IBM Big Data Hub

DECEMBER 4, 2023

Advacements in machine learning algorithms, neural networks and the computational power of generative AI, combined with human expertise, intuition and creativity, can unlock new possibilities and achieve levels of innovation that were previously unimaginable.

Experimentation 67

Experimentation Modeling Metrics Machine Learning 67

Cloudera

FEBRUARY 25, 2021

While there are many factors that can contribute to this inefficiency, one of the most prevalent hurdles to overcome has to do with simply getting projects off the ground and selecting the right approaches, algorithms, and applications that will lead to fast results and trustworthy decision making. .

Deep Learning 83

Deep Learning Machine Learning Forecasting Visualization 83

Cloudera

MARCH 21, 2022

Data Science tools, algorithms, and practices are rapidly evolving to solve business problems on an unprecedented scale. Finding the right data and getting access to it are two of the top pain points we hear from our customers. The first step in any machine learning project is finding and getting access to the data store.

Machine Learning 83

Machine Learning Predictive Modeling Data Science Testing 83

Cloudera

APRIL 19, 2021

In our previous blog post in this series , we explored the benefits of using GPUs for data science workflows, and demonstrated how to set up sessions in Cloudera Machine Learning (CML) to access NVIDIA GPUs for accelerating Machine Learning Projects. Introduction. Follow along with the code here.

Deep Learning 73

Deep Learning Machine Learning Data Science Modeling 73

Smart Data Collective

JUNE 21, 2023

Before you can learn how to apply AI in SEO, you need to understand what SEO is in the first place. SEO has gone through many iterations over the years as Google frequently updates its algorithm. In any case, there are four fundamental aspects of SEO that remain as important today as they have ever been. The good news?

Optimization 121

Optimization Marketing Strategy ROI 121

IBM Big Data Hub

JULY 19, 2023

The explosion in digital data—in combination with declining availability of skilled and willing human resources to ingest and process the digital data in a compliant manner—is forcing life sciences organizations to explore AI, machine learning and now generative AI technologies.

Consulting 65

Consulting Machine Learning Manufacturing Optimization 65

DataRobot

MARCH 9, 2021

This blog is meant to be a fun take on predicting Song and Record of the Year for the 63rd Annual GRAMMY Awards. What I love about doing this side project each year is that it demonstrates how ubiquitous data science and machine learning can be — from tried and true traditional use cases to non-traditional ones like the GRAMMYs.

Machine Learning 98

Machine Learning Recreation/Entertainment Modeling Data Science 98

Smart Data Collective

AUGUST 12, 2019

Google is constantly developing new artificial intelligence algorithms to improve search engine results. However, Google isn’t the only company using AI and machine learning in search. SEO professionals are using AI as well. One of the biggest uses is with link building. percent of the time.”.

Machine Learning 76

Machine Learning Marketing Strategy Publishing 76

IBM Big Data Hub

APRIL 13, 2023

After developing a machine learning model, you need a place to run your model and serve predictions. Someone with the knowledge of SQL and access to a Db2 instance, where the in-database ML feature is enabled, can easily learn to build and use a machine learning model in the database.

Modeling 91

Modeling Statistics Machine Learning Testing 91

Sisense

APRIL 10, 2020

People looking into data science languages are usually confused about which language they should learn first: R or Python. Both are extremely useful for an array of data science applications, including Natural Language Processing (NLP). NLP can be used on written text or speech data. and perform analyses. Libraries for NLP.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data. What is text mining?

Data mining 58

Data mining Machine Learning Deep Learning Marketing 58

Ontotext

MAY 26, 2021

But, as Ontotext’s CEO Atanas Kiryakov often says, we forget that nobody nurtures the Artificial Intelligence (AI) systems for 7 years to learn how to walk, talk, count, read, write and, even more important, not touch hot stoves and avoid tilting full glasses and asking: Can I get 3 more cookies? What do AI systems need to learn?

Data Quality 87

Data Quality Machine Learning Measurement Metadata 87

Ontotext

FEBRUARY 1, 2024

Not only are these ontologies capable of representing any domain of knowledge, but the structure of the data model is also transparent and intelligible to both humans and machines. Ontologies in RDF also support machine inferencing, allowing new knowledge to be derived from existing data.

