Search results for key value store

Found 409 documents, 5930 searched:

• Key Factors Affecting the Time to Insights

  This report provides an overview of the key factors affecting the time to insights, including the benefits of BI and the need for tailored solutions.

  https://www.kdnuggets.com/2023/03/key-factors-affecting-time-insights.html

• Key Data Science, Machine Learning, AI and Analytics Developments of 2022

  It's the end of the year, and so it's time for KDnuggets to assemble a team of experts and get to the bottom of what the most important data science, machine learning, AI and analytics developments of 2022 were.

  https://www.kdnuggets.com/2022/12/key-data-science-machine-learning-ai-analytics-developments-2022.html

• Key-Value Databases, Explained

  Among the four big NoSQL database types, key-value stores are probably the most popular ones due to their simplicity and fast performance. Let’s further explore how key-value stores work and what are their practical uses.

  https://www.kdnuggets.com/2021/04/nosql-explained-understanding-key-value-databases.html

• Handling Missing Values in Time-series with SQL

  This article is about a specific use-case that comes up often when dealing with time-series data.

  https://www.kdnuggets.com/2022/09/handling-missing-values-timeseries-sql.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

• 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

• 5 Key Components of a Data Sharing Platform

  Read this article for an overview of what the components of a data-sharing platform are.

  https://www.kdnuggets.com/2022/05/5-key-components-data-sharing-platform.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

• Using Datawig, an AWS Deep Learning Library for Missing Value Imputation

  A lot of missing values in the dataset can affect the quality of prediction in the long run. Several methods can be used to fill the missing values and Datawig is one of the most efficient ones.

  https://www.kdnuggets.com/2021/12/datawig-aws-deep-learning-library-missing-value-imputation.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

• 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

• Feature Store vs Data Warehouse

  A feature store is a data warehouse of features for machine learning. Differently from a data warehouse, it is dual-database: one serving features at low latency to online applications and another storing large volumes of features. Learn how Data Scientists leverage this capability in production-deployed models.

  https://www.kdnuggets.com/2020/12/feature-store-vs-data-warehouse.html

• Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology">Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology

  Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.

  https://www.kdnuggets.com/2020/12/developments-trends-ai-data-science-machine-learning-technology.html

• Deploying Secure and Scalable Streamlit Apps on AWS with Docker Swarm, Traefik and Keycloak

  If you are a data scientist who just wants to get the work done but doesn’t necessarily want to go down the DevOps rabbit hole, this tutorial offers a relatively straightforward deployment solution leveraging Docker Swarm and Traefik, with an option of adding user authentication with Keycloak.

  https://www.kdnuggets.com/2020/10/deploying-secure-scalable-streamlit-apps-aws-docker-swarm-traefik-keycloak.html

• Key Machine Learning Technique: Nested Cross-Validation, Why and How, with Python code

  Selecting the best performing machine learning model with optimal hyperparameters can sometimes still end up with a poorer performance once in production. This phenomenon might be the result of tuning the model and evaluating its performance on the same sets of train and test data. So, validating your model more rigorously can be key to a successful outcome.

  https://www.kdnuggets.com/2020/10/nested-cross-validation-python.html

• AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020">AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020

  As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions.

  https://www.kdnuggets.com/2019/12/predictions-ai-machine-learning-data-science-research.html

• Anomaly Detection, A Key Task for AI and Machine Learning, Explained

  One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Anomaly detection refers to identification of items or events that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by a human expert.

  https://www.kdnuggets.com/2019/10/anomaly-detection-explained.html

• AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019">AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019

  Review of 2018 and Predictions for 2019 from our panel of experts, including Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Bill Schmarzo, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood.

  https://www.kdnuggets.com/2018/12/predictions-data-science-analytics-2019.html

• Big Data: Main Developments in 2017 and Key Trends in 2018">Big Data: Main Developments in 2017 and Key Trends in 2018

  As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2017 and their 2018 key trend predictions.

  https://www.kdnuggets.com/2017/12/big-data-main-developments-2017-key-trends-2018.html

• The Key to Data Monetization

  While I have talked frequently about the concept of Analytic Profiles, I’ve never written a blog that details how Analytic Profiles work. So let’s create a “Day in the Life” of an Analytic Profile to explain how an Analytic Profile works to capture and “monetize” your analytic assets.

