accounts to Follow for Data Science, Machine Learning & AI

  I have put this blog together to help you figure out what Instagram accounts you should follow to get the best Data Science, Machine Learning, and Artificial Intelligence content.

  https://www.kdnuggets.com/2022/08/best-instagram-accounts-follow-data-science-machine-learning-ai.html

• Trust in AI is Priceless

  Many machine learning models fail to deliver. Sadly, it’s often due to a lack of focus on data quality.

  https://www.kdnuggets.com/2022/08/trust-ai-priceless.html

• What is Text Classification?

  We will define text classification, how it works, some of its most known algorithms, and provide data sets that might help start your text classification journey.

  https://www.kdnuggets.com/2022/07/text-classification.html

• Detecting Data Drift for Ensuring Production ML Model Quality Using Eurybia

  This article will focus on a step-by-step data drift study using Eurybia an open-source python library

  https://www.kdnuggets.com/2022/07/detecting-data-drift-ensuring-production-ml-model-quality-eurybia.html

• Market Data and News: A Time Series Analysis

  In this article we introduce a few tools and techniques for studying relationships between the stock market and the news. We explore time series processing, anomaly detection, and an event-based view of the news. We also generate intuitive charts to demonstrate some of these concepts, and share the code behind all of this in a notebook.

  https://www.kdnuggets.com/2022/06/market-data-news-time-series-analysis.html

• Top 15 Books to Master Data Strategy

  In this article, we outline 15 books on topics ranging from the technical to the non-technical, to help you improve your understanding of end-to-end best practices related to data.

  https://www.kdnuggets.com/2022/06/top-15-books-master-data-strategy.html

• How Activation Functions Work in Deep Learning

  Check out a this article for a better understanding of activation functions.

  https://www.kdnuggets.com/2022/06/activation-functions-work-deep-learning.html

• Database Key Terms, Explained

  Interested in a survey of important database concepts and terminology? This post concisely defines 16 essential database key terms.

  https://www.kdnuggets.com/2016/07/database-key-terms-explained.html

• Data Management: How to Stay on Top of Your Customer’s Mind?

  Extract, profile, and manage your customer data in a flash with customer data management solutions, and achieve a customer-centric culture.

  https://www.kdnuggets.com/2022/04/data-management-stay-top-customer-mind.html

• Data Scientist, Data Engineer & Other Data Careers, Explained

  In this article, we will have a look at five distinct data careers, and hopefully provide some advice on how to get one's feet wet in this convoluted field.

  https://www.kdnuggets.com/2021/05/data-scientist-data-engineer-data-careers-explained.html

• How Artificial Intelligence Can Transform Data Integration

  Let's take a look at what goes into creating a foundation for enterprise-wide data intelligence and how AI and ML can permanently transform data integration.

  https://www.kdnuggets.com/2022/04/artificial-intelligence-transform-data-integration.html

• A Quick Guide to Find the Right Minds for Annotation

  Let's look through the points below for useful tips on how to choose the proper outsourcing partner to handle the labeling for your next AI model.

  https://www.kdnuggets.com/2022/04/quick-guide-find-right-minds-annotation.html

• Uncertainty Quantification in Artificial Intelligence-based Systems

  The article summarizes the plethora of UQ methods using Bayesian techniques, shows issues and gaps in the literature, suggests further directions, and epitomizes AI-based systems within the Financial Crime domain.

  https://www.kdnuggets.com/2022/04/uncertainty-quantification-artificial-intelligencebased-systems.html

• MLOps Is a Mess But That’s to be Expected

  In this post, I want to focus the discussion about the state of machine learning operations (MLOps) today, where we are, where we are going.

  https://www.kdnuggets.com/2022/03/mlops-mess-expected.html

• Risk Management Framework for AI/ML Models

  How sound risk management acts as a catalyst to building successful AI/ML models.

  https://www.kdnuggets.com/2022/03/risk-management-framework-aiml-models.html

• Data-Centric AI: Is it Real? For Everyone? Are We Ready?

  Check out this deep dive into Data-Centric AI.

  https://www.kdnuggets.com/2022/03/data-centric-ai-real-everyone-ready.html

• The Significance of Data Quality in Making a Successful Machine Learning Model

  Good quality data becomes imperative and a basic building block of an ML pipeline. The ML model can only be as good as its training data.

  https://www.kdnuggets.com/2022/03/significance-data-quality-making-successful-machine-learning-model.html

• How You Can Use Machine Learning to Automatically Label Data

  AI and machine learning can provide us with these tools. This guide will explore how we can use machine learning to label data.

  https://www.kdnuggets.com/2022/02/machine-learning-automatically-label-data.html

• Unstructured Data: The Must-Have For Analytics In 2022

  Let's investigate the current need that enterprise organizations have to rapidly parse through unstructured data and examine several data management trends that are highly relevant in 2022.

