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. Watermark attacks.

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

Analytics Vidhya

DECEMBER 4, 2020

Introduction Random Forests are always referred to as black-box models. This article was published as a part of the Data Science Blogathon. Let’s try. The post Lets Open the Black Box of Random Forests appeared first on Analytics Vidhya.

Data Science 399

Data Science Publishing Modeling Analytics 399

datapine

MAY 14, 2019

2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Best for: This best data science book is especially effective for those looking to enter the data-driven machine learning and deep learning avenues of the field. 4) “Machine Learning Yearning” by Andrew Ng.

Data Science 350

Data Science Machine Learning Big Data Data-driven 350

AWS Big Data

OCTOBER 1, 2024

Over the last year, Amazon Redshift added several performance optimizations for data lake queries across multiple areas of query engine such as rewrite, planning, scan execution and consuming AWS Glue Data Catalog column statistics. Enabling AWS Glue Data Catalog column statistics further improved performance by 3x versus last year.

Data Lake 115

Data Lake Statistics Broadcasting Optimization 115

AWS Big Data

DECEMBER 17, 2024

Today, Amazon Redshift is used by customers across all industries for a variety of use cases, including data warehouse migration and modernization, near real-time analytics, self-service analytics, data lake analytics, machine learning (ML), and data monetization. Industry-leading price-performance: Amazon Redshift launches RA3.large

Data Lake 97

Data Lake Data Warehouse Data-driven Optimization 97

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. For each table ingested by the zero-ETL integration, two groups of logs are created: status and statistics.

Data Integration 114

Data Integration Data Lake Statistics Data-driven 114

Rocket-Powered Data Science

JULY 6, 2023

These AI applications are essentially deep machine learning models that are trained on hundreds of gigabytes of text and that can provide detailed, grammatically correct, and “mostly accurate” text responses to user inputs (questions, requests, or queries, which are called prompts). Guess what? It isn’t.

Statistics 152

Statistics Deep Learning Machine Learning Enterprise 152

Rocket-Powered Data Science

JULY 7, 2019

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Examples: (1-3) All those applications shown in the definition of Machine Learning. (4) See [link].

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

datapine

MARCH 25, 2019

Having bestowed your data analysis techniques and methods with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless. Conduct statistical analysis. Build a data management roadmap.

Key Performance Indicator 217

Key Performance Indicator Statistics Big Data Visualization 217

datapine

NOVEMBER 27, 2019

Often seen as the highest foe-friend of the human race in movies ( Skynet in Terminator, The Machines of Matrix or the Master Control Program of Tron), AI is not yet on the verge to destroy us, in spite the legit warnings of some reputed scientists and tech-entrepreneurs. Prescriptive analytics goes a step further into the future.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Rocket-Powered Data Science

OCTOBER 6, 2023

What is the point of those obvious statistical inferences? In statistical terms, the joint probability of event Y and condition X co-occurring, designated P(X,Y), is essentially the probability P(Y) of event Y occurring. How do predictive and prescriptive analytics fit into this statistical framework? ” “Just 26.5%

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Statistics 157

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you.

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

Rocket-Powered Data Science

MARCH 10, 2020

The book is awesome, an absolute must-have reference volume, and it is free (for now, downloadable from Neo4j ). In the discussion of power-law distributions, we see again another way that graphs differ from more familiar statistical analyses that assume a normal distribution of properties in random populations. Graph Algorithms book.

Metadata 250

Metadata Machine Learning Prescriptive Analytics ROI 250

Paul DeBeasi

JANUARY 30, 2019

Machine learning projects are inherently different from traditional IT projects in that they are significantly more heuristic and experimental, requiring skills spanning multiple domains, including statistical analysis, data analysis and application development. Four Options for Integrating Machine Learning with IoT.

Machine Learning 56

Machine Learning IoT Experimentation Statistics 56

Smart Data Collective

NOVEMBER 7, 2019

Machine learning is disrupting the mobile app development industry. Although mobile app developers have used machine learning in some way or another for years, they are finding new applications for it. Machine learning is particularly useful when it comes to avoiding many of the biggest mistakes that app developers make.

Machine Learning 68

Machine Learning Marketing Recreation/Entertainment Advertising 68

datapine

NOVEMBER 19, 2019

AI refers to the autonomous intelligent behavior of software or machines that have a human-like ability to make decisions and to improve over time by learning from experience. Currently, popular approaches include statistical methods, computational intelligence, and traditional symbolic AI.

Technology 279

Technology Internet of Things IT IoT 279

AWS Big Data

JANUARY 9, 2025

In this post, we use the term vanilla Parquet to refer to Parquet files stored directly in Amazon S3 and accessed through standard query engines like Apache Spark, without the additional features provided by table formats such as Iceberg. He is a former data engineer and is passionate about all things data and machine learning.

Metadata 111

Metadata Snapshot Cost-Benefit Optimization 111

Rocket-Powered Data Science

JULY 19, 2023

This was not a scientific or statistically robust survey, so the results are not necessarily reliable, but they are interesting and provocative. If my explanation above is the correct interpretation of the high percentage, and if the statement refers to successfully deployed applications (i.e.,

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

DataKitchen

NOVEMBER 5, 2024

For instance, records may be cleaned up to create unique, non-duplicated transaction logs, master customer records, and cross-reference tables. Data is typically organized into project-specific schemas optimized for business intelligence (BI) applications, advanced analytics, and machine learning.

