DataRobot

JULY 27, 2021

In a global marketplace where decision-making needs to happen with increasing velocity, data science teams often need not only to speed up their modeling deployment but also do it at scale across their entire enterprise. Often, they are doing this with smaller teams in place than they need due to the shortage of data scientists.

Optimization 98

Optimization Modeling Machine Learning Deep Learning 98

Smart Data Collective

JUNE 4, 2021

What is data science? Data science is analyzing and predicting data, It is an emerging field. Some of the applications of data science are driverless cars, gaming AI, movie recommendations, and shopping recommendations. Data scientists use algorithms for creating data models.

Data Science 105

Data Science Statistics Deep Learning Machine Learning 105

Analytics Vidhya

APRIL 11, 2024

Employing a tiered approach empowers individuals and organizations to select the optimal model aligning with their specific requirements. Introduction Anthropic’s Claude 3 API ushers in a new era of artificial intelligence, offering a comprehensive suite of AI assistants tailored to diverse user needs.

Modeling 251

Modeling Optimization Analytics Deep Learning 251

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 data science? What is machine learning?

Machine Learning 59

Machine Learning Data Science Statistics Deep Learning 59

IBM Big Data Hub

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How the models are stored.

Data Science 57

Data Science Machine Learning Cost-Benefit Deep Learning 57

CIO Business Intelligence

JANUARY 24, 2024

Generative AI and large language models (LLMs) like ChatGPT are only one aspect of AI. It’s the culmination of a decade of work on deep learning AI. Model sizes: ~5 billion to >1 trillion parameters. Model sizes: ~Millions to billions of parameters. AI encompasses many things.

Deep Learning 112

Deep Learning Unstructured Data Machine Learning Modeling 112

DataRobot

FEBRUARY 10, 2021

Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. These errors might seem small, but the effects can be disastrous when the model is used to make decisions in the real world.

Modeling 98

Modeling Deep Learning Machine Learning Testing 98

datapine

AUGUST 14, 2019

Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Domino Data Lab

JULY 28, 2021

Data science is an exciting, interdisciplinary field that is revolutionizing the way companies approach every facet of their business. Data Science — A Venn Diagram of Skills. Data science encapsulates both old and new, traditional and cutting-edge. 3 Components of Data Science Skills.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

IBM Big Data Hub

DECEMBER 19, 2023

Anomalies are not inherently bad, but being aware of them, and having data to put them in context, is integral to understanding and protecting your business. The challenge for IT departments working in data science is making sense of expanding and ever-changing data points.

Machine Learning 71

Machine Learning Optimization Unstructured Data Sales 71

Rocket-Powered Data Science

DECEMBER 16, 2018

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. There are also examples of cold start in supervised machine learning (where you do have class labels on the training data).

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. Data Science Techniques.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Insight

SEPTEMBER 20, 2019

Segmentation Since a few patients had multiple images in the dataset, the data were separated, by patient, into three parts: training (80%), validation (10%), and testing (10%). The model was a modified U-Net and trained on GPU hosted by Amazon Web Services (AWS) EC2 instances. Here, we built a model to mimic this process.

Deep Learning 53

Deep Learning Cost-Benefit Testing Modeling 53

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

O'Reilly on Data

DECEMBER 10, 2019

We can collect many examples of what we want the program to do and what not to do (examples of correct and incorrect behavior), label them appropriately, and train a model to perform correctly on new inputs. In short, we can use machine learning to automate software development itself. Instead, we can program by example. and Matroid.

Software 261

Software Machine Learning Risk Data-driven 261

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Rocket-Powered Data Science

DECEMBER 15, 2018

In these applications, there is no requirement or presumption regarding the existence of labeled training data — we are essentially parameterizing or characterizing the patterns in the data ( e.g., the trends, correlations, segments, clusters, associations). How do we know that our progression is moving towards more accurate models?

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

Jen Stirrup

MAY 5, 2020

Recently, I gave a Make Your Data Work Monday webinar on the complexities of the data sources for data science in Azure, and I thought it important enough to turn into an actual post. How can you differentiate the different opportunities to store your data in Azure? The data is also distributed.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

Cloudera

APRIL 10, 2021

When working on complex, or rigorous enterprise machine learning projects, Data Scientists and Machine Learning Engineers experience various degrees of processing lag training models at scale. To overcome this, practitioners often turn to NVIDIA GPUs to accelerate machine learning and deep learning workloads. .

