Rocket-Powered Data Science

FEBRUARY 15, 2023

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”. Debugging AI Products.

Management 314

Management Machine Learning Metrics Modeling 314

Cloudera

NOVEMBER 13, 2024

Large Language Models (LLMs) will be at the core of many groundbreaking AI solutions for enterprise organizations. These enable customer service representatives to focus their time and attention on more high-value interactions, leading to a more cost-efficient service model. Increase Productivity.

Cost-Benefit 87

Cost-Benefit Data Processing Machine Learning Testing 87

Cloudera

DECEMBER 9, 2024

Were thrilled to announce the release of a new Cloudera Accelerator for Machine Learning (ML) Projects (AMP): Summarization with Gemini from Vertex AI . To showcase how easy it is to build an AI application using Cloudera AI and Google’s Vertex AI Model Garden.

Machine Learning 75

Machine Learning Modeling Testing Optimization 75

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.

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

Cloudera

DECEMBER 4, 2024

Today, Artificial Intelligence (AI) and Machine Learning (ML) are more crucial than ever for organizations to turn data into a competitive advantage. To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput.

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

AWS Big Data

DECEMBER 4, 2024

They’re taking data they’ve historically used for analytics or business reporting and putting it to work in machine learning (ML) models and AI-powered applications. SageMaker simplifies the discovery, governance, and collaboration for data and AI across your lakehouse, AI models, and applications.

Data Analytics 96

Data Analytics Analytics Data Lake Data Quality 96

O'Reilly on Data

MARCH 26, 2024

TL;DR LLMs and other GenAI models can reproduce significant chunks of training data. He first tried to do so by becoming Cervantes, learning Spanish, and forgetting all the history since Cervantes wrote Don Quixote , among other things, but then decided it would make more sense to (re)write the text as Menard himself.

Modeling 322

Modeling Machine Learning Risk Advertising 322

datapine

DECEMBER 11, 2019

SaaS is a software distribution model that offers a lot of agility and cost-effectiveness for companies, which is why it’s such a reliable option for numerous business models and industries. Learn what will enhance the SaaS infrastructure in our free cheat sheet! Exclusive Bonus Content: Get The Top 10 Saas Trends Handbook!

Software 314

Software Cost-Benefit Data-driven Data Processing 314

O'Reilly on Data

SEPTEMBER 15, 2021

There was a lot of uncertainty about stability, particularly at smaller companies: Would the company’s business model continue to be effective? Learning new skills and improving old ones were the most common reasons for training, though hireability and job security were also factors. Would your job still be there in a year?

Machine Learning 363

Machine Learning Statistics Reporting Consulting 363

DataKitchen

APRIL 26, 2021

In early April 2021, DataKItchen sat down with Jonathan Hodges, VP Data Management & Analytics, at Workiva ; Chuck Smith, VP of R&D Data Strategy at GlaxoSmithKline (GSK) ; and Chris Bergh, CEO and Head Chef at DataKitchen, to find out about their enterprise DataOps transformation journey, including key successes and lessons learned.

Measurement 246

Measurement Metrics Data-driven Dashboards 246

O'Reilly on Data

NOVEMBER 28, 2023

And everyone has opinions about how these language models and art generation programs are going to change the nature of work, usher in the singularity, or perhaps even doom the human race. 16% of respondents working with AI are using open source models. A few have even tried out Bard or Claude, or run LLaMA 1 on their laptop.

Enterprise 342

Enterprise Testing Modeling Reporting 342

Cloudera

JANUARY 7, 2025

As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience. Heres a deep dive into why and how enterprises master multi-cloud deployments to enhance their data and AI initiatives. The terms hybrid and multi-cloud are often used interchangeably.

Cost-Benefit 82

Cost-Benefit Optimization Data-driven Strategy 82

Cloudera

JANUARY 9, 2025

Thats why were moving from Cloudera Machine Learning to Cloudera AI. Why AI Matters More Than ML Machine learning (ML) is a crucial piece of the puzzle, but its just one piece. It means combining data engineering, model ops, governance, and collaboration in a single, streamlined environment. Ready to learn more?