  https://www.kdnuggets.com/2017/07/key-data-monetization.html

• An ode to the analytics grease monkeys

  Analytics is not one time job. It needs to be automated, deployed and improved for future business analytics requirements. Here an IBM expert discusses about development & deployment of analytics assets and capabilities of it.

  https://www.kdnuggets.com/2017/02/analytics-grease-monkeys.html

• 9 Key Deep Learning Papers, Explained">9 Key Deep Learning Papers, Explained

  If you are interested in understanding the current state of deep learning, this post outlines and thoroughly summarizes 9 of the most influential contemporary papers in the field.

  https://www.kdnuggets.com/2016/09/9-key-deep-learning-papers-explained.html

• Big Data Key Terms, Explained

  Just getting started with Big Data, or looking to iron out the wrinkles in your current understanding? Check out these 20 Big Data-related terms and their concise definitions.

  https://www.kdnuggets.com/2016/08/big-data-key-terms-explained.html

• Running Redis on Google Colab

  Open source Redis is being increasingly used in Machine Learning, but running it on Colab is different compared to on your local machine or with Docker. Read on for a 2-step tutorial on how to do it.

  https://www.kdnuggets.com/2022/01/running-redis-google-colab.html

• Graph Databases Burst into the Mainstream

  What do Amazon, Facebook, Google, IBM, Microsoft and Twitter have in common? They're all adopters of graph databases - a hot technology that continues to evolve.

  https://www.kdnuggets.com/2018/02/graph-databases-burst-into-the-mainstream.html

• 7 Steps to Understanding NoSQL Databases

  Are you a newcomer to NoSQL, interested in gaining a real understanding of the technologies and architectures it includes? This post is for you.

  https://www.kdnuggets.com/2016/07/seven-steps-understanding-nosql-databases.html

• Exploring the OpenAI API with Python

  Let’s learn all the useful services from the OpenAI.

  https://www.kdnuggets.com/exploring-the-openai-api-with-python

• Convert Python Dict to JSON: A Tutorial for Beginners

  Learn how to convert a Python dictionary to JSON with this quick tutorial.

  https://www.kdnuggets.com/convert-python-dict-to-json-a-tutorial-for-beginners

• Mistral 7B-V0.2: Fine-Tuning Mistral’s New Open-Source LLM with Hugging Face

  Access Mistral’s latest open-source model and fine-tune it on a custom dataset.

  https://www.kdnuggets.com/mistral-7b-v02-fine-tuning-mistral-new-open-source-llm-with-hugging-face

• WTF is Regularization and What is it For?

  This article explains the concept of regularization and its significance in machine learning and deep learning. We have discussed how regularization can be used to enhance the performance of linear models, as well as how it can be applied to improve the performance of deep learning models.

  https://www.kdnuggets.com/wtf-is-regularization-and-what-is-it-for

• Vector Database for LLMs, Generative AI, and Deep Learning

  Exploring the limitless possibilities of AI and making it context-aware.

  https://www.kdnuggets.com/vector-database-for-llms-generative-ai-and-deep-learning

• The Right Way to Access Dictionaries in Python

  Effectively accessing dictionaries data with Python’s get() and setdefault().

  https://www.kdnuggets.com/the-right-way-to-access-dictionaries-in-python

• Converting JSONs to Pandas DataFrames: Parsing Them the Right Way

  Navigating Complex Data Structures with Python's json_normalize.

  https://www.kdnuggets.com/converting-jsons-to-pandas-dataframes-parsing-them-the-right-way

• 5 Super Helpful SQL Cheat Sheets You Can’t Miss!

  Want to refresh your SQL skills? Bookmark these useful cheat sheets covering SQL basics, joins, window functions, and more.

  https://www.kdnuggets.com/5-super-helpful-sql-cheat-sheets-you-cant-miss

• 5 Ways of Converting Unstructured Data into Structured Insights with LLMs

  From Chaos to Clarity: Understanding the Unstructured Data Dilemma.

  https://www.kdnuggets.com/5-ways-of-converting-unstructured-data-into-structured-insights-with-llms

• Can Data Governance Address AI Fatigue?

  This post explains how data governance can help data scientists handle AI fatigue and build robust models.

  https://www.kdnuggets.com/can-data-governance-address-ai-fatigue

• What Junior ML Engineers Actually Need to Know to Get Hired?

