Analytics Vidhya

FEBRUARY 3, 2022

This article will provide you with a hands-on implementation on how to deploy an ML model in the Azure cloud. If you are new to Azure machine learning, I would recommend you to go through the Microsoft documentation that has been provided in the […].

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Modeling Machine Learning Data Science Publishing 304

Analytics Vidhya

SEPTEMBER 4, 2021

This article was published as a part of the Data Science Blogathon Introduction Building a cool machine learning project is one thing, and another when you need other people to see it too. Sure, you can put the entire project on GitHub, but how will your grandparents figure out what you’ve done? No, we need to […].

Modeling 300

Modeling Machine Learning Data Science Publishing 300

Analytics Vidhya

MAY 31, 2022

Introduction on Amazon Sagemaker Amazon Sagemaker is arguably the most powerful, feature-rich, and fully managed machine learning service developed by Amazon. From creating your own labeled datasets to deploying and monitoring the models on production, Sagemaker is equipped to do everything. It can also […].

Machine Learning 266

Machine Learning Data Science Publishing Modeling 266

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.

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Strategy Experimentation Uncertainty Machine Learning 289

Analytics Vidhya

NOVEMBER 1, 2021

This article was published as a part of the Data Science Blogathon Introduction A machine learning model is intended to solve a real-world problem and the solution as a service must reach the consumer who can use it easily. This is the essence of putting your model into production.

Machine Learning 225

Machine Learning Modeling Data Science Publishing 225

Analytics Vidhya

OCTOBER 6, 2019

Overview What are the next steps after you’ve deployed your machine learning model? Post-deployment monitoring is a crucial step in any machine learning project. The post Deployed your Machine Learning Model?

Machine Learning 263

Machine Learning Modeling Analytics Data Science 263

O'Reilly on Data

JUNE 9, 2020

The biggest problem facing machine learning today isn’t the need for better algorithms; it isn’t the need for more computing power to train models; it isn’t even the need for more skilled practitioners. It’s getting machine learning from the researcher’s laptop to production.

Machine Learning 254

Machine Learning Modeling Metrics IT 254

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 275

Modeling Machine Learning Risk Advertising 275

Analytics Vidhya

APRIL 15, 2021

HalGatewood.com on Unsplash Prerequisites: Basic machine learning (ML) and basic. The post Easily Deploy Your Machine Learning Model into a Web App Using Netlify appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon.

Machine Learning 184

Machine Learning Modeling Data Science Publishing 184

Cloudera

NOVEMBER 29, 2023

In the dynamic world of machine learning operations (MLOps), staying ahead of the curve is essential. That’s why we’re excited to announce the Cloudera Model Registry as generally available, a game-changer that’s set to transform the way you manage your machine learning models in production environments.

Modeling 84

Modeling Machine Learning Metadata Metrics 84

IBM Big Data Hub

APRIL 9, 2024

Preparing and annotating data IBM watsonx.data helps organizations put their data to work, curating and preparing data for use in AI models and applications. ” Watsonx.data uses machine learning (ML) applications to simulate data that represents ball positioning projections. ” Watsonx.ai ” Watsonx.ai

Management 86

Management IT Machine Learning Metrics 86

O'Reilly on Data

AUGUST 18, 2020

So you need to redesign your company’s data infrastructure. That is, products that are laser-focused on one aspect of the data science and machine learning workflows, in contrast to all-in-one platforms that attempt to solve the entire space of data workflows. Lessons Learned from Data Warehouse and Data Engineering Platforms.

Data Science 355

Data Science Machine Learning Data Warehouse Deep Learning 355

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). A lot to learn, but worthwhile to access the unique and special value AI can create in the product space.

Management 332

Management Machine Learning Experimentation Metrics 332

Cloudera

MARCH 26, 2024

How do you adapt a foundational model to your specific needs? Cloudera: Your Trusted Partner in AI With over 25 Exabytes of Data Under Management and hundreds of customers leveraging our platform for Machine Learning, Cloudera has a long and successful history as an industry leader.

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Cost-Benefit Machine Learning Enterprise Modeling 80

AWS Big Data

JANUARY 17, 2024

it’s now effortless to integrate with AI/ML models to power semantic search and other use cases. To use neural search, you must set up an ML model. In this post, we demonstrate how to configure AI/ML connectors to external models through the OpenSearch Service console. Starting with version 2.9

Dashboards 96

Dashboards Data Processing Modeling Machine Learning 96

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps is a term that refers to the set of practices and tools that organizations use to improve the quality and speed of data analytics and machine learning. This can help organizations to build trust in their data-related workflows, and to drive better outcomes from their data analytics and machine learning initiatives.

Machine Learning 130

Machine Learning Data-driven Optimization Modeling 130

O'Reilly on Data

JUNE 27, 2023

Does your company plan to release an AI chatbot, similar to OpenAI’s ChatGPT or Google’s Bard? Doing so means giving the general public a freeform text box for interacting with your AI model. Welcome to your company’s new AI risk management nightmare. But first, let’s dig deeper into the problem.

