O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data poisoning attacks.

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

O'Reilly on Data

MARCH 24, 2020

On the machine learning side, we are entering what Andrei Karpathy, director of AI at Tesla, dubs the Software 2.0 By Wansink’s own admission in the blog post, that’s not what happened in his lab.” Data professionals spend an inordinate amount on time cleaning, repairing, and preparing data.

Machine Learning 364

Machine Learning Statistics Data Quality Data Collection 364

Rocket-Powered Data Science

JULY 6, 2021

If you include the title of this blog, you were just presented with 13 examples of heteronyms in the preceding paragraphs. My favorite approach to TAM creation and to modern data management in general is AI and machine learning (ML). What you have just experienced is a plethora of heteronyms. Can you find them all?

Strategy 267

Strategy Machine Learning Metadata Knowledge Discovery 267

datapine

NOVEMBER 27, 2019

Today, most companies understand the impact of data quality on analysis and further decision-making processes and hence choose to implement a data quality management (DQM) policy, department, or techniques. Artificial intelligence (AI) is the science aiming to make machines execute what is usually done by complex human intelligence.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

datapine

SEPTEMBER 27, 2022

Now it’s time to ponder over our hand-picked list of the 20 best SQL learning books available today. Whether you’re a programmer, a data analyst, or a business intelligence end user, knowing the best way to learn SQL is invaluable to anyone dealing with or handling digital data. SQL isn’t just for database administrators (DBAs).

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”.

Management 314

Management Machine Learning Metrics Modeling 314

Smarten

NOVEMBER 26, 2024

Data insight techniques provide a comprehensive set of tools, data analysis and quality assurance features to allow users to identify errors, enhance data quality, and boost productivity.’ Users do not have to learn complex systems or look to data scientists or business analysts for answers. But too much data can also create issues.

Data Quality 62

Data Quality Machine Learning Predictive Modeling Metadata 62

datapine

MARCH 25, 2019

Not only will we explore data analysis methods and techniques, but we’ll also look at different types of data analysis while demonstrating how to do data analysis in the real world with a 10-step blueprint for success. Top 10 Data Analysis Methods & Techniques. Exclusive Bonus Content: Why Is Analysis Important? Set your KPIs.

Key Performance Indicator 217

Key Performance Indicator Statistics Big Data Visualization 217

datapine

AUGUST 14, 2019

Data science tools are used for drilling down into complex data by extracting, processing, and analyzing structured or unstructured data to effectively generate useful information while combining computer science, statistics, predictive analytics, and deep learning. Here, we list the most prominent ones used in the industry. Source: RStudio.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Analytics Vidhya

OCTOBER 13, 2020

Introduction In this blog, I will explain how using simple machine learning. This article was published as a part of the Data Science Blogathon. The post Modernize Support Logs Using Simple Python Commands appeared first on Analytics Vidhya.

Machine Learning 328

Machine Learning Data Science Publishing Analytics 328

AWS Big Data

DECEMBER 4, 2024

Extract, transform, and load (ETL) is the process of combining, cleaning, and normalizing data from different sources to prepare it for analytics, artificial intelligence (AI), and machine learning (ML) workloads. Trigger a state machine in AWS Step Functions. You don’t need to maintain complex ETL pipelines.

Data Integration 115

Data Integration Data Lake Statistics Data-driven 115

Smarten

FEBRUARY 19, 2025

Predictive analytics encompasses techniques like data mining, machine learning (ML) and predictive modeling techniques like time series forecasting, classification, association, correlation, clustering, hypothesis testing and descriptive statistics to analyze current and historical data and predict future events, results and business direction.

