IBM Big Data Hub

APRIL 15, 2024

First and foremost, we must reconsider our approach to initiatives, moving away from a solely awareness-focused, compliance-driven model. It’s crucial to establish a comprehensive set of metrics capable of measuring risk reduction and overall program success. This approach requires a shift, but how do we accomplish it?

Metrics 97

Metrics Measurement Risk Data Processing 97

CIO Business Intelligence

MARCH 18, 2024

We are proceeding cautiously because the rise of LLMs [large language models] presents a new level of data security risk,” he says. “We We have been developing our own internal AI capability over the last few years using open-source models. Our data team uses gen AI on Amazon cloud to explore sustainability metrics.

Risk 133

Risk Cost-Benefit Data Processing Testing 133

Cloudera

JUNE 26, 2023

For data engineering and data science teams, CDSW is highly effective as a comprehensive platform that trains, develops, and deploys machine learning models. It can provide a complete solution for data exploration, data analysis, data visualization, viz applications, and model deployment at scale.

Testing 75

Testing Data Processing Visualization Data Science 75

AWS Big Data

AUGUST 21, 2023

For the demo, we’re using the Amazon Titan foundation model hosted on Amazon Bedrock for embeddings, with no fine tuning. It similarly codes the query as a vector and then uses a distance metric to find nearby vectors in the multi-dimensional space. The algorithm for finding nearby vectors is called kNN (k Nearest Neighbors).

Data Processing 90

Data Processing Experimentation Visualization Metrics 90

AWS Big Data

MARCH 13, 2024

For building any generative AI application, enriching the large language models (LLMs) with new data is imperative. Each service implements k-nearest neighbor (k-NN) or approximate nearest neighbor (ANN) algorithms and distance metrics to calculate similarity. This is where the Retrieval Augmented Generation (RAG) technique comes in.

Data Processing 110

Data Processing Dashboards Machine Learning Management 110

IBM Big Data Hub

OCTOBER 16, 2023

They differ from virtual machines in that they leverage the features and resources of the host OS versus requiring a guest OS in every instance. Containerization helps DevOps teams avoid the complications that arise when moving software from testing to production.

Data Processing 80

Data Processing Dashboards Metrics Software 80

CIO Business Intelligence

NOVEMBER 29, 2023

If you choose not to use a cloud provider’s native services in order to remain agnostic, you lose many of the ‘better, cheaper, faster’ business case metrics,” says Holcombe. Most of the time, those CIOs decide to keep the multi-cloud model because they’re locked in. You’re trapped, big time,” says Nag. Adopt a hybrid cloud approach.

Recreation/Entertainment 124

Recreation/Entertainment Testing Strategy Risk 124

Cloudera

JULY 24, 2023

As a result, the platform development team needs to test many different combinations to ultimately identify the right major / minor version of each project that properly integrates with the rest of the custom distribution. data engineering pipelines, machine learning models).

Analytics 65

Analytics Testing Metrics Management 65

O'Reilly on Data

MARCH 31, 2020

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. For machine learning systems used in consumer internet companies, models are often continuously retrained many times a day using billions of entirely new input-output pairs.

Management 332

Management Machine Learning Experimentation Metrics 332

datapine

APRIL 15, 2020

Your Chance: Want to test an agile business intelligence solution? Business intelligence is moving away from the traditional engineering model: analysis, design, construction, testing, and implementation. In the traditional model communication between developers and business users is not a priority. Finalize testing.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

AWS Big Data

JUNE 14, 2023

This post focuses on how to properly size your MSK tiered storage cluster, which metrics to monitor, and the best practices to consider when running a production workload. Therefore, we model these three different consumer groups in the equation to account for their impact on infrastructure usage.

Metrics 81

Metrics Cost-Benefit Testing Optimization 81

CIO Business Intelligence

MARCH 13, 2024

We did side-by-side testing,” he says. AI models are able to present best practice code systems to help junior developers learn and hone their skills.” In testing, gen AI was also particularly good at generating test cases and creating dummy data for testing. It’s not about reducing headcount, he adds.

IT 128

IT Consulting Testing Cost-Benefit 128

Cloudera

SEPTEMBER 13, 2021

Cruise Control is integrated with Kafka through metrics reporting. In CDP it connects to Cloudera Manager’s time series database to fetch metrics. Metrics Reporting. This is a pluggable component that fetches and stores Kafka metrics. The open source version of Cruise Control stores metrics back to Kafka or Prometheus.

Metrics 71

Metrics Data Processing Optimization Testing 71

DataRobot

JUNE 29, 2021

Well, just imagine a production machine learning model that always stays accurate after it’s deployed—all by itself. Machine learning models trained on 2019 data didn’t know what to do. Continuous AI not only retrains your current production models for you. Why do I think this? Sounds pretty cool, right? Over and over.

Machine Learning 80

Machine Learning Data Processing Forecasting Modeling 80

datapine

JUNE 18, 2019

We live in a data-driven age, and the ability to use financial insights and metrics to your advantage will set you apart from the pack. Our monthly reports are on top illustrated with beautiful data visualizations that provide a better understanding of the metrics tracked. The reporting tools to do that exist for that very purpose.

Reporting 251

Reporting Metrics KPI Finance 251

CIO Business Intelligence

SEPTEMBER 22, 2022

Seeing that remote working continues to be a pressing issue still finding its footing after nearly three years in beta testing, the work surrounding feasible solutions seems to compound as time goes on, with some intending a full return to office while others have forged the company future on remote models.