  This article will provided you with a better understanding of what skills are required for a junior ML developer to be considered for a job. If you are looking to land your first job, you should read this article thoroughly.

  https://www.kdnuggets.com/what-junior-ml-engineers-actually-need-to-know-to-get-hired

• Evaluating Methods for Calculating Document Similarity

  The blog covers methods for representing documents as vectors and computing similarity, such as Jaccard similarity, Euclidean distance, cosine similarity, and cosine similarity with TF-IDF, along with pre-processing steps for text data, such as tokenization, lowercasing, removing punctuation, removing stop words, and lemmatization.

  https://www.kdnuggets.com/evaluating-methods-for-calculating-document-similarity

• Evolution in ETL: How Skipping Transformation Enhances Data Management

  This article provides an overview of two new data preparation techniques that enable data democratization while minimizing transformation burdens.

  https://www.kdnuggets.com/evolution-in-etl-how-skipping-transformation-enhances-data-management

• Back to Basics Week 4: Advanced Topics and Deployment

  Welcome back to Week 4 of KDnuggets’ "Back to Basics" series. This week, we will dive into more advanced topics such as neural networks and deployment.

  https://www.kdnuggets.com/back-to-basics-week-4-advanced-topics-and-deployment

• 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

• 5 Ways You Can Use ChatGPT Vision for Data Analysis

  Enhances data analysis by interpreting visual data, including math formula, data extraction, evaluating the results, dashboards, and charts.

  https://www.kdnuggets.com/5-ways-you-can-use-chatgpt-vision-for-data-analysis

• Overview of PEFT: State-of-the-art Parameter-Efficient Fine-Tuning

  Learn how Parameter-Efficient Fine-Tuning techniques like LoRA enable efficient adaptation of large language models using limited compute resources.

  https://www.kdnuggets.com/overview-of-peft-stateoftheart-parameterefficient-finetuning

• A Brief History of the Neural Networks

  From the biological neuron to LLMs: How AI became smart.

  https://www.kdnuggets.com/a-brief-history-of-the-neural-networks

• 7 Best Cloud Database Platforms

  Cloud databases have made it easier and cheaper to develop enterprise-level applications, offering flexibility, convenience, and standard database functionality. See what KDnuggets recommends.

  https://www.kdnuggets.com/7-best-cloud-database-platforms

• Best Practices for Building ETLs for ML

  This article talks about several best practices for writing ETLs for building training datasets. It delves into several software engineering techniques and patterns applied to ML.

  https://www.kdnuggets.com/best-practices-for-building-etls-for-ml

• Why SQL is THE Language to Learn for Data Science

  SQL is the essential data science language due to its universal database accessibility, efficient data cleaning capabilities, seamless integration with other languages, and requirement for most data science jobs.

  https://www.kdnuggets.com/why-sql-is-the-language-to-learn-for-data-science

• 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

• Leveraging GPT Models to Transform Natural Language to SQL Queries

  By training GPT to query with few-shot prompting.

  https://www.kdnuggets.com/leveraging-gpt-models-to-transform-natural-language-to-sql-queries

• The Top 5 Data Management Tools For Your Projects

  See what KDnuggets is recommending for the top 5 cutting-edge tools for cloud, ETL, transformation, master data management, and visualization.

  https://www.kdnuggets.com/top-5-data-management-tools-for-your-projects

• Job Trends in Data Analytics: NLP for Job Trend Analysis

  Perform job trend analysis and check the results using NLP.

  https://www.kdnuggets.com/job-trends-in-data-analytics-nlp-for-job-trend-analysis

• Deploying Your Machine Learning Model to Production in the Cloud

  Learn a simple way to have a live model hosted on AWS.

  https://www.kdnuggets.com/deploying-your-ml-model-to-production-in-the-cloud

• 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

• Working with Big Data: Tools and Techniques

  Where do you start in a field as vast as big data? Which tools and techniques to use? We explore this and talk about the most common tools in big data.

  https://www.kdnuggets.com/working-with-big-data-tools-and-techniques

• Data Management Principles for Data Science

  Back to Basics: Understanding key data management principles that data scientists should know.

  https://www.kdnuggets.com/data-management-principles-for-data-science

• 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

• Getting Started with Python Data Structures in 5 Steps

  This tutorial covers Python's foundational data structures - lists, tuples, dictionaries, and sets. Learn their characteristics, use cases, and practical examples, all in 5 steps.

