IBM Big Data Hub

MARCH 27, 2024

Some companies attempt to estimate Scope 3 emissions by collecting data from suppliers and manually categorizing data, but progress is hindered by challenges such as large supplier base, depth of supply chains, complex data collection processes and substantial resource requirements.

Experimentation 77

Experimentation Modeling Deep Learning Data-driven 77

Sisense

APRIL 10, 2020

Some standard Python libraries are Pandas, Numpy, Scikit-Learn, SciPy, and Matplotlib. These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling. Libraries used for NLP are: NLTK, gensim, SpaCy , glove, and Scikit-Learn. Every library has its own purpose and benefits.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Smart Data Collective

APRIL 24, 2020

It’s impossible to deny the importance of data in several industries, but that data can get overwhelming if it isn’t properly managed. The problem is that managing and extracting valuable insights from all this data needs exceptional data collecting, which makes data ingestion vital.

Data Lake 97

Data Lake Data Collection Deep Learning Management 97

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. There won’t be any need for them.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

O'Reilly on Data

DECEMBER 10, 2019

That doesn’t mean we aren’t seeing tools to automate various aspects of software engineering and data science. Those tools are starting to appear, particularly for building deep learning models. We’re seeing continued adoption of tools like AWS’ Sagemaker and Google’s AutoML. and Matroid.

Software 261

Software Machine Learning Risk Data-driven 261

O'Reilly on Data

JULY 28, 2020

This tradeoff between impact and development difficulty is particularly relevant for products based on deep learning: breakthroughs often lead to unique, defensible, and highly lucrative products, but investing in products with a high chance of failure is an obvious risk. arbitrary stemming, stop word removal.). Conclusion.

Marketing 362

Marketing Experimentation Metrics Testing 362

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.

Data Science 54

Data Science Machine Learning Cost-Benefit Deep Learning 54

DataRobot

OCTOBER 20, 2021

The flow of data through an organization: Mapping how data flows through an organization helps organizations get and stay aligned on potential bias risks with data collection and data degradation. rule-based AI , machine learning , deep learning , etc.)

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

bridgei2i

MARCH 9, 2021

Machine Learning: Since intelligence without learning isn’t intelligence, this subset of AI focuses on parsing data and modifying itself without human effort. ML techniques provide better data-based outputs over time. Deep Learning: DL falls under ML, but its capabilities aren’t comparable.

Technology 52

Technology Deep Learning Data-driven Optimization 52

CIO Business Intelligence

SEPTEMBER 22, 2022

“Data is at the core of digital transformation, and data ingestion, transmission, storage, and analysis are key steps,” says Chen “Huawei provides full-stack data collection, transmission, storage, computing, and analysis solutions to effectively support end-to-end closed-loop data processing.”.

Technology 52

Technology Digital Transformation Broadcasting Uncertainty 52

Ontotext

DECEMBER 15, 2023

The official (first) repo is tensorflow/tensor2tensor that has topics: machine-learning reinforcement-learning deep-learning machine-translation tpu. By exploring the first topic machine-learning , we find 117k Github repos. The taxonomy integrates various data sources, offering a holistic view of AI innovation.

Machine Learning 69

Machine Learning Interactive Deep Learning Statistics 69

CIO Business Intelligence

FEBRUARY 10, 2023

Its flagship tool, Pipe Sleuth, uses an advanced, deep learning neural network model to do image analysis of small diameter sewer pipes, classify them, and then create a condition assessment report.

Predictive Analytics 139

Predictive Analytics Analytics Predictive Modeling Forecasting 139

Smart Data Collective

APRIL 25, 2022

Artificial intelligence can use deep learning as a means to create automatically generate content for their social media page. Employees will no longer need to worry about carrying out the tasks that they will have had to do previously in an effort to build on their business’ social presence. The Creation of Content on Social Media.

Management 131

Management Machine Learning Marketing Deep Learning 131

IBM Big Data Hub

AUGUST 28, 2023

Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g., The data collection process should be tailored to the specific objectives of the analysis.

Data mining 54

Data mining Machine Learning Deep Learning Marketing 54

Smart Data Collective

MAY 8, 2022

Let’s not forget that big data and AI can also automate about 80% of the physical work required from human beings, 70% of the data processing, and more than 60% of the data collection tasks. From the statistics shown, this means that both AI and big data have the potential to affect how we work in the workplace.

Big Data 101

Big Data Data Analytics Analytics Cost-Benefit 101

Decision Management Solutions

FEBRUARY 8, 2021

The goal is to establish the Mayflower 400 as an open platform for marine research that would reduce costs and ease the burden on scientists and sailors, who have to brave a dangerous and unpredictable environment in the course of their data-collecting missions.

Machine Learning 93

Machine Learning Testing Deep Learning Optimization 93

O'Reilly on Data

JUNE 18, 2019

Even if we boosted the quality of the available data via unification and cleaning, it still might not be enough to power the even more complex analytics and predictions models (often built as a deep learning model). An important paradigm for solving both these problems is the concept of data programming.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

CIO Business Intelligence

MARCH 30, 2022

The company next plans to explore new technologies, such as deep learning, Baumhof says. So, in addition to incorporating new data into your decision intelligence strategy, rethinking the underlying algorithms can also help increase the quality of your results. Augment complex processes — especially for data collection.