Data Quality 198

Data Quality Testing Metrics Reporting 198

datapine

MARCH 31, 2022

According to the US Bureau of Labor Statistics, demand for qualified business intelligence analysts and managers is expected to soar to 14% by 2026, with the overall need for data professionals to climb to 28% by the same year. The Bureau of Labor Statistics also states that in 2015, the annual median salary for BI analysts was $81,320.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These statistical models are growing as a result of the wide swaths of available current data as well as the advent of capable artificial intelligence and machine learning.

Predictive Analytics 139

Predictive Analytics Analytics Predictive Modeling Data mining 139

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. Naidu has a PG diploma in Applied Statistics from the Indian Statistical Institute, Calcutta and BTech in Electrical and Electronics from NIT, Warangal.

Metadata 105

Metadata Data Lake Modeling Data Warehouse 105

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

IBM Big Data Hub

DECEMBER 19, 2023

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

Machine Learning 64

Machine Learning Optimization Unstructured Data Sales 64

CIO Business Intelligence

OCTOBER 13, 2023

Analytics/data science architect: These data architects design and implement data architecture supporting advanced analytics and data science applications, including machine learning and artificial intelligence. Data scientists are experts in applying computer science, mathematics, and statistics to building models.

Data Architecture 132

Data Architecture Data Warehouse Statistics Visualization 132

Rocket-Powered Data Science

MARCH 19, 2019

Here, we broaden our meaning of “bias” to go beyond model bias, which has the technical statistical meaning of “underfitting”, which essentially means that there is more information and structure in the data than our model has captured.

Big Data 192

Big Data Statistics Manufacturing Data Science 192

datapine

DECEMBER 4, 2019

Without a doubt, it’s a big technological advancement, and one of the big statistics buzzwords, but the extent to which it is believed to be already applied is vastly exaggerated. Augmented analytics was indeed previously referred to as “Smart Data Discovery”. The commercial use of predictive analytics is a relatively new thing.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

datapine

JANUARY 6, 2022

Data interpretation refers to the process of using diverse analytical methods to review data and arrive at relevant conclusions. Quantitative analysis refers to a set of processes by which numerical data is analyzed. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

Rocket-Powered Data Science

JULY 14, 2023

This was not a scientific or statistically robust survey, so the results are not necessarily reliable, but they are interesting and provocative. I recently saw an informal online survey that asked users what types of data (tabular; text; images; or “other”) are being used in their organization’s analytics applications.

Analytics 166

Analytics Internet of Things Data Science Prescriptive Analytics 166

CIO Business Intelligence

OCTOBER 13, 2023

It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models. This role is responsible for training, developing, deploying, scheduling, monitoring, and improving scalable machine learning solutions in the enterprise.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Birst BI

NOVEMBER 20, 2018

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. The simplest answer is that these terms refer to some of the many analytic methods available to Data Scientists.

Statistics 84

Statistics Machine Learning Deep Learning Sales 84

AWS Big Data

FEBRUARY 7, 2025

You will create a connector to SageMaker with Amazon Titan Text Embeddings V2 to create embeddings for a set of documents with population statistics. To learn more about deploying DeepSeek-R1 on SageMaker, refer to Deploying DeepSeek-R1 Distill Model on AWS using Amazon SageMaker AI. How is the trending comparing with Miami?

Data Processing 69

Data Processing Dashboards Modeling Statistics 69

BizAcuity

APRIL 1, 2023

1] With the rise of Big Data in today’s world, Machine Learning (ML) is popularly used to identify, assess, and monitor financial risks as well as detect various suspicious activities and transactions. Refine the model: Machine learning applications require meticulous attention to optimize an algorithm.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

BizAcuity

JANUARY 18, 2023

With the rise of Big Data in today’s world, Machine Learning (ML) is popularly used to identify, assess, and monitor financial risks as well as detect various suspicious activities and transactions. How Machine Learning Helps Detect and Prevent AML. OCR is widely used to digitize all kinds of physical documentation.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

AWS Big Data

MARCH 13, 2024

RAG is a machine learning (ML) architecture that uses external documents (like Wikipedia) to augment its knowledge and achieve state-of-the-art results on knowledge-intensive tasks. For more information on the choice of index algorithm, refer to Choose the k-NN algorithm for your billion-scale use case with OpenSearch.

Data Processing 132

Data Processing Dashboards Machine Learning Metrics 132

DataKitchen

MAY 10, 2021

Best practices include continuous monitoring of machine learning models for degradations in accuracy. . We liken this methodology to the statistical process controls advocated by management guru Dr. Edward Deming. In addition to statistical process controls, we recommend location and historical balance tests.

Testing 214

Testing Manufacturing Data Quality Statistics 214

FineReport

JUNE 11, 2021

According to the definition, business intelligence and analytics refer to the data management solutions implemented in companies to collect, analyze and drive insights from data. Business analytics is how companies use statistical methods and techniques to analyze historical data to gain new insights and improve strategic decision-making.

Business Intelligence 98

Business Intelligence Analytics Dashboards Statistics 98