Enterprise 59

Enterprise Management Metadata Modeling 59

Ontotext

MARCH 8, 2023

Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. For many organizations, however, the question remains, “Is it the right solution for us?” Several factors are driving the adoption of knowledge graphs. We get this question regularly.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Ontotext

JUNE 22, 2023

This will minimize the risk of investing resources in fundamentally wrong assumptions and will give researchers more confidence to choose top targets for validation. They need some kind of compass to find their way in the data jungle and identify information that is useful and relevant to their specific use case.

Metrics 52

Metrics Statistics Visualization Metadata 52

IBM Big Data Hub

SEPTEMBER 19, 2023

DevOps , SRE , Platform, I TOps, and developer teams are all under pressure to keep applications performant while operating faster and smarter than ever. Modernization is hard enough; observability should be easy to install, simple to use and transcend technology stacks.

IT 88

IT Metrics Forecasting Technology 88

IBM Big Data Hub

OCTOBER 9, 2023

Artificial intelligence (AI) can help usher in a new era of human resource management, where data analytics, machine learning and automation can work together to save people time and support higher-quality outcomes. These conversations are happening as the workforce is undergoing a major change.

Machine Learning 74

Machine Learning Management Optimization Technology 74

Ontotext

NOVEMBER 4, 2021

These models originate from different use cases: distributed knowledge representation and open data publishing on the web vs graph analytics designed to be as easy to start with as possible. This post continues our series in which we want to provide an overview of what we do and how our webinars fit into it. The Background Story.

Metadata 98

Metadata Visualization Modeling Enterprise 98

Sisense

JANUARY 6, 2021

What are white-labeled reports White-label reports: Under the hood Exploring white-label dashboards Use case snapshots Horsepower under the hood. You get to provide them with top-tier reporting power, just by choosing the right platform and integrating it with your product. Every company is becoming a data company.

Dashboards 119

Dashboards Reporting Snapshot Visualization 119

CIO Business Intelligence

APRIL 24, 2023

When prompted with natural language instructions, one can use these learnings in a context-specific manner to generate an output of astonishing sophistication. Deep learning emerged in academia in the early 2000s, with broader industry adoption starting around 2010. These foundation models are trained on vast amounts of data.

Enterprise 52

Enterprise Deep Learning Modeling Machine Learning 52

Sisense

JANUARY 22, 2021

A well-structured fintech algorithm could easily use machine learning (ML) to advise a human agent to approve or reject a business loan application. The resulting insight is the algorithm’s classification of the applicant as an acceptable or unacceptable risk. Actionable insights example: Broadridge.

Machine Learning 52

Machine Learning Unstructured Data Forecasting Advertising 52

Rita Sallam

APRIL 2, 2023

The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms. The Bake-Offs are fast-paced, fun and informative sessions that let you see leading vendors side-by-side using scripted demos and a common data set in a controlled setting.

Optimization 52

Optimization Machine Learning Insurance Risk 52

IBM Big Data Hub

OCTOBER 9, 2023

As governments around the globe continue experimenting with the use of AI, and looking into ways to tap into foundational models offered by generative AI, one important question stands out: How will citizens benefit from the technology? The public sector is called just that for a reason: the public should always be the priority.

Interactive 54

Interactive Technology Statistics Consulting 54

Cloudera

AUGUST 25, 2020

Thank you for joining us for part two of our discussion around data, analytics and machine learning within the Financial Service Sector Dr. Thank you for joining us for part two of our discussion around data, analytics and machine learning within the Financial Service Sector Dr. Harmon.

Prescriptive Analytics 72

Prescriptive Analytics Technology Machine Learning Risk 72

IBM Big Data Hub

SEPTEMBER 27, 2022

This has created concerns among both private and public organizations as to whether AI is being used responsibly. To scale use of AI in a responsible manner requires AI governance, the process of defining policies and establishing accountability throughout the AI lifecycle. Data and insights belong to their creator. Is it fair?