Machine Learning 144

Machine Learning Risk Testing Data-driven 144

IBM Big Data Hub

SEPTEMBER 6, 2023

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. What is AI marketing?

Marketing 82

Marketing Technology Advertising ROI 82

DataRobot Blog

JULY 27, 2022

Here we will demonstrate an end-to-end unattended workflow that: trains a new model on the Fashion MNIST Dataset uploads it to an Algorithmia Data Collection creates a new algorithm on Algorithmia creates DataRobot deployment Links everything together via the MLOps Agent. The Demo: Autoscaling with MLOps.

Metrics 52

Metrics Deep Learning Modeling Machine Learning 52

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. Marketing and sales: Conversational AI has become an invaluable tool for data collection. It assists customers and gathers crucial customer data during interactions to convert potential customers into active ones.

Enterprise 101

Enterprise Interactive Data-driven Key Performance Indicator 101

Cloudera

FEBRUARY 16, 2022

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. So, we have workspaces, projects and sessions in that order.

Machine Learning 84

Machine Learning Modeling Metadata Recreation/Entertainment 84

O'Reilly on Data

MARCH 31, 2020

That foundation means that you have already shifted the culture and data infrastructure of your company. If you’re just learning to walk, there are ways to speed up your progress. Here are some great places to start: “ Rules of Machine Learning: Best Practices for ML Engineering” (Google). Insight Fellows Data PM Program.

Management 332

Management Machine Learning Experimentation Metrics 332

IBM Big Data Hub

FEBRUARY 14, 2024

Financial services AI-powered FinOps (Finance + DevOps) helps financial institutions operationalize data-driven cloud spend decisions to safely balance cost and performance in order to minimize alert fatigue and wasted budget. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

Cost-Benefit 85

Cost-Benefit Insurance Unstructured Data Machine Learning 85

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 166

Analytics Machine Learning Deep Learning Data Science 166

Rocket-Powered Data Science

JULY 6, 2021

The Edge-to-Cloud architectures are responding to the growth of IoT sensors and devices everywhere, whose deployments are boosted by 5G capabilities that are now helping to significantly reduce data-to-action latency. 7) Deep learning (DL) may not be “the one algorithm to dominate all others” after all.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

O'Reilly on Data

MARCH 18, 2020

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. Supervised learning is dominant, deep learning continues to rise. AI tools organizations are using.

Enterprise 309

Enterprise Deep Learning Data Governance Risk 309

IBM Big Data Hub

MAY 17, 2023

They use drones for tasks as simple as aerial photography or as complex as sophisticated data collection and processing. It can offer data on demand to different business units within an organization, with the help of various sensors and payloads. The global commercial drone market is projected to grow from USD 8.15

Data Collection 79

Data Collection Visualization Data-driven Unstructured Data 79

Insight

JANUARY 14, 2020

In short, I was faced with two major difficulties regarding data collection: I didn’t have nearly enough images, and the images I did have were not representative of a realistic gym environment. On top of this issue, I only had between 40–50 unique images for each class. Check out the Keras documentation here.

Metrics 58

Metrics Optimization Modeling Testing 58

Cloudera

NOVEMBER 19, 2020

Analyzing historical data is an important strategy for anomaly detection. The modeling process begins with data collection. Here, Cloudera Data Flow is leveraged to build a streaming pipeline which enables the collection, movement, curation, and augmentation of raw data feeds.

Machine Learning 118

Machine Learning Data-driven Modeling Deep Learning 118

IBM Big Data Hub

NOVEMBER 14, 2023

Data products and data mesh Data products are assembled data from sources that can serve a set of functional needs that can be packaged into a consumable unit. Each data product has its own lifecycle environment where its data and AI assets are managed in their product-specific data lakehouse.

Strategy 108

Strategy Cost-Benefit Data-driven Measurement 108

Cloudera

OCTOBER 1, 2022

While the word “data” has been common since the 1940s, managing data’s growth, current use, and regulation is a relatively new frontier. . Governments and enterprises are working hard today to figure out the structures and regulations needed around data collection and use.

Data Governance 74

Data Governance Strategy Enterprise Machine Learning 74

Cloudera

FEBRUARY 4, 2019

With the rise of streaming architectures and digital transformation initiatives everywhere, enterprises are struggling to find comprehensive tools for data management to handle high volumes of high-velocity streaming data. He currently works at Cloudera, managing their Data-in-Motion product line.

IoT 73

IoT Prescriptive Analytics Digital Transformation Internet of Things 73

Domino Data Lab

OCTOBER 9, 2019

We’ll examine National Oceanic and Atmospheric Administration (NOAA) data management practices which I learned about at their workshop, as a case study in how to handle data collection, dataset stewardship, quality control, analytics, and accountability when the stakes are especially high. How cool is that?!

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

Domino Data Lab

JULY 22, 2019

We’ve got this complex landscape, tons of data sharing, an economy of data, external data, tons of mobile devices. and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries. You can take TensorFlow.js

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

MARCH 3, 2019

Then, when we received 11,400 responses, the next step became obvious to a duo of data scientists on the receiving end of that data collection. Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. One-fifth use reinforcement learning.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Machine learning model interpretability. training data”) show the tangible outcomes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105