  https://www.kdnuggets.com/2022/01/unstructured-data-analytics-2022.html

• Getting Started Cleaning Data

  In order to achieve quality data, there is a process that needs to happen. That process is data cleaning. Learn more about the various stages of this process.

  https://www.kdnuggets.com/2022/01/getting-started-cleaning-data.html

• How to Answer Data Science Coding Interview Questions

  Use this checklist to make sure your answer to the data science coding interview questions is on the right track.

  https://www.kdnuggets.com/2022/01/answer-data-science-coding-interview-questions.html

• KDnuggets™ News 22:n03, Jan 19: A Deep Look Into 13 Data Scientist Roles and Their Responsibilities; Top Five SQL Window Functions You Should Know For Data Science Interviews

  A Deep Look Into 13 Data Scientist Roles and Their Responsibilities; Top Five SQL Window Functions You Should Know For Data Science Interviews; 5 Things to Keep in Mind Before Selecting Your Next Data Science Job; Models Are Rarely Deployed: An Industry-wide Failure in Machine Learning Leadership; Running Redis on Google Colab

  https://www.kdnuggets.com/2022/n03.html

• Data Quality: The Good, The Bad, and The Ugly

  Incorrect or unclean data leads to false conclusions. The time you take to understand and clean the data is vital to the outcome and quality of the results. Data Quality always takes the win against complex fancy algorithms.

  https://www.kdnuggets.com/2022/01/data-quality-good-bad-ugly.html

• Learn Deep Learning by Building 15 Neural Network Projects in 2022

  Here are 15 neural network projects you can take on in 2022 to build your skills, your know-how, and your portfolio.

  https://www.kdnuggets.com/2022/01/15-neural-network-projects-build-2022.html

• Software Mistakes and Tradeoffs: New book by Tomasz Lelek and StackOverflow guru Jon Skeet

  Flexibility versus maintainability—every decision you make in software engineering involves balancing tradeoffs. Software Mistakes and Tradeoffs is available in early access from its publisher Manning. Pre-order now and start reading immediately as part of the Manning Early Access Program (MEAP).

  https://www.kdnuggets.com/2021/12/manning-software-mistakes-tradeoffs-book.html

• Data Science & Analytics Industry Main Developments in 2021 and Key Trends for 2022

  We have solicited insights from experts at industry-leading companies, asking: "What were the main AI, Data Science, Machine Learning Developments in 2021 and what key trends do you expect in 2022?" Read their opinions here.

  https://www.kdnuggets.com/2021/12/developments-predictions-data-science-analytics-industry.html

• What Is AI Model Governance?

  How exactly does AI model governance help tackle these issues? And how can you ensure you’re using it to best fit your needs? Read on.

  https://www.kdnuggets.com/2021/12/ai-model-governance.html

• Introduction to Clustering in Python with PyCaret

  A step-by-step, beginner-friendly tutorial for unsupervised clustering tasks in Python using PyCaret.

  https://www.kdnuggets.com/2021/12/introduction-clustering-python-pycaret.html

• Avoid These Mistakes with Time Series Forecasting

  A few checks to make before training a Machine Learning model on data that could be random.

  https://www.kdnuggets.com/2021/12/avoid-mistakes-time-series-forecasting.html

• Most Common SQL Mistakes on Data Science Interviews">Most Common SQL Mistakes on Data Science Interviews

  Sure, we all make mistakes -- which can be a bit more painful when we are trying to get hired -- so check out these typical errors applicants make while answering SQL questions during data science interviews.

  https://www.kdnuggets.com/2021/11/common-sql-mistakes-data-science-interviews.html

• 5 Tips to Get Your First Data Scientist Job

  Read some of the key things the author has learned during the infamous job seeking stage.

  https://www.kdnuggets.com/2021/11/5-tips-first-data-scientist-job.html

• Two Simple Things You Need to Steal from Agile for Data and Analytics Work

  Peer Review and Definition of Done: small changes, BIG impact.

  https://www.kdnuggets.com/2021/11/simple-things-steal-agile-data-science-analytics.html

• Nine Tools I Wish I Mastered Before My PhD in Machine Learning">Nine Tools I Wish I Mastered Before My PhD in Machine Learning

  Whether you are building a start up or making scientific breakthroughs these tools will bring your ML pipeline to the next level.

  https://www.kdnuggets.com/2021/09/nine-tools-mastered-before-phd-machine-learning.html

• The Significance of Data-centric AI

  How a systematic way of maintaining data quality can do wonders to your model performance.

  https://www.kdnuggets.com/2021/08/significance-data-centric-ai.html

• 5 Tips for Writing Clean R Code

  This article summarizes the most common mistakes to avoid and outline best practices to follow in programming in general. Follow these tips to speed up the code review iteration process and be a rockstar developer in your reviewer’s eyes!

