Smart Data Collective

NOVEMBER 9, 2022

There are a number of great applications of machine learning. The main purpose of machine learning is to partially or completely replace manual testing. Machine learning makes it possible to fully automate the work of testers in carrying out complex analytical processes. Top ML Companies.

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Machine Learning Testing Cost-Benefit Data-driven 96

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 machine learning? This post will dive deeper into the nuances of each field.

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Machine Learning Data Science Statistics Deep Learning 55

Smart Data Collective

FEBRUARY 21, 2021

Deep learning tech is influencing and enhancing many industries, promising to provide insights into key business operations which were not previously possible to unearth. One of the biggest applications of this technology lies with using deep learning to streamline fleet management. Route adjustments made in real time.

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Deep Learning Management Cost-Benefit Manufacturing 97

Smart Data Collective

JULY 8, 2021

Let’s talk about some benefits and risks of artificial intelligence. Artificial Intelligence employs machine learning algorithms such as Deep Learning and neural networks to learn new information like humans. It eliminates the requirement for feeding new codes every time we want them to learn a new thing.

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Risk Cost-Benefit Machine Learning Deep Learning 141

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

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. A more general approach is to learn a Generalized Additive Model (GAM).

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Machine Learning Deep Learning Modeling Interactive 108

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029.

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Machine Learning Marketing Forecasting Modeling 49

IBM Big Data Hub

SEPTEMBER 27, 2023

Large language models (LLMs) are foundation models that use artificial intelligence (AI), deep learning and massive data sets, including websites, articles and books, to generate text, translate between languages and write many types of content. All this reduces the risk of a data leak or unauthorized access.

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Risk Modeling Cost-Benefit Enterprise 93

CIO Business Intelligence

OCTOBER 13, 2023

Machine learning engineer Machine learning engineers are tasked with transforming business needs into clearly scoped machine learning projects, along with guiding the design and implementation of machine learning solutions.

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Deep Learning Machine Learning Modeling Software 142

O'Reilly on Data

DECEMBER 10, 2019

Roughly a year ago, we wrote “ What machine learning means for software development.” Karpathy suggests something radically different: with machine learning, we can stop thinking of programming as writing a step of instructions in a programming language like C or Java or Python. Instead, we can program by example.

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Software Machine Learning Risk Data-driven 259

CIO Business Intelligence

MAY 14, 2024

Like many public health agencies across the US, the King County Medical Examiner’s Office tracks drug overdose deaths to target interventions for populations at risk and save lives. The ML models include classic ML and deep learning to predict category labels from the narrative text in reports.

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Deep Learning Machine Learning Reporting Experimentation 73

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Validating Machine Learning Models. This should make intuitive sense, as individuals with more financial reserves would pose the institution with a lower credit risk compared to those with less.

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Risk Modeling Metrics Business Objectives 52

Domino Data Lab

MARCH 6, 2019

Niels Kasch , cofounder of Miner & Kasch , an AI and Data Science consulting firm, provides insight from a deep learning session that occurred at the Maryland Data Science Conference. Deep Learning on Imagery and Text. Introduction. And yeah … Miner & Kasch was founded by UMBC alumni.

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Deep Learning Data Science Machine Learning Modeling 55

O'Reilly on Data

FEBRUARY 11, 2019

Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning. In a forthcoming survey, “Evolving Data Infrastructure,” we found strong interest in machine learning (ML) among respondents across geographic regions. Deep Learning.

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Big Data Technology Machine Learning Deep Learning 207

Laminar Security

DECEMBER 13, 2023

Close to 70% of respondents in an ISC report indicated that they believe their organization lacks requisite cybersecurity staff to handle cloud data risk effectively. Learn in this article how Laminar harnesses AI for data discovery and classification and reduces public cloud data risks.

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Data-driven Machine Learning Risk Deep Learning 119

CIO Business Intelligence

JULY 5, 2023

Artificial intelligence and machine learning are the No. Generative AI is raising the interest level even further as organizations begin testing different use cases for deep-learning models. AI is really doing the heavy lift, in terms of identifying risks,” Crowley says.

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Machine Learning Deep Learning Consulting Enterprise 78

O'Reilly on Data

JANUARY 7, 2020

In fact, in our 2019 surveys, more than half of the respondents said AI (deep learning, specifically) will be part of their future projects and products—and a majority of companies are starting to adopt machine learning. To stay competitive, data scientists need to at least dabble in machine and deep learning.

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Deep Learning Machine Learning Experimentation Modeling 177

IBM Big Data Hub

DECEMBER 15, 2023

While open-source AI offers enticing possibilities, its free accessibility poses risks that organizations must navigate carefully. Enterprises may expose their stakeholders to risk when they use technologies that they didn’t build in-house. Morgan’s Athena uses Python-based open-source AI to innovate risk management.