  https://www.kdnuggets.com/5-steps-getting-started-python-data-structures

• Python Basics: Syntax, Data Types, and Control Structures

  Want to learn Python? Get started today by learning Python's syntax, supported data types, and control structures.

  https://www.kdnuggets.com/python-basics-syntax-data-types-and-control-structures

• Getting Started with Python for Data Science

  Back to Basics: A beginner's guide to setting up Python and understanding its role in data science.

  https://www.kdnuggets.com/getting-started-with-python-for-data-science

• How to Digest 15 Billion Logs Per Day and Keep Big Queries Within 1 Second

  This article describes a large-scale data warehousing use case to provide reference for data engineers who are looking for log analytic solutions. It introduces the log processing architecture and real-case practice in data ingestion, storage, and queries.

  https://www.kdnuggets.com/how-to-digest-15-billion-logs-per-day-and-keep-big-queries-within-1-second

• The Ultimate Guide to Mastering Seasonality and Boosting Business Results

  This post discusses the importance of media mix modeling and how it can be used to maximize the business impact of advertising. It also discusses the impact of seasonality on media advertising and how media mix modeling can be used to minimize the impact of seasonality on business outcomes.

  https://www.kdnuggets.com/2023/08/media-mix-modeling-ultimate-guide-mastering-seasonality-boosting-business-results.html

• Data Validation for PySpark Applications using Pandera

  New features and concepts.

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

• How to Build a Real-Time Recommendation Engine Using Graph Databases

  "You may also like" is a simple phrase that implies a new era in the way businesses interact and connect with their customers, and graph databases can easily help to build recommendation engines.

  https://www.kdnuggets.com/2023/08/build-realtime-recommendation-engine-graph-databases.html

• Top 6 Tools to Improve Your Productivity on Snowflake

  The post reviews 6 top tools for improving productivity with Snowflake for data preparation, visualization, integration, BI and governance.

  https://www.kdnuggets.com/2023/08/top-6-tools-improve-productivity-snowflake.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

• Introduction to Data Science: A Beginner’s Guide

  This article is a guide for new data scientists, and it's designed to help you get started quickly. It's meant to be a starting point, but if you're already in the market for a new job, you may want to read this article more.

  https://www.kdnuggets.com/2023/07/introduction-data-science-beginner-guide.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

• GPT-Engineer: Your New AI Coding Assistant

  GPT-Engineer is an AI-powered application builder that generates codebases from project descriptions. It simplifies building applications, including our key-value database example, and works well with GPT-4.

  https://www.kdnuggets.com/2023/07/gpt-engineer-ai-coding-assistant.html

• Database Optimization: Exploring Indexes in SQL

  Learn about Indexing in SQL and how you can increase the retrieval speed of the SELECT queries and WHERE clauses.

  https://www.kdnuggets.com/2023/07/database-optimization-exploring-indexes-sql.html

• How to Build a Streaming Semi-structured Analytics Platform on Snowflake

  Building a datalake for semi-structured data or json has always been challenging. Imagine if the json documents are streaming or continuously flowing from healthcare vendors then we need a robust modern architecture that can deal with such a high volume. At the same time analytics layer also needs to be created so as to generate value from it.

  https://www.kdnuggets.com/2023/07/build-streaming-semistructured-analytics-platform-snowflake.html

• Stop Hard Coding in a Data Science Project – Use Config Files Instead

  How to efficiently interact with config files in Python.

  https://www.kdnuggets.com/2023/06/stop-hard-coding-data-science-project-config-files-instead.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

• Using RAPIDS cuDF to Leverage GPU in Feature Engineering

  Improving Performance by Replacing Pandas with cuDF in Creating Data Frames and Engineering Features and Integrating with Google Colab.

  https://www.kdnuggets.com/2023/06/rapids-cudf-leverage-gpu-feature-engineering.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

• Advanced Feature Selection Techniques for Machine Learning Models

  Mastering Feature Selection: An Exploration of Advanced Techniques for Supervised and Unsupervised Machine Learning Models.

  https://www.kdnuggets.com/2023/06/advanced-feature-selection-techniques-machine-learning-models.html

