Domino Data Lab

JULY 10, 2019

New tools are constantly being added to the deep learning ecosystem. For example, there have been multiple promising tools created recently that have Python APIs, are built on top of TensorFlow or PyTorch , and encapsulate deep learning best practices to allow data scientists to speed up research.

Deep Learning 110

Deep Learning Visualization Recreation/Entertainment Data Processing 110

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

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.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

Smart Data Collective

JUNE 10, 2022

The Bureau of Labor Statistics reports that there are over 105,000 data scientists in the United States. Machine Learning Engineer. As a machine learning engineer, you would create data funnels and deliver software solutions. Machine Learning Scientist. Are you interested in a career in data science?

Data Science 119

Data Science Machine Learning Statistics Business Intelligence 119

CIO Business Intelligence

JUNE 7, 2022

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

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

OCTOBER 4, 2023

There’s also strong demand for non-certified security skills, with DevSecOps, security architecture and models, security testing, and threat detection/modelling/management attracting the highest pay premiums. AI skills more valuable than certifications There were a couple of stand-outs among those.

Prescriptive Analytics 135

Prescriptive Analytics IT Machine Learning Risk 135

Domino Data Lab

JULY 28, 2021

Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication. Math and Statistics Expertise.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

Data Science 57

Data Science Machine Learning Cost-Benefit Deep Learning 57

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 332

Management Machine Learning Experimentation Metrics 332

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.” Generative AI uses advanced machine learning algorithms and techniques to analyze patterns and build statistical models.

Enterprise 124

Enterprise Modeling Testing Data-driven 124

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Agreeing on metrics.

Marketing 362

Marketing Experimentation Metrics Testing 362

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Validating Machine Learning Models. This may be accomplished through a wide variety of tests, to develop a deeper introspection into how the model behaves.

Risk 52

Risk Modeling Metrics Business Objectives 52

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. In this article, take a deep dive into data science and how Domino’s Enterprise MLOps platform allows you to scale data science in your business. In fact, deep learning was first described theoretically in 1943.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Domino Data Lab

DECEMBER 1, 2019

In the context of machine learning, we consider data drift 1 to be the change in model input data that leads to a degradation of model performance. Step 4: Generate the test, train and noisy MNIST data sets. Generate the train and test sets (x_train, _), (x_test, _) = mnist.load_data() x_train = x_train.astype('float32') / 255.

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. While AGI promises machine autonomy far beyond gen AI, even the most advanced systems still require human expertise to function effectively. What are the types of AGI?

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

Domino Data Lab

JULY 17, 2021

Further, deep learning methods are built on the foundation of signal processing. NOTE : The scope of this blog is a machine learning approach to TISV. The likelihood function measures the goodness of fit of a statistical model to a sample of data for given values of the unknown parameters.

Modeling 64

Modeling Testing Machine Learning Measurement 64

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 108

Machine Learning Deep Learning Modeling Interactive 108

Domino Data Lab

AUGUST 9, 2021

With the right tools, your data science teams can focus on what they do best – testing, developing and deploying new models while driving forward-thinking innovation. Before selecting a tool, you should first know your end goal – machine learning or deep learning. This is no exaggeration by any means.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Smarten

APRIL 19, 2024

The Current State of AI in Analytics By combining machine learning with AI, analytics vendors can and have made strides in providing user-friendly, self-serve augmented analytics products that include the newest versions of these technologies: Machine Learning (ML) Generative Artificial Intelligence (GenAI) Deep Learning Natural Language Processing (..)

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

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 119

Deep Learning Machine Learning Data Science Cost-Benefit 119

Domino Data Lab

AUGUST 10, 2021

At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. PyTorch: used for deep learning models, like natural language processing and computer vision. It’s used for developing deep learning models.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MAY 8, 2019

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. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Not yet, if ever.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Data Science and Beyond

OCTOBER 15, 2017

Here are my thoughts from 2014 on defining data science as the intersection of software engineering and statistics , and a more recent post on defining data science in 2018. I’ve also dabbled in deep learning , marine surveys , causality , and other things that I haven’t had the chance to write about.

Data Science 71

Data Science Deep Learning Machine Learning Data-driven 71

DataRobot

NOVEMBER 9, 2021

Signal classification models are typically built using time series principles; traditionally used features that include central, windowed, lag, and lead statistics can do the job but sometimes there might be scenarios where we want to eke out more performance out of the data. Image courtesy towardsAI.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

Smart Data Collective

OCTOBER 14, 2019

Ludwig is a tool that allows people to build data-based deep learning models to make predictions. Ludwig is a TensorFlow-based toolbox that aims to allow people to use machine learning during their data work without having extensive prior knowledge. Here are some open-source options to consider. Apache Drill.

Data Science 69

Data Science Machine Learning Big Data Interactive 69

Domino Data Lab

JULY 15, 2021

Originally created for software development, Python is used in a variety of contexts, including deep learning research and model deployment. IDEs (also sometimes referred to as notebooks) is a coding tool that makes it easier to develop and test code. Some common IDEs are RStudio and Jupyter Notebook.

Data Science 145

Data Science Visualization Modeling Enterprise 145

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). The data types used in deep learning are interesting.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies.

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238

Smart Data Collective

NOVEMBER 9, 2022

There are a number of great applications of machine learning. One of the biggest benefits is testing processes for optimal effectiveness. The main purpose of machine learning is to partially or completely replace manual testing. Machine learning is used in many industries. Top ML Companies.

Machine Learning 99

Machine Learning Testing Cost-Benefit Data-driven 99

Domino Data Lab

JULY 9, 2019

Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. I still believe that data science is the craft of trying to apply machine learning to some real world problem.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

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 ” One of his more egregious errors was to continually test already collected data for new hypotheses until one stuck, after his initial hypothesis failed [4]. .” Let’s get everybody to do X.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

O'Reilly on Data

JANUARY 25, 2021

We’re not pretending the frameworks themselves are comparable—Spring is primarily for backend and middleware development (though it includes a web framework); React and Angular are for frontend development; and scikit-learn and PyTorch are machine learning libraries. AI, Machine Learning, and Data.

Machine Learning 363

Machine Learning Software Testing Technology 363