IBM Big Data Hub

JANUARY 9, 2024

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. High variance in a model may indicate the model works with training data but be inadequate for real-world industry use cases.

Enterprise 73

Enterprise Data Integration Data Quality Contextual Data 73

IBM Big Data Hub

MAY 20, 2024

Today, an organization’s quantum computing readiness score is most influenced by its operating model: if an organization invests in a team and a process to govern their quantum innovation, they are better positioned than peers that focus just on the technology without corresponding investment in their talent and innovation process.

Data Processing 97

Data Processing Optimization Experimentation Enterprise 97

AWS Big Data

MARCH 13, 2024

For building any generative AI application, enriching the large language models (LLMs) with new data is imperative. For the model used to create embeddings, we settled on all-mpnet-base-v2 to create a 768-dimensional vector space. After you review the cluster configuration, select the jump host as the target for the run command.

Data Processing 114

Data Processing Dashboards Machine Learning Management 114

Smart Data Collective

JUNE 4, 2021

Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Data science needs knowledge from a variety of fields including statistics, mathematics, programming, and transforming data. Mathematics, statistics, and programming are pillars of data science. Statistics.

Data Science 104

Data Science Statistics Deep Learning Machine Learning 104

datapine

MARCH 31, 2022

On the flip side, if you enjoy diving deep into the technical side of things, with the right mix of skills for business intelligence you can work a host of incredibly interesting problems that will keep you in flow for hours on end. 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

NOVEMBER 18, 2020

The Bureau of Labor Statistics estimates that the number of data scientists will increase from 32,700 to 37,700 between 2019 and 2029. Previously, such problems were dealt with by specialists in mathematics and statistics. Statistics, mathematics, linear algebra. It hosts a data analysis competition. Practical experience.

Data mining 143

Data mining Data Science Informatics Statistics 143

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 55

Machine Learning Data Science Statistics Deep Learning 55

Data Science 101

DECEMBER 11, 2019

Every year they host an excellent and influential conference focusing on many areas of data science. Topics of interest include artificial intelligence, big data, data analytics, data science, data mining, deep learning, knowledge graphs, machine learning, relational databases and statistical methods. 1989 to be exact. 22-27, 2020.

KDD 81

KDD Knowledge Discovery Data mining Statistics 81

O'Reilly on Data

SEPTEMBER 15, 2021

There was a lot of uncertainty about stability, particularly at smaller companies: Would the company’s business model continue to be effective? We also asked respondents what tools they used for statistics and machine learning and what platforms they used for data analytics and data management. Would your job still be there in a year?

Machine Learning 362

Machine Learning Statistics Reporting Consulting 362

IBM Big Data Hub

SEPTEMBER 13, 2023

Using IBM Cloud to transform global payments rails The need for continuously available payment processing requires a rethinking of end-of-day cycles and introduces the need for stand-in processing for the times when host applications are down for end-of-day cycles or unavailable due to system outages or maintenance.

Forecasting 75

Forecasting Statistics Modeling Data Processing 75

CIO Business Intelligence

JANUARY 20, 2023

Dashboards are hosted software applications that automatically pull together available data into charts and graphs that give a sense of the immediate state of the company. It uses data mining , data modeling, and machine learning to answer why something happened and predict what might happen in the future.

Business Intelligence 136

Business Intelligence Dashboards Data mining OLAP 136

CIO Business Intelligence

APRIL 25, 2022

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. It culminates with a capstone project that requires creating a machine learning model. Switchup rating: 5.0 (out

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

Smart Data Collective

JUNE 28, 2021

They are not subject to data loss from hosting it in the cloud, which might have retention policies outside their control. E-commerce companies are using a lot of great data centers and hosting options. They are leveraging hosting services like Hatching Web to reach more customers. Role of Data Centers in E-commerce.

Management 87

Management Big Data Data Processing Advertising 87

datapine

SEPTEMBER 27, 2022

Some of these ‘structures’ may include putting all the information; for instance, a structure could be about cars, placing them into tables that consist of makes, models, year of manufacture, and color. Ralph Kimball and Margy Ross co-authored this third edition of Kimball’s classic guide to dimensional modeling.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

Cost-Benefit 91

Cost-Benefit Modeling Manufacturing Interactive 91

Domino Data Lab

JULY 10, 2019

This blog also provides code examples with a Jupyter notebook that you can download or run via hosting provided by Domino. The model definition via a YAML file. The same encoding and decoding models developed for one task can be reused for different tasks. In my work, it is common for each run to represent a tweak of the model.

Deep Learning 110

Deep Learning Visualization Recreation/Entertainment Data Processing 110

O'Reilly on Data

MARCH 31, 2020

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. For machine learning systems used in consumer internet companies, models are often continuously retrained many times a day using billions of entirely new input-output pairs.

Management 327

Management Machine Learning Experimentation Metrics 327

Ontotext

MARCH 24, 2021

The availability of Wikidata identifiers for administrative territorial entities enables people to access geographical information (coordinates and polygons) using SPARQL federation as well as allows linking with various other statistical datasets for more in depth analysis of electoral behavior. The road ahead.

Statistics 142

Statistics Data Processing Publishing Metrics 142

IBM Big Data Hub

AUGUST 14, 2023

But business leaders are facing a host of talent-related challenges, as a new global study from the IBM Institute for Business Value (IBV) reveals , from the skills gap to shifting employee expectations to the need for new operating models. billion people in the global workforce, according to World Bank statistics.