Machine Learning 63

Machine Learning Modeling Cost-Benefit Data Science 63

Cloudera

FEBRUARY 11, 2022

Advances in the development and application of Machine Learning (ML) and Deep Learning (DL) algorithms, require greater care to ensure that the ethics embedded in previous rule-based systems are not lost. What is Machine Learning. Instead, they are learned by training a model on data.

Machine Learning 96

Machine Learning Sales Data Science Modeling 96

O'Reilly on Data

JULY 28, 2020

There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. Ethics and Data Science is a short book that helps developers think through data problems, and includes a checklist that team members should revisit throughout the process.

Marketing 362

Marketing Experimentation Metrics Testing 362

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” Demystifying generative AI At the heart of Generative AI lie massive databases of texts, images, code and other data types.

Enterprise 124

Enterprise Modeling Testing Data-driven 124

CIO Business Intelligence

APRIL 25, 2022

But despite all the money flowing into ML projects, most organizations are struggling to get their ML models and applications working on production systems. . And another recent survey has the worst numbers of all, finding that 90% of ML models are not deployed to production. So what’s the problem? The problem with ML. research.

Machine Learning 134

Machine Learning Enterprise Deep Learning Cost-Benefit 134

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 predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics 122

Predictive Analytics Analytics Predictive Modeling Data mining 122

O'Reilly on Data

MARCH 7, 2019

Language understanding benefits from every part of the fast-improving ABC of software: AI (freely available deep learning libraries like PyText and language models like BERT ), big data (Hadoop, Spark, and Spark NLP ), and cloud (GPU's on demand and NLP-as-a-service from all the major cloud providers).

Deep Learning 227

Deep Learning Testing Machine Learning Modeling 227

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. Exceptional data may point to data entry or software integration errors, but also may point to threats or opportunities.

Statistics 84

Statistics Machine Learning Deep Learning Optimization 84

CIO Business Intelligence

JUNE 1, 2022

The company is applying winning insights from rapid, data-driven, evolutionary models versus relying on engine speed and aerodynamics alone to win races. Like professional basketball, industrial-scale farming, national politics, and global merchandising, auto racing has become a data science. A Competitive Differentiator.

Manufacturing 119

Manufacturing Digital Transformation Predictive Analytics Strategy 119

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. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 87

Metrics Machine Learning Risk Statistics 87

CIO Business Intelligence

JUNE 2, 2022

HPC5’s performance level enables sophisticated in-house algorithms to process subsoil data, as well as geophysical and seismic information from around the world. For optimizing existing resources, Eni uses HPC5 to model, study, and ultimately improve refinement operations. .

Informatics 98

Informatics Modeling Testing Technology 98

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Domino Data Lab

APRIL 29, 2021

Deep learning enthusiasts are increasingly putting NVIDIA’s GTC at the top of their gotta-be-there conference list. Three of them were particularly compelling and inspired a new point of view on transfer learning that I feel is important for analytical practitioners and leaders to understand. Deep Learning Trends from GTC21.

Data Science 134

Data Science Deep Learning Modeling Forecasting 134

DataRobot

OCTOBER 20, 2021

Data literacy ensures that diverse perspectives are baked into AI governance and leads to the production of AI systems that achieve better and more consistent outcomes. It is a key component of developing responsible AI, and promotes trust not only in AI concepts but also in individual AI models. What Is Data Literacy?

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. For instance, a parsing model could identify the subject, verb and object of a complete sentence. Topic modeling: Topic modeling aims to discover underlying themes and/or topics in a collection of documents.

Data mining 57

Data mining Machine Learning Deep Learning Marketing 57

CIO Business Intelligence

JANUARY 4, 2023

Here are the shifts we noted: Delivery Models : Moving from an almost strictly on-premises systems approach (with some remote access services), HPC is embracing remote delivery models. Of course, data center challenges are also driving demand for these alternatives. Just starting out with analytics?

Cost-Benefit 59

Cost-Benefit Machine Learning Modeling Uncertainty 59

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning in marketing and sales According to Forbes , marketing and sales teams prioritize AI and ML more than any other enterprise department. Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). Many stock market transactions use ML.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

Occam's Razor

MARCH 30, 2017

People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Most Deep Learning methods involve artificial neural networks, modeling how our bran works. Here’s the higher-order-bit….

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166