Predictive Analytics 62

Predictive Analytics Analytics Predictive Modeling Forecasting 62

Domino Data Lab

MAY 20, 2021

Machine Learning algorithms often need to handle highly-imbalanced datasets. This in turns makes the performance evaluation of the classifier difficult, and can also harm the learning of an algorithm that strives to maximise accuracy. This renders measures like classification accuracy meaningless. Chawla et al.,

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Occam's Razor

MARCH 30, 2017

Machine Learning | Marketing. Machine Learning | Analytics. Invest in continuous learning. People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. AI is an intelligent machine. AI | Now | Global Maxima.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

datapine

DECEMBER 4, 2019

With technologies such as natural language processing, machine learning, pattern recognition cognitive computing is considered as a next-generation system that will help experts to make better decisions throughout industries such as healthcare, retail, security, and e-commerce, among others.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

DataKitchen

NOVEMBER 5, 2024

Data is typically organized into project-specific schemas optimized for business intelligence (BI) applications, advanced analytics, and machine learning. The faster the iteration, the more organizations learn, refine their processes, and elevate their data quality standards. No one likes their customer to find data errors.

Data Quality 195

Data Quality Testing Metrics Reporting 195

datapine

APRIL 21, 2022

Digital transformation of your business is possible when you can use emerging automation, Machine Learning (ML), and Artificial Intelligence (AI) technologies in your marketing. Interest: Next, you will have a set of customers interested in learning more about your business and whether it can meet their demands. Bottomline.

Digital Transformation 206

Digital Transformation Marketing Advertising Scorecard 206

Data Science 101

APRIL 26, 2019

Gopher Data – Gophers doing data analysis, no schedule events, last blog post was 2017 Gopher Notes – Golang in Jupyter Notebooks Lgo – Interactive programming with Jupyter for Golang Gota – Data frames for Go, “The API is still in flux so use at your own risk.” Machine Learning with Go?

Data Science 111

Data Science Machine Learning Interactive Risk 111

Cloudera

NOVEMBER 17, 2020

One of the many areas where machine learning has made a large difference for enterprise business is in the ability to make accurate predictions in the realm of fraud detection. The research team at Cloudera Fast Forward have written a report on using deep learning for anomaly detection. a Hive Table).

Deep Learning 115

Deep Learning Machine Learning Modeling Reporting 115

Cloudera

JANUARY 7, 2021

While dozens of techniques now exist for the fundamental task of text classification, many of them require massive amounts of labeled data in order to prove useful. However, collecting annotations for your use case is typically one of the most costly parts of the machine learning life cycle.

Machine Learning 107

Machine Learning Deep Learning IT 107

Cloudera

NOVEMBER 19, 2020

The Association of Certified Fraud Examiners reports the use of artificial intelligence and machine learning in anti-fraud programs is expected to almost triple in the next two years. Machine learning algorithms enable fraud detection systems to distinguish between legitimate and fraudulent behaviors.

Machine Learning 119

Machine Learning Data-driven Modeling Deep Learning 119

CIO Business Intelligence

APRIL 15, 2022

And 20% of IT leaders say machine learning/artificial intelligence will drive the most IT investment. Insights gained from analytics and actions driven by machine learning algorithms can give organizations a competitive advantage, but mistakes can be costly in terms of reputation, revenue, or even lives.

Analytics 145

Analytics Machine Learning Insurance Testing 145

Smart Data Collective

FEBRUARY 28, 2023

Here at Smart Data Collective, we have blogged extensively about the changes brought on by AI technology. Over the past few months, many others have started talking about some of the changes that we blogged about for years. Machine learning technology has already had a huge impact on our lives in many ways.

Machine Learning 117

Machine Learning Risk Cost-Benefit Marketing 117

datapine

JANUARY 6, 2022

5) Data Interpretation Techniques & Methods. Typically, narrative data is gathered by employing a wide variety of person-to-person techniques. These techniques include: Observations: detailing behavioral patterns that occur within an observation group. Data Interpretation Techniques and Methods. Table of Contents.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

AWS Big Data

DECEMBER 18, 2024

In this blog post, well dive into the various scenarios for how Cohere Rerank 3.5 The service also provides multiple query languages, including SQL and Piped Processing Language (PPL) , along with customizable relevance tuning and machine learning (ML) integration for improved result ranking. Overview of Cohere Rerank 3.5

Metrics 97

Metrics Modeling Data Processing Machine Learning 97

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. If not, take a look at the recording where we also cover a few of the points we’ll describe in this blog post. Maybe you also attended the webinar ?