Machine Learning 85

Machine Learning Enterprise Data-driven Modeling 85

AWS Big Data

DECEMBER 17, 2024

Today, Amazon Redshift is used by customers across all industries for a variety of use cases, including data warehouse migration and modernization, near real-time analytics, self-service analytics, data lake analytics, machine learning (ML), and data monetization.

Data Lake 92

Data Lake Data Warehouse Data-driven Optimization 92

AWS Big Data

DECEMBER 18, 2024

In the rapidly evolving landscape of AI-powered search, organizations are looking to integrate large language models (LLMs) and embedding models with Amazon OpenSearch Service. In this blog post, well dive into the various scenarios for how Cohere Rerank 3.5 Overview of Cohere Rerank 3.5 See Cohere Rerank 3.5

Metrics 87

Metrics Modeling Data Processing Machine Learning 87

AWS Big Data

OCTOBER 29, 2024

The CDH is used to create, discover, and consume data products through a central metadata catalog, while enforcing permission policies and tightly integrating data engineering, analytics, and machine learning services to streamline the user journey from data to insight.

Data Lake 100

Data Lake Sales Metadata Machine Learning 100

DataKitchen

AUGUST 3, 2021

This post (1 of 5) is the beginning of a series that explores the benefits and challenges of implementing a data mesh and reviews lessons learned from a pharmaceutical industry data mesh example. DDD divides a system or model into smaller subsystems called domains. But first, let’s define the data mesh design pattern. See the pattern?

Data Architecture 246

Data Architecture Data Lake Cost-Benefit Data Warehouse 246

DataKitchen

MARCH 11, 2021

DataKitchen Blog: Why Are There So Many *Ops Terms? Gartner: “ XOps practices link development, deployment and maintenance together to create a shared understanding of requirements, transfer of skills and processes for monitoring and maintaining analytics and AI artifacts (see Figure 2).”. See Figure 1. Opportunity. Implementation.

Analytics 130

Analytics Machine Learning Modeling Reporting 130

DataKitchen

JANUARY 2, 2021

ModelOps is “ at the core of an organization’s AI strategy ” and is “ focused on operationalizing AI models, including the full life cycle management of AI decision models and AI governance.” Blog: Deliver AI and ML Models at Scale with ModelOps. Blog: Improving Teamwork in Data Analytics with DataOps.

Testing 130

Testing Machine Learning Reporting Modeling 130

Domino Data Lab

AUGUST 6, 2021

In the world of machine learning (ML) and artificial intelligence (AI), governance is a lifelong pursuit. All models require testing and auditing throughout their deployment and, because models are continually learning, there is always an element of risk that they will drift from their original standards.

Machine Learning 145

Machine Learning Modeling Testing Data Science 145

Analytics Vidhya

FEBRUARY 9, 2023

Introduction Data Scientists have an important role in the modern machine-learning world. Leveraging ML pipelines can save them time, money, and effort and ensure that their models make accurate predictions and insights. Data scientists […] The post Why Data Scientists Should Adopt Machine Learning Pipelines?

Machine Learning 317

Machine Learning Data Science Modeling Analytics 317

Cloudera

NOVEMBER 19, 2021

We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. Organizations need to usher their ML models out of the lab (i.e., COPML accounts for the fact that true production machine learning (i.e.,

Machine Learning 122

Machine Learning Modeling Experimentation IT 122

Rocket-Powered Data Science

JULY 19, 2023

The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage. One could say that sentinel analytics is more like unsupervised machine learning, while precursor analytics is more like supervised machine learning.

Data-driven 188

Data-driven Enterprise Analytics Machine Learning 188

DataKitchen

NOVEMBER 29, 2021

Model developers will test for AI bias as part of their pre-deployment testing. Continuous testing, monitoring and observability will prevent biased models from deploying or continuing to operate. Many in the data industry recognize the serious impact of AI bias and seek to take active steps to mitigate it.

