Sat.Dec 18, 2021 - Fri.Dec 24, 2021

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Machine learning does not produce value for my business. Why?

KDnuggets

What is going on when machine learning can't make the jump from testing to production, and so doesn't add any business value?

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How To Use Data For Smarter Business Decisions

Smart Data Collective

Big data technology has become an invaluable asset to so many organizations around the world. There are a lot of benefits of utilizing data technology, such as improving financial reporting, forecasting marketing trends and efficient human resource allocation. It is crucial to business growth , as companies transition to more digital business models.

Big Data 136
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MLOPs Operations: A beginner’s Guide | Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction According to a report, 55% of businesses have never used a machine learning model before. Eighty-Five per cent of the models will not be brought into production. Lack of skill, a lack of change-management procedures, and the absence of automated systems are some […].

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Reducing The Cost Of Failure With DataOps

DataKitchen

The post Reducing The Cost Of Failure With DataOps first appeared on DataKitchen.

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The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.

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6 Predictive Models Every Beginner Data Scientist Should Master

KDnuggets

Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This post brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.

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Exploratory vs. Explanatory: The Difference Between Data Analysis and Data Presentation

Juice Analytics

?? Exploratory data analysis is.the "herding cats" ?? stage of working with data. It is a chaotic, often solitary, exercise requiring persistence in search of insights.finding what matters in the data by connecting data sources, determining relationships within the data, and understanding what measures and dimensions are most important.the starting point for working with data.

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2022 Big Data Predictions from the Cloud

DataKitchen

The post 2022 Big Data Predictions from the Cloud first appeared on DataKitchen.

Big Data 246
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Alternative Feature Selection Methods in Machine Learning

KDnuggets

Feature selection methodologies go beyond filter, wrapper and embedded methods. In this article, I describe 3 alternative algorithms to select predictive features based on a feature importance score.

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Python for Business: Optimize Pre-Processing Data for Decision-Making

Smart Data Collective

The rise of machine learning and the use of Artificial Intelligence gradually increases the requirement of data processing. That’s because the machine learning projects go through and process a lot of data, and that data should come in the specified format to make it easier for the AI to catch and process. Likewise, Python is a popular name in the data preprocessing world because of its ability to process the functionalities in different ways.

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ML Hyperparameter Optimization App using Streamlit

Analytics Vidhya

This article was published as a part of the Data Science Blogathon About Streamlit Streamlit is an open-source Python library that assists developers in creating interactive graphical user interfaces for their systems. It was designed especially for Machine Learning and Data Scientist team. Using Streamlit, we can quickly create interactive web apps and deploy them.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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Cloudera Data Engineering 2021 Year End Review

Cloudera

Since the release of Cloudera Data Engineering (CDE) more than a year ago , our number one goal was operationalizing Spark pipelines at scale with first class tooling designed to streamline automation and observability. In working with thousands of customers deploying Spark applications, we saw significant challenges with managing Spark as well as automating, delivering, and optimizing secure data pipelines.

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How to Speed Up XGBoost Model Training

KDnuggets

XGBoost is an open-source implementation of gradient boosting designed for speed and performance. However, even XGBoost training can sometimes be slow. This article will review the advantages and disadvantages of each approach as well as go over how to get started.

Modeling 153
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Artificial Intelligence and the Future of Databases in the Big Data Era

Smart Data Collective

Big data is a phrase that the industry coined in 1987 , but it took years before it became truly popular. By the time the name was a household term, big data was everywhere, and companies were seeking ways to store and use the data. Data scientists knew that big data could hold valuable insights. The key was finding a way to analyze it as it continued to flood in constantly.

Big Data 126
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12 Data Plot Types for Visualisation from Concept to Code

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction When data is collected, there is a need to interpret and analyze it to provide insight into it. This insight can be about patterns, trends, or relationships between variables. Data interpretation is the process of reviewing data through well-defined methods. They help assign meaning […].

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How to Build an Experimentation Culture for Data-Driven Product Development

Speaker: Margaret-Ann Seger, Head of Product, Statsig

Experimentation is often seen as an aspirational practice, especially at smaller, fast-moving companies who are strapped for time and resources. So, how can you get your team making decisions in a more data-driven way while continuing to remain lean and maintaining ship velocity? In this webinar, Margaret-Ann Seger, Head of Product at Statsig, will teach you how to build an experimentation culture from the ground-up, graduating from just getting started with data-driven development to operating

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3 key factors for a sales compensation plan that sparks motivation

Jedox

A sales compensation plan that motivates your sales team to reach their maximum potential is something sales executive dreams of. Ultimately, the most success is achieved through effective motivation. This blog post outlines three key factors that transform your sales compensation plan into a powerful source of motivation. A lack of oversight into performance, delayed compensation payments, unsatisfactory sales incentives and commission payments.

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Hands-On Reinforcement Learning Course, Part 1

KDnuggets

Start your learning journey in Reinforcement Learning with this first of two part tutorial that covers the foundations of the technique with examples and Python code.

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Benefits of Using AI Optimized Video Messaging at Work

Smart Data Collective

Artificial intelligence has become an invaluable form of technology for fostering better communications in the workplace. Artificial intelligence has been a beneficial changing force for many forms of communication technology. Video messaging is just one example. Video technology is becoming much more sophisticated. More video messaging services are dependent on data analytics, as the analytics in video market is growing over 20% a year.