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

Smart Data Collective

MAY 1, 2022

Multiple emails, social media posts, blogs, articles, and other text forms are generated daily. Text analysis , or text mining, is a machine—learning technique that can extract valuable data from large amounts of unstructured text. What is text analysis? This is where text analysis comes into the picture.

Unstructured Data 132

Unstructured Data Cost-Benefit Machine Learning Marketing 132

Smart Data Collective

JUNE 21, 2023

SEO techniques serve as a baseline. Before you can learn how to apply AI in SEO, you need to understand what SEO is in the first place. Keep reading to learn some ways that artificial intelligence can help with SEO. AI creates personalized optimization strategies, while SEO techniques serve as a baseline. What is SEO?

Optimization 144

Optimization Marketing Strategy ROI 144

Domino Data Lab

MAY 15, 2019

It focuses on his ML product management insights and lessons learned. Machine Learning Projects are Hard: Shifting from a Deterministic Process to a Probabilistic One. Over the years, I have listened to data scientists and machine learning (ML) researchers relay various pain points and challenges that impede their work.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

Cloudera

APRIL 6, 2022

This blog post was written by Elizabeth Howell, Ph.D AI requires good data and strong training algorithms, such as through machine learning, to make decisions about what data to send back to decision-makers. sat-1 artificial intelligence chip filters them out so that only usable data is returned,” ESA said in a blog post.

Big Data 103

Big Data Machine Learning Insurance Data Processing 103

Smart Data Collective

AUGUST 12, 2019

However, Google isn’t the only company using AI and machine learning in search. Machine Learning is Changing the Art of Link building. Machine learning is playing an important role in the nature of SEO strategies, including building links. Google is already using machine learning in Gmail, reaching a 99.9

Machine Learning 93

Machine Learning Marketing Publishing Strategy 93

DataRobot

AUGUST 25, 2021

In a recent blog, we talked about how, at DataRobot , we organize trust in an AI system into three main categories: trust in the performance in your AI/machine learning model , trust in the operations of your AI system, and trust in the ethics of your modelling workflow, both to design the AI system and to integrate it with your business process.

Metrics 98

Metrics Measurement Machine Learning Data Collection 98

CIO Business Intelligence

SEPTEMBER 12, 2023

Generative AI systems are information content development tools, not robots — you can ask such a tool to “Tell me all the common ways to infect a machine,” but you cannot ask it to “Infect these machines at this company.” Please see our Symantec Enterprise Blog and our Generative AI Protection Demo for more details.

Risk 131

Risk Enterprise Software Marketing 131

Cloudera

NOVEMBER 3, 2021

Thanks to pioneers like Andrew NG and Fei-Fei Li, GPUs have made headlines for performing particularly well with deep learning techniques. Today, deep learning and GPUs are practically synonymous. While deep learning is an excellent use of the processing power of a graphics card, it is not the only use.

Deep Learning 117

Deep Learning Machine Learning Data Science Cost-Benefit 117

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. This blog post motivates this problem more fully, and discusses monotonic splines and lattices as a solution. In this blog post, we describe how we impose common-sense “shape constraints” on complex models.

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

Cloudera

DECEMBER 8, 2020

Two years ago we wrote a research report about Federated Learning. You can read it online here: Federated Learning. Federated Learning is a paradigm in which machine learning models are trained on decentralized data. In federated learning, a network of nodes shares models rather than training data with a server.

Machine Learning 85

Machine Learning Data Lake Reporting Data Collection 85

AWS Big Data

OCTOBER 14, 2024

Amazon EMR provides a big data environment for data processing, interactive analysis, and machine learning using open source frameworks such as Apache Spark, Apache Hive, and Presto. These partition filters will speed up the SQL query execution and is one of the top query optimization techniques.

Metadata 105

Metadata Data Lake Modeling Data Warehouse 105