  https://www.kdnuggets.com/2021/08/5-tips-writing-clean-r-code.html

• The Rise of Vector Data

  Embedding models convert raw data such as text, images, audio, logs, and videos into vector embeddings (“vectors”) to be used for predictions, comparisons, and other cognitive-like functions.

  https://www.kdnuggets.com/2021/05/pinecone-rise-vector-data.html

• Awesome list of datasets in 100+ categories

  With an estimated 44 zettabytes of data in existence in our digital world today and approximately 2.5 quintillion bytes of new data generated daily, there is a lot of data out there you could tap into for your data science projects. It's pretty hard to curate through such a massive universe of data, but this collection is a great start. Here, you can find data from cancer genomes to UFO reports, as well as years of air quality data to 200,000 jokes. Dive into this ocean of data to explore as you learn how to apply data science techniques or leverage your expertise to discover something new.

  https://www.kdnuggets.com/2021/05/awesome-list-datasets.html

• Machine Translation in a Nutshell

  Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California for a snapshot of machine translation. Dr. Farzindar also provided the original art for this article.

  https://www.kdnuggets.com/2021/05/machine-translation-nutshell.html

• Feature stores – how to avoid feeling that every day is Groundhog Day

  Feature stores stop the duplication of each task in the ML lifecycle. You can reuse features and pipelines for different models, monitor models consistently, and sidestep data leakage with this MLOps technology that everyone is talking about.

  https://www.kdnuggets.com/2021/05/feature-stores-how-avoid-feeling-every-day-is-groundhog-day.html

• How to Build an Impressive Data Science Resume

  Every one of us needs a resume to showcase our skills and experience but how much effort are we putting into it to make it impactful. It is undeniable that resumes play a key role in our job application process. This article will explore some simple strategies to significantly improve the presentation as well as the content of data science resumes.

  https://www.kdnuggets.com/2021/04/build-impressive-data-science-resume.html

• Build an Effective Data Analytics Team and Project Ecosystem for Success

  Apply these techniques to create a data analytics program that delivers solutions that delight end-users and meet their needs.

  https://www.kdnuggets.com/2021/04/build-effective-data-analytics-team-project-ecosystem-success.html

• Document Databases, Explained

  Out of all the NoSQL database types, document-stores are considered the most sophisticated ones. They store data in a JSON format which as opposed to a classic rows and columns structure.

  https://www.kdnuggets.com/2021/03/understanding-nosql-database-types-document.html

• Speeding up Scikit-Learn Model Training

  If your scikit-learn models are taking a bit of time to train, then there are several techniques you can use to make the processing more efficient. From optimizing your model configuration to leveraging libraries to speed up training through parallelization, you can build the best scikit-learn model possible in the least amount of time.

  https://www.kdnuggets.com/2021/03/speed-up-scikit-learn-model-training.html

• Graph Databases, Explained

  Between the four main NoSQL database types, graph databases are widely appreciated for their application in handling large sets of unstructured data coming from various sources. Let’s talk about how graph databases work and what are their practical uses.

  https://www.kdnuggets.com/2021/02/understanding-nosql-database-types-graph.html

• Feature Store as a Foundation for Machine Learning

  With so many organizations now taking the leap into building production-level machine learning models, many lessons learned are coming to light about the supporting infrastructure. For a variety of important types of use cases, maintaining a centralized feature store is essential for higher ROI and faster delivery to market. In this review, the current feature store landscape is described, and you can learn how to architect one into your MLOps pipeline.

  https://www.kdnuggets.com/2021/02/feature-store-foundation-machine-learning.html

• Column-Oriented Databases, Explained

  NoSQL Databases have four distinct types. Key-value stores, document-stores, graph databases, and column-oriented databases. In this article, we’ll explore column-oriented databases, also known simply as “NoSQL columns”.

  https://www.kdnuggets.com/2021/02/understanding-nosql-database-types-column-oriented-databases.html

• How to Speed up Scikit-Learn Model Training

  Scikit-Learn is an easy to use a Python library for machine learning. However, sometimes scikit-learn models can take a long time to train. The question becomes, how do you create the best scikit-learn model in the least amount of time?

  https://www.kdnuggets.com/2021/02/speed-up-scikit-learn-model-training.html

• Data Engineering — the Cousin of Data Science, is Troublesome">Data Engineering — the Cousin of Data Science, is Troublesome

  A Data Scientist must be a jack of many, many trades. Especially when working in broader teams, understanding the roles of others, such as data engineering, can help you validate progress and be aware of potential pitfalls. So, how can you convince your analysts to realize the importance of expanding their toolkit? Examples from real life often provide great insight.