Testing 245

Testing Data Lake Data Architecture Manufacturing 245

Cloudera

NOVEMBER 17, 2020

One of the many areas where machine learning has made a large difference for enterprise business is in the ability to make accurate predictions in the realm of fraud detection. A well-tuned, accurate model can predict which are the false positives and reduce the follow-up costs and improve customer confidence dramatically.

Deep Learning 115

Deep Learning Machine Learning Modeling Reporting 115

CIO Business Intelligence

SEPTEMBER 29, 2023

These skills include expertise in areas such as text preprocessing, tokenization, topic modeling, stop word removal, text classification, keyword extraction, speech tagging, sentiment analysis, text generation, emotion analysis, language modeling, and much more.

Deep Learning 143

Deep Learning Machine Learning Consulting Modeling 143

DataKitchen

MAY 10, 2021

Advanced firms: Experiment, learn, and continuously improve the effectiveness of your IDB applications; leverage the power of machine learning (ML) to automate apps and processes to scale your IDB capabilities even further. Blog: What is DataOps ? White Paper: Launch Your DataOps Journey with the DataOps Maturity Model.

Data-driven 130

Data-driven Data Architecture Machine Learning Strategy 130

datapine

DECEMBER 4, 2019

The accuracy of the predictions depends on the data used to create the model. For instance, if a model is created based on the factors inherent at one company, it doesn’t necessarily apply at a second company. The same may be true about a model for one year compared to the next year within the same company.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Cloudera

JANUARY 6, 2021

Python is used extensively among Data Engineers and Data Scientists to solve all sorts of problems from ETL/ELT pipelines to building machine learning models. In this blog series, we will explain how to configure PySpark and HBase together for basic Spark use as well as for jobs maintained in CDSW. Restart Region Servers.

Machine Learning 133

Machine Learning Data Science Management Enterprise 133

DataKitchen

NOVEMBER 20, 2021

It considers whether a component is deployable , monitorable , maintainable, reusable, secure and adds value to the end-user or customer. They don’t have to understand how to deploy analytics into production – an automated QC and deployment orchestration performs that job. When analysts stay focused, it speeds up deployment.

Testing 246

Testing Machine Learning Data Warehouse Analytics 246

DataKitchen

OCTOBER 28, 2021

Machine learning (ML) models are computer programs that draw inferences from data — usually lots of data. One way to think of ML models is that they instantiate an algorithm (a decision-making procedure often involving math) in software and then, at relatively low cost, deploy it on a large scale. What Is AI Bias?

Testing 130

Testing IT Manufacturing Machine Learning 130

CIO Business Intelligence

MAY 6, 2022

Google Cloud Platform (GCP) is set to release two new solutions targeted at the manufacturing sector and aiming to ease data engineering and analytics tasks, unifying data from diverse machine assets to offer business insights to factory managers. Edge-cloud connection helps data extraction.

Manufacturing 140

Manufacturing Analytics Machine Learning IoT 140

CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

DataRobot

NOVEMBER 16, 2021

Many organizations, including state and local governments, are dipping their toes into machine learning (ML) and artificial intelligence (AI). As we’ve discussed in this blog series, some are already reaping the rewards of AI through increased productivity, cost savings, etc. Issues with Deployment. What is MLOps?

Cost-Benefit 98

Cost-Benefit Machine Learning Modeling Data Processing 98

Cloudera

JULY 27, 2021

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. We believe the best way to learn what a technology is capable of is to build things with it.

Data Science 117

Data Science Enterprise Experimentation Machine Learning 117

Cloudera

NOVEMBER 19, 2020

The Association of Certified Fraud Examiners reports the use of artificial intelligence and machine learning in anti-fraud programs is expected to almost triple in the next two years. Machine learning algorithms enable fraud detection systems to distinguish between legitimate and fraudulent behaviors.

Machine Learning 119

Machine Learning Data-driven Modeling Deep Learning 119