Risk Management 224

Risk Management Risk Management Modeling 224

CIO Business Intelligence

MARCH 29, 2024

So too is keeping your options open. That’s why Rocket Mortgage has been a vigorous implementor of machine learning and AI technologies — and why CIO Brian Woodring emphasizes a “human in the loop” AI strategy that will not be pinned down to any one generative AI model. The rest are on premises.

Data Lake 114

Data Lake Machine Learning Data Warehouse Unstructured Data 114

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 120

Machine Learning Modeling Experimentation IT 120

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.

Software 261

Software Machine Learning Risk Data-driven 261

IBM Big Data Hub

MAY 10, 2024

Underpinning most artificial intelligence (AI) deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain. Deep learning requires a tremendous amount of computing power.

Deep Learning 111

Deep Learning Cost-Benefit Manufacturing Optimization 111

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Identifying the problem.

Marketing 362

Marketing Experimentation Metrics Testing 362

Cloudera

NOVEMBER 1, 2023

Elevate your AI applications with our latest applied ML prototype At Cloudera, we continuously strive to empower organizations to unlock the full potential of their data, catalyzing innovation and driving actionable insights. High-level overview of real-time data ingest with Cloudera DataFlow to Pinecone vector database.

Machine Learning 105

Machine Learning Optimization Modeling Interactive 105

DataRobot Blog

DECEMBER 6, 2022

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

AWS Big Data

NOVEMBER 1, 2023

Amazon Redshift ML allows data analysts, developers, and data scientists to train machine learning (ML) models using SQL. In previous posts, we demonstrated how you can use the automatic model training capability of Redshift ML to train classification and regression models.

Modeling 96

Modeling Data Warehouse Machine Learning Recreation/Entertainment 96

CIO Business Intelligence

APRIL 25, 2024

Of the 750 CIOs around the world surveyed by Lenovo, 81% said they are already leveraging third-party AI Tools or deploying a mix of third-party and proprietary AI. Protecting sensitive data and ensuring the integrity of AI models against cyber threats, such as adversarial attacks, are key concerns for CIOs,” he said.

ROI 120

ROI Cost-Benefit Machine Learning Technology 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.

Metrics 88

Metrics Machine Learning Risk Statistics 88

AWS Big Data

MARCH 21, 2024

However, it can be challenging to set up a Kafka cluster along with other data processing components that scale automatically depending on your application’s needs. In this post, we explain how you can use some of these services, including MSK Serverless , to build a serverless data platform to meet your real-time needs.

Data Lake 96

Data Lake Management Modeling Optimization 96

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 Risk 145

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 110

Deep Learning Machine Learning Modeling Reporting 110

CIO Business Intelligence

MARCH 6, 2024

Marrying machine learning with crowdsourced telemetry and passive identification technology enables organizations to rapidly assess and score risk for everything and everyone that you can now see. The first is the ability to get to ROI faster. To learn more, visit us here.

ROI 119

ROI Manufacturing Cost-Benefit IoT 119

CIO Business Intelligence

MAY 12, 2022

But sometimes can often be more than enough if the prediction can help your enterprise plan better, spend more wisely, and deliver more prescient service for your customers. Working with dedicated predictive analytics tools is often relatively easy, at least compared to programming your own tools from scratch. Are they right?

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

CIO Business Intelligence

FEBRUARY 14, 2023

To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. DataStax Real-time data and decisioning First, a few quick definitions.

Data Strategy 109

Data Strategy Strategy Machine Learning Data-driven 109

DataRobot Blog

FEBRUARY 28, 2023

While this can be an excellent strategy for a future-oriented company, it can prove futile if you don’t maximize the value of your investment. This includes: Supporting Snowflake External OAuth configuration Leveraging Snowpark for exploratory data analysis with DataRobot-hosted Notebooks and model scoring.

Data Processing 72

Data Processing Experimentation Machine Learning Modeling 72

DataRobot

NOVEMBER 16, 2021

Many organizations, including state and local governments, are dipping their toes into machine learning (ML) and artificial intelligence (AI). According to a recent study by NewVantage Partners, only 15 percent of organizations surveyed have deployed AI capabilities into widespread production. What is MLOps?

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Cost-Benefit Machine Learning Modeling Risk 98

IBM Big Data Hub

DECEMBER 20, 2023

By giving machines the growing capacity to learn, reason and make decisions, AI is impacting nearly every industry, from manufacturing to hospitality, healthcare and academia. Identify potential partners and vendors Find companies in the AI and ML space that have worked within your industry.

Strategy 79

Strategy Business Objectives Cost-Benefit Consulting 79

CIO Business Intelligence

FEBRUARY 9, 2024

The two new features, namely a testing center and the provision of prompt engineering suggestions, are the fruit of significant investment in the company’s AI engineering team, said Claire Cheng, vice president of machine learning and AI engineering at Salesforce.

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Testing Enterprise Modeling Machine Learning 116