Consulting 52

Consulting Machine Learning Modeling Strategy 52

Domino Data Lab

AUGUST 6, 2021

When you factor in the requirements of a business-critical machine learning model in a working enterprise environment, the old cat-herding meme won’t even get a smile. Develop: includes accessing and preparing data and algorithms, researching and development of models and experimentation. 4 Steps in the DSLC. Assess the Project.

Data Science 75

Data Science Testing Modeling Publishing 75

Cloudera

SEPTEMBER 18, 2020

In Part Two they will look at how businesses in both sectors can move to stabilize their respective supply chains and use real-time streaming data, analytics, and machine learning to increase operational efficiency and better manage disruption. The 6 key takeaways from this blog are below: 6 key takeaways.

Manufacturing 86

Manufacturing IoT Forecasting Machine Learning 86

Ontotext

JANUARY 10, 2020

In the era of bots serving as investment vehicles and plenty of platforms offering tools for automated investing, could it be that the strength of investment strategies soon becomes a matter of computing power and algorithms well-built? Long story short, it takes a well-built knowledge graph and a set of algorithms to mine it.

Data mining 59

Data mining Machine Learning Data-driven Marketing 59

datapine

AUGUST 20, 2019

Analyzing loads of data that are not accurate and contain anomalies is of no use as it corrupts business operations. The Big data problem can be comprehended properly using a layered architecture. The Big data problem can be comprehended properly using a layered architecture. Videos, pictures etc. fall under this category.

Big Data 100

Big Data B2B Cost-Benefit Structured Data 100

Rita Sallam

JUNE 2, 2023

The first featured analytics and BI platform Gartner Magic Quadrant leaders while the other showcased high interest data science and machine learning platforms. The Show Floor Showdowns were held on the Exhibit Hall show floor and were based on and abridged version of the same demo script and data set used in the Bake-Offs.

Optimization 52

Optimization Insurance Machine Learning Risk 52

BizAcuity

JULY 22, 2019

One where every sector around us, right from transport, retail and manufacturing to automobiles, is undergoing a major transformation. That’s because fleets nowadays have a range of vehicle monitoring sensors embedded in them, which enables us to track the vehicle condition, GPS location and more. The Evolution of Fleet Management.

IoT 93

IoT Management Internet of Things Optimization 93

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Validating Machine Learning Models. Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. Effective validation helps ensure that models are sound.

Risk 52

Risk Modeling Metrics Business Objectives 52

Sisense

FEBRUARY 3, 2020

Create This is your firsthand look at what that means to us. We rejoice in what makes us human and what keeps pushing us forward: problem solving, imagination, and curiosity.”. “So impressive to see the level of customer-centric innovation possible in 24 hours or (much) less, using AWS! At Sisense, we are builders.

Machine Learning 97

Machine Learning Visualization Management Analytics 97

Occam's Razor

MARCH 30, 2017

Primarily because we got our first real everyday access to products and services that used some form of AI to delight us. Machine Learning | Marketing. Machine Learning | Analytics. A real problem for all of us. Google Photos uses AI to solve this problem. AI | Now | Global Maxima. Cool, right?

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

BizAcuity

JULY 13, 2021

Artificial Intelligence impersonates human intelligence using various algorithms to collect data and improve performance with data compliance over some time. For the mass of collected data that AI technology can process and use, the customer’s habits are known.

Optimization 52

Optimization Marketing Analytics Data Warehouse 52

Alation

OCTOBER 5, 2021

It begins with establishing key parameters: What is data, who can use it, how can they use it, and why? For organizations to succeed in monitoring, managing, and optimizing their data resources, they must always keep the importance of their data-driven initiatives at the top of all strategic planning agendas.

Data Governance 96

Data Governance Strategy Data Quality Metrics 96

Sisense

JUNE 12, 2019

Stay tuned for more blogs by me as we roll these capabilities out!). In most of the cases, these plans turned into dark lakes of ever accumulating files that went unused due to the lack of skills, tools, or resources for analytics implementation. Yesterday, during Eureka! , If you were not in attendance at Eureka! Back to my session.

Analytics 75

Analytics Data-driven Dashboards IoT 75