IT 73

IT Consulting Data-driven Strategy 73

CIO Business Intelligence

AUGUST 5, 2022

Today, data visualization encompasses all manners of presenting data visually, from dashboards to reports, statistical graphs, heat maps, plots, infographics, and more. Choropleths use shades or patterns on a map to represent a statistical variable, such as population density by state. What is the business value of data visualization?

Visualization 115

Visualization Dashboards Statistics Business Intelligence 115

IBM Big Data Hub

DECEMBER 4, 2023

As part of our generative AI initiatives, we can demonstrate the ability to use a foundation model with prompt tuning to review the structured and unstructured data within the insurance documents (data associated with the customer query) and provide tailored recommendations concerning the product, contract or general insurance inquiry.

Insurance 54

Insurance Digital Transformation Risk Management Interactive 54

Sisense

OCTOBER 6, 2020

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. All descriptive statistics can be calculated using quantitative data. It’s generated by a host of sources in different ways.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. One reason to do ramp-up is to mitigate the risk of never before seen arms.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Cloudera

MAY 18, 2021

Prior the introduction of CDP Public Cloud, many organizations that wanted to leverage CDH, HDP or any other on-prem Hadoop runtime in the public cloud had to deploy the platform in a lift-and-shift fashion, commonly known as “Hadoop-on-IaaS” or simply the IaaS model.

Cost-Benefit 85

Cost-Benefit Data-driven Machine Learning Data Warehouse 85

CIO Business Intelligence

JUNE 14, 2022

These systems will also be hosted – or are planned to be hosted – in appropriate environments aligned to the cross-government cloud and technology infrastructure strategy. But delivery depends on addressing engrained governance, structure, politics, and resource models.”.

Strategy 89

Strategy IT Digital Transformation Data Processing 89

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

CIO Business Intelligence

SEPTEMBER 4, 2023

And each year, Hackergal hosts a hackathon event at the end of the school year for kids participating in coding clubs. You really can’t get better statistics in that sense, especially from a survey that you put out to kids that age group.

Statistics 99

Statistics Data Processing Technology Modeling 99

Alation

JANUARY 20, 2022

This is done by collecting data statistics. They can then collaborate around that data via shared conversations and queries hosted in the data catalog. In other words, we provide each of the capabilities described above in Alation’s data governance model, but in this case for data quality. Step 5: Data Profiling. Data patterns?

Data Quality 94

Data Quality Data Governance Metrics Statistics 94

CIO Business Intelligence

JANUARY 26, 2023

Tracking costs is just one small part of a system that is constantly gathering statistics and watching for anomalies. It will work across all major (and minor) clouds as well as pods hosted on premises. The modeling layer can build out amortization and consumption schedules to forecast future demand.

Management 113

Management Forecasting Dashboards Optimization 113

Smart Data Collective

AUGUST 8, 2019

FireEye claims that email is the launchpad for more than 90 percent of cyber attacks, while a multitude of other statistics confirm that email is the preferred vector for criminals. Legitimate cloud hosting : Phishing sites can evade the blacklisting trap if they are hosted on reputable cloud services, such as Microsoft Azure.

Big Data 85

Big Data Data Processing Machine Learning Predictive Analytics 85

Cloudera

SEPTEMBER 29, 2022

Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem. It is also agnostic to where the different domains are hosted. This data model is also the structure of the contract that is defined between the producers and consumers of the data.

Data Architecture 74

Data Architecture Metadata Data Warehouse Sales 74

The Unofficial Google Data Science Blog

JULY 18, 2023

Editor's note : The relationship between reliability and validity are somewhat analogous to that between the notions of statistical uncertainty and representational uncertainty introduced in an earlier post. Throughout, we’ll refer to our model-derived measurement of inter-rater reliability as the Intraclass Correlation Coefficient (ICC).

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Sisense

DECEMBER 11, 2019

We hosted over 150 people from more than 100 companies, who gathered to learn why data can supercharge their companies and how harnessing the huge power of data can take business from startup to unicorn. It’s why Sisense, having merged with Periscope Data in May 2019, chose to host this event in Tel Aviv. What VCs want from startups.

Data Lake 80

Data Lake Big Data Sales Data-driven 80

datapine

MAY 17, 2021

Businesses can benefit from improved data driven decision making as well as enhanced business processes and models and share insights across departments more fluently while propelling intelligent business strategies. What is a discovery model, and how do you use it in a real-world business context? 5) Develop a data discovery model.

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161

AWS Big Data

NOVEMBER 8, 2023

The 3-node RA3 16XL provisioned cluster that had previously been hosting their warehouse was taking around 12 hours to ingest this data to Amazon Redshift , and Gilead was looking to optimize the data ingestion process in a more dynamic manner. This data volume is expected to increase monthly and is fully refreshed each month.

Data Lake 78

Data Lake Data Warehouse Cost-Benefit Optimization 78

AWS Big Data

OCTOBER 18, 2023

Amazon Redshift ML makes it straightforward for data scientists to create, train, and deploy ML models using familiar SQL. AWS SCT highlights these objects in blue in the conversion statistics diagram and creates action items with a complexity attached to them. to indicate local host. You can also run predictions using SQL.

Analytics 98

Analytics Data Warehouse Testing Dashboards 98

Cloudera

OCTOBER 19, 2022

On the Connector page there is a summary of the connectors with some overall statistics, like how many connectors are running and/or failed; this can help determine if there are any errors at a glance. Below the overall statistics section there are three columns, one for Source Connectors, one for Topics , and one for Sink Connectors.

Sales 72

Sales Interactive Statistics Management 72