  https://www.kdnuggets.com/2021/01/data-engineering-troublesome.html

• MLOps: Model Monitoring 101

  Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to the model building stage so that ML models can constantly improve themselves under different scenarios.

  https://www.kdnuggets.com/2021/01/mlops-model-monitoring-101.html

• 5 Reasons Why Containers Will Rule Data Science

  Historically, containers were a way to abstract a software stack away from the operating system. For data scientists, containers have historically offered few benefits.

  https://www.kdnuggets.com/2020/11/gigantum-containers-will-rule-data-science.html

• The Best Data Science Certification You’ve Never Heard Of">The Best Data Science Certification You’ve Never Heard Of

  The CDMP is the best data strategy certification you’ve never heard of. (And honestly, when you consider the fact that you’re probably working a job that didn’t exist ten years ago, it’s not surprising that this certification isn’t widespread just yet.)

  https://www.kdnuggets.com/2020/11/best-data-science-certification-never-heard.html

• Mastering Time Series Analysis with Help From the Experts

  Read this discussion with the “Time Series” Team at KNIME, answering such classic questions as "how much past is enough past?" others that any practitioner of time series analysis will find useful.

  https://www.kdnuggets.com/2020/10/mastering-time-series-analysis-experts.html

• 10 Underrated Python Skills

  Tips for feature analysis, hyperparameter tuning, data visualization and more.

  https://www.kdnuggets.com/2020/10/10-underrated-python-skills.html

• LinkedIn’s Pro-ML Architecture Summarizes Best Practices for Building Machine Learning at Scale

  The reference architecture is powering mission critical machine learning workflows within LinkedIn.

  https://www.kdnuggets.com/2020/09/linkedin-pro-ml-architecture-best-practices-building-machine-learning-scale.html

• DeepMind’s Three Pillars for Building Robust Machine Learning Systems

  Specification Testing, Robust Training and Formal Verification are three elements that the AI powerhouse believe hold the essence of robust machine learning models.

  https://www.kdnuggets.com/2020/08/deepmind-three-pillars-building-robust-machine-learning-systems.html

• Word Embedding Fairness Evaluation

  With word embeddings being such a crucial component of NLP, the reported social biases resulting from the training corpora could limit their application. The framework introduced here intends to measure the fairness in word embeddings to better understand these potential biases.

  https://www.kdnuggets.com/2020/08/word-embedding-fairness-evaluation.html

• A Tour of End-to-End Machine Learning Platforms

  An end-to-end machine learning platform needs a holistic approach. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!

  https://www.kdnuggets.com/2020/07/tour-end-to-end-machine-learning-platforms.html

• 10 Steps for Tackling Data Privacy and Security Laws in 2020

  Data privacy laws, such as the CCPA, GDPR, and HIPAA, are here to stay and significantly impact everyone in the digital era. These steps will guide organizations to prepare for compliance and ensure they support the fundamental privacy rights of their customers and users.

  https://www.kdnuggets.com/2020/07/10-steps-data-privacy-security-laws.html

• Apache Spark on Dataproc vs. Google BigQuery

  This post looks at research undertaken to provide interactive business intelligence reports and visualizations for thousands of end users, in the hopes of addressing some of the challenges to architects and engineers looking at moving to Google Cloud Platform in selecting the best technology stack based on their requirements and to process large volumes of data in a cost effective yet reliable manner.

  https://www.kdnuggets.com/2020/07/apache-spark-dataproc-vs-google-bigquery.html

• Largest Dataset Analyzed – Poll Results and Trends

  The results show that despite the deluge of Big Data, large majority still works in Gigabyte or Megabyte-size datasets. Data Scientists work with the largest-size datasets, followed by Data Engineers, Data Analysts, and Business Analysts. Read more for details.

  https://www.kdnuggets.com/2020/07/poll-largest-dataset-analyzed-results.html

• Software engineering fundamentals for Data Scientists

  As a data scientist writing code for your models, it's quite possible that your work will make its way into a production environment to be used by the masses. But, writing code that is deployed as software is much different than writing code for exploratory data analysis. Learn about the key approaches for making your code production-ready that will save you time and future headaches.

  https://www.kdnuggets.com/2020/06/software-engineering-fundamentals-data-scientists.html

• New Poll: What was the largest dataset you analyzed / data mined?

  Take part in KDnuggets latest survey to have your voice heard, and let the community know what the largest dataset size you have worked with is.

  https://www.kdnuggets.com/2020/06/new-poll-largest-dataset.html

• 5 Essential Papers on AI Training Data

  Data pre-processing is not only the largest time sink for most Data Scientists, but it is also the most crucial aspect of the work. Learn more about training data and data processing tasks from 5 leading academic papers.

  https://www.kdnuggets.com/2020/06/5-essential-papers-ai-training-data.html