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Deep Learning Experimentation Machine Learning Risk 66

CIO Business Intelligence

OCTOBER 4, 2023

Certified Information Systems Auditor (CISA); PMI Program, Portfolio, and Risk Management Professionals (PgMP, PfMP and PMI-RMP); Six Sigma Black Belt and Master Black Belt; Certified in Governance, Risk, and Compliance (ISC2); and Certified in Risk and Information Systems Control (CRISC) also drew large premiums.

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Prescriptive Analytics IT Machine Learning Risk 120

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.

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Metrics Machine Learning Risk Statistics 82

O'Reilly on Data

SEPTEMBER 12, 2018

Watch highlights from expert talks covering data science, machine learning, algorithmic accountability, and more. Preserving privacy and security in machine learning. Ben Lorica offers an overview of recent tools for building privacy-preserving and secure machine learning products and services. Watch " Wait.

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IoT Machine Learning Deep Learning Data Science 140

Smart Data Collective

JUNE 4, 2021

Basics of Machine Learning. Machine learning is the science of building models automatically. Whereas in machine learning, the algorithm understands the data and creates the logic. Whereas in machine learning, the algorithm understands the data and creates the logic. Semi-Supervised Learning.

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Data Science Statistics Deep Learning Machine Learning 104

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 John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing.

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Machine Learning Statistics Data Quality Modeling 343

CIO Business Intelligence

MARCH 13, 2024

L’analisi dei dati attraverso l’apprendimento automatico (machine learning, deep learning, reti neurali) è la tecnologia maggiormente utilizzata dalle grandi imprese che utilizzano l’IA (51,9%). Le reti neurali sono il modello di machine learning più utilizzato oggi.

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Machine Learning Deep Learning Big Data Testing 95

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.

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Management Machine Learning Experimentation Metrics 327

IBM Big Data Hub

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AutoML tools: Automated machine learning, or autoML, supports faster model creation with low-code and no-code functionality.

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Machine Learning Manufacturing Cost-Benefit Deep Learning 73

CIO Business Intelligence

APRIL 15, 2022

And 20% of IT leaders say machine learning/artificial intelligence will drive the most IT investment. Insights gained from analytics and actions driven by machine learning algorithms can give organizations a competitive advantage, but mistakes can be costly in terms of reputation, revenue, or even lives.

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Analytics Machine Learning Insurance Testing 141

CIO Business Intelligence

JANUARY 13, 2023

Software-based advanced analytics — including big data, machine learning, behavior analytics, deep learning and, eventually, artificial intelligence. Worst case, they let security teams limit the damage of a successful attack to something determined to be an acceptable level of risk. Why is this so important?

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Software Machine Learning Behavioral Analytics Risk 126

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Why aren’t traditional software tools sufficient?

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Modeling Machine Learning Testing Metrics 193

IBM Big Data Hub

JULY 11, 2023

Better machine learning (ML) algorithms, more access to data, cheaper hardware and the availability of 5G have contributed to the increasing application of AI in the healthcare industry, accelerating the pace of change. US, German and French researchers used deep learning on more than 100,000 images to identify skin cancer.

Deep Learning 55

Deep Learning Cost-Benefit Insurance Machine Learning 55

Analytics Vidhya

JANUARY 30, 2024

Introduction Large Language Models (LLMs) have revolutionized the field of natural language processing, enabling machines to generate human-like text and engage in conversations. Jailbreaking and exploiting weaknesses in LLMs pose significant risks, such as misinformation generation, offensive outputs, and privacy concerns.

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Risk Modeling Analytics Deep Learning 263

CIO Business Intelligence

JUNE 2, 2022

Among the hot technologies, artificial intelligence and machine learning — a subset of AI that that makes more accurate forecasts and analysis as it ingests data — continue to be of high interest as banks keep a strong focus on costs while trying to boost customer experience and revenue.

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Predictive Analytics Technology Analytics Machine Learning 137

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data poisoning attacks. Watermark attacks.

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Modeling Machine Learning Predictive Modeling Consulting 219

Corinium

JUNE 6, 2019

Regulations and compliance requirements, especially around pricing, risk selection, etc., A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI.

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Insurance Analytics Forecasting Deep Learning 250

CIO Business Intelligence

SEPTEMBER 19, 2023

AI personalization utilizes data, customer engagement, deep learning, natural language processing, machine learning, and more to curate highly tailored experiences to end-users and customers.

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Enterprise Cost-Benefit Optimization Deep Learning 139

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?

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Data Science Machine Learning Cost-Benefit Deep Learning 54

Analytics Vidhya

MARCH 4, 2024

This landmark decision reflects the government’s proactive approach to addressing rising concerns surrounding the potential risks and ethical considerations associated with AI technology.

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Modeling Risk Technology Analytics 228

CIO Business Intelligence

MARCH 30, 2022

For organizations looking to move beyond stale reports, decision intelligence holds promise, giving them the ability to process large amounts of data with a sophisticated mix of tools such as artificial intelligence and machine learning to transform data dashboards and business analytics into more comprehensive decision support platforms.

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Machine Learning Testing Risk Data-driven 142