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Anomaly Detection Model on Time Series Data in Python using Facebook Prophet

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction Time series data is the collection of data at specific time intervals like on an hourly basis, weekly basis. Stock market data, e-commerce sales data is perfect example of time-series data. Time-series data analysis is different from usual data analysis because you can […].

Modeling 392
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Entity Resolution Checklist: What to Consider When Evaluating Options

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

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The Best of Both Worlds for AI Success: Quick Wins & Long-Term Transformation

Dataiku

The stakes have never been higher in a changing world that demands constant agility and adaptability from businesses across all industries, and the race is on for organizations to fully transform with AI. That said, urgency doesn’t translate to ease. Many organizations still feel overwhelmed by the decisions and challenges that stand in the way of implementing AI throughout their business processes.

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A Faster Way to Prepare Time-Series Data with the AI & Analytics Engine

KDnuggets

Many real-world datasets consist of records of events that occur at arbitrary and irregular intervals. These datasets then need to be processed into regular time series for further analysis. We will use the AI & Analytics Engine to illustrate how you can prepare your time-series data in just 1 step.

Analytics 136
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Get Maximum Value from Your Visual Data

DataRobot

The value of AI these days is undeniable. However, in a fast-changing environment, a decision made at the right time is critical. We collect more and more diverse data types, and we’re not always sure how we can turn this data into real value. Sometimes it takes hours and days of experimenting to get valuable insights. Or even if we have a pretty good understanding of the problem, there is not enough data to run a successful project and deliver impact back to the business.

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A Comprehensive Guide on Markov Chain

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Overview · Markovian Assumption states that the past doesn’t give a piece of valuable information. Given the present, history is irrelevant to know what will happen in the future. · Markov Chain is a stochastic process that follows the Markovian Assumption. · Markov chain […].

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Driving Business Impact for PMs

Speaker: Jon Harmer, Product Manager for Google Cloud

Move from feature factory to customer outcomes and drive impact in your business! This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. You will deepen your understanding of your customers and their needs as well as identifying and de-risking the different kinds of hypotheses built into your roadmap.

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Can Deep Learning Change the Game for Time Series Forecasting?

Dataiku

The encoder-decoder framework is undoubtedly one of the most popular concepts in deep learning. Widely used to solve sophisticated tasks such as machine translation, image captioning, and text summarization, it has led to great breakthroughs.

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Why we will always need humans to train AI — sometimes in real-time

KDnuggets

Customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data are necessary to train and perfect the AI that impacts our lives and daily conveniences.

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Please stop plagiarising my blog posts

Jen Stirrup

I know you will see this message. I’ve emailed you to ask you to stop it. Stop copying my posts and material and passing them off as your own. You are not me, and you never will be. Find your own voice. Write about your own experiences, successes and failures. You bring shame upon yourself by tritely stealing my work. This is straightforward thievery of my time, ideas and content.

IT 101
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Multiclass Classification Using Transformers for Beginners

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction In the last article, we have discussed implementing the BERT model using the TensorFlow hub; you can read it here. Implementing BERT using the TensorFlow hub was tedious since we had to perform every step from scratch. First, we build our tokenizer, then […]. The post Multiclass Classification Using Transformers for Beginners appeared first on Analytics Vidhya.

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Reimagined: Building Products with Generative AI

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.

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Log4j Mitigation Resources and Tools

CDW Research Hub

The Apache Log4j remote code vulnerability discovered in early December has the entire cybersecurity industry—from practitioners to vendors—scrambling to understand the exploit, identify impacted systems, and determine the best response, especially if quickly updating systems isn’t possible. Severity and impact. If you don’t already know what the Log4j 0-day exploit is and what it means, this article is a primer, but here are a few quick facts: Vendor application: Apache Log4j (v2).

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Tips & Tricks of Deploying Deep Learning Webapp on Heroku Cloud

KDnuggets

Check out these key development issues and tips learned from personal experience when deploying a TensorFlow-based image classifier Streamlit app on a Heroku server.

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Dashboard vs. Scorecard: Differences, Advantages, Templates

FineReport

As important parts of business intelligence, scorecards and dashboards can both play an obvious role in promoting enterprise performance management. However, many users are confused with the difference between scorecard vs. dashboard. This article aims to provide a reference for the choice of enterprises. Definition of scorecard and dashboard. What is a scorecard?

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Building a custom CNN model: Identification of COVID-19

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Dear readers, In this blog, let’s build our own custom CNN(Convolutional Neural Network) model all from scratch by training and testing it with our custom image dataset. This is, of course, mostly considered a more impressive work rather than training a pre-trained CNN model […].

Modeling 373
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Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity

Speaker: Nicholas Zeisler, CX Strategist & Fractional CXO

The first step in a successful Customer Experience endeavor (or for that matter, any business proposition) is to find out what’s wrong. If you can’t identify it, you can’t fix it! 💡 That’s where the Voice of the Customer (VoC) comes in. Today, far too many brands do VoC simply because that’s what they think they’re supposed to do; that’s what all their competitors do.