• A Playbook to Scale MLOps

  MLOps teams are pressured to advance their capabilities to scale AI. We teamed up with Ford Motors to explore how to scale MLOps within an organization and how to get started.

  https://www.kdnuggets.com/2023/06/playbook-scale-mlops.html

• Solving 5 Complex SQL Problems: Tricky Queries Explained

  The 5 hardest things Josh Berry, a 15 year analytics professional, experienced while switching from Python to SQL. Offering examples, SQL code, and a resource to customize the SQL to your own project.

  https://www.kdnuggets.com/2022/07/5-hardest-things-sql.html

• How to Efficiently Scale Data Science Projects with Cloud Computing

  This article discusses the key components that contribute to the successful scaling of data science projects. It covers how to collect data using APIs, how to store data in the cloud, how to clean and process data, how to visualize data, and how to harness the power of data visualization through interactive dashboards.

  https://www.kdnuggets.com/2023/05/efficiently-scale-data-science-projects-cloud-computing.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

• What Is ChatGPT Doing and Why Does It Work?

  In this article, we will explain how ChatGPT works and why it is able to produce coherent and diverse conversations.

  https://www.kdnuggets.com/2023/04/chatgpt-work.html

• Text Summarization Development: A Python Tutorial with GPT-3.5

  Utilizing the power of GPT-3.5 to develop a simple summarize generator application.

  https://www.kdnuggets.com/2023/04/text-summarization-development-python-tutorial-gpt35.html

• How to Build a Scalable Data Architecture with Apache Kafka

  Learn about Apache Kafka architecture and its implementation using a real-world use case of a taxi booking app.

  https://www.kdnuggets.com/2023/04/build-scalable-data-architecture-apache-kafka.html

• Top 19 Skills You Need to Know in 2023 to Be a Data Scientist

  Skills like the ability to clean, transform, statistically analyze, visualize, communicate, and predict data.

  https://www.kdnuggets.com/2023/04/top-19-skills-need-know-2023-data-scientist.html

• 3 Ways to Merge Pandas DataFrames

  Combine Pandas data frames using the merge, join, and concatenate operations.

  https://www.kdnuggets.com/2023/03/3-ways-merge-pandas-dataframes.html

• 7 Must-Know Python Tips for Coding Interviews

  Preparing for your next Python coding interview? Here’s a list of useful tips that’ll come in handy and help you write Pythonic code.

  https://www.kdnuggets.com/2023/03/7-mustknow-python-tips-coding-interviews.html

• NoSQL Databases and Their Use Cases

  Learn about NoSQL Databases and their types like key-value, document, graph and column family with their use cases.

  https://www.kdnuggets.com/2023/03/nosql-databases-cases.html

• First Open Source Implementation of DeepMind’s AlphaTensor

  The first open-source implementation of AlphaTensor has been released and opens the door for new developments to revolutionize the computational performance of deep learning models.

  https://www.kdnuggets.com/2023/03/first-open-source-implementation-deepmind-alphatensor.html

• 4 Ways to Generate Passive Income Using ChatGPT

  Discover how you can leverage ChatGPT to generate passive income.

  https://www.kdnuggets.com/2023/03/4-ways-generate-passive-income-chatgpt.html

• SQL Query Optimization Techniques

  Learn how to optimize the queries written in SQL to make them execute faster and more memory efficient.

  https://www.kdnuggets.com/2023/03/sql-query-optimization-techniques.html

• Getting Started with Python Generators

  Learn about Python generators and write memory-efficient and Pythonic code.

  https://www.kdnuggets.com/2023/02/getting-started-python-generators.html

• Building a Recommender System for Amazon Products with Python

  I built a recommender system for Amazon’s electronics category.

  https://www.kdnuggets.com/2023/02/building-recommender-system-amazon-products-python.html

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• 25 Advanced SQL Interview Questions for Data Scientists

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• How to Package and Distribute Machine Learning Models with MLFlow

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• Why is Data Management so Important to Data Science?

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• How to Perform Motion Detection Using Python

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• The 5 Best Places To Host Your Data Science Portfolio

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• Prepare Your Data for Effective Tableau & Power BI Dashboards

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• Publications on Data Science, Machine Learning, AI & Analytics

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• Everything You Need to Know About Tensors

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• 6 Highest Paying Companies for Data Scientists

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• How to Ace Data Science Assessment Test by Using Automatic EDA Tools

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