Uncertainty 111

Uncertainty Forecasting Visualization Big Data 111

DataRobot

JUNE 10, 2021

To foster innovation in this area, AICrowd hosted a competition to predict the olfactory properties of a molecule. DataRobot’s AutoML uses different feature engineering techniques and a variety of machine learning algorithms to identify the best model for multilabel classification. With DataRobot this is a one-click step.

Metrics 52

Metrics Machine Learning Visualization Experimentation 52

AWS Big Data

FEBRUARY 26, 2024

When you start the process of designing your data model for Amazon Keyspaces, it’s essential to possess a comprehensive understanding of your access patterns, similar to the approach used in other NoSQL databases. Now that all components have been deployed, we can test the solution and conduct various searches on the OpenSearch Service index.

Dashboards 62

Dashboards Testing Metrics Optimization 62

AWS Big Data

JANUARY 9, 2024

Traditional lexical search, based on term frequency models like BM25, is widely used and effective for many search applications. Semantic search In semantic search, the search engine uses an ML model to encode text or other media (such as images and videos) from the source documents as a dense vector in a high-dimensional vector space.

Cost-Benefit 91

Cost-Benefit Visualization Modeling Machine Learning 91

AWS Big Data

OCTOBER 11, 2023

Near-real-time streaming analytics captures the value of operational data and metrics to provide new insights to create business opportunities. These metrics help agents improve their call handle time and also reallocate agents across organizations to handle pending calls in the queue. We use two datasets in this post.

Management 104

Management Metadata Analytics Dashboards 104

Occam's Razor

SEPTEMBER 15, 2011

Winners, well before they think data or tool, have a well structured Digital Marketing & Measurement Model. This article guides you in understanding the value of the Digital Marketing & Measurement Model (notice the repeated emphasis on Marketing, not just Measurement), and how to create one for yourself. Here’s a great test.

Measurement 166

Measurement Marketing Modeling Key Performance Indicator 166

CIO Business Intelligence

AUGUST 15, 2023

Shamim Mohammad, CIO, CarMax CarMax That volume created a Sisyphean task for the company’s content writers, as they struggled to provide up-to-date information by make, model, and year for each vehicle in the company’s constantly changing inventory. And it yields multiple business metric improvements, such as limiting surplus inventory.

IT 98

IT Manufacturing IoT Cost-Benefit 98

AWS Big Data

JULY 26, 2023

The vector engine supports the popular distance metrics such as Euclidean, cosine similarity, and dot product, and can accommodate 16,000 dimensions, making it well-suited to support a wide range of foundational and other AI/ML models. You can choose to host your collection on a public endpoint or within a VPC.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

AWS Big Data

MAY 2, 2023

If your updates to a dataset triggers multiple subsequent DAGs, then you can use the Airflow metric max_active_tasks_per_dag to control the parallelism of the consumer DAG and reduce the chance of overloading the system. The workflow steps are as follows: The producer DAG makes an API call to a publicly hosted API to retrieve data.

Testing 100

Testing Experimentation Management Metadata 100

The Unofficial Google Data Science Blog

JULY 17, 2018

Alternatively, guidance and insight may be delivered below the executive level to product managers and engineering leads, directing product feature development via metrics and A/B experiments. This measure of web page value is on a meaningful linear scale, such that our freshness metric (a weighted average) has an intuitive interpretation.

Data Science 85

Data Science Snapshot Data Processing Optimization 85

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Because data analysts often build machine learning models, programming and AI knowledge are also valuable. It’s also necessary to understand data cleaning and processing techniques.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

The Unofficial Google Data Science Blog

JULY 18, 2023

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. We will follow the example of Janson and Olsson , and start from this generalized definition of the metric, which they call iota.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

AWS Big Data

DECEMBER 21, 2023

smava follows the data vault modeling methodology with the Raw Vault, Business Vault, and Data Mart stages to prepare the data products for end consumers. Evolution of the data platform requirements smava started with a single Redshift cluster to host all three data stages.

Data Lake 88

Data Lake Data Warehouse Data-driven B2B 88

AWS Big Data

NOVEMBER 16, 2023

As with all AWS services, Amazon Redshift is a customer-obsessed service that recognizes there isn’t a one-size-fits-all for customers when it comes to data models, which is why Amazon Redshift supports multiple data models such as Star Schemas, Snowflake Schemas and Data Vault.

Enterprise 83

Enterprise Data Warehouse Snapshot Cost-Benefit 83

CIO Business Intelligence

OCTOBER 13, 2022

Hosting the entire infrastructure on-premise will turn out to be exorbitant,” he says. This is followed by an end-to-end testing upgrading from component,” Kumar says. “If At the end of that sprint, users would have a UI/UX for testing and it would be open to any changes if required instead of being there till the end of the project.”.

ROI 132

ROI IT Sales Consulting 132

O'Reilly on Data

APRIL 22, 2020

For example, payday lending businesses are no doubt compliant with the law, but many aren’t models for good corporate citizenship. There aren’t simple standards and tests for ethical behavior, nor are you as likely to be called into court for acting unethically. Ethics is much more slippery.

Cost-Benefit 249

Cost-Benefit Finance Strategy Data-driven 249

CIO Business Intelligence

JUNE 8, 2022

The coming months are a leadership test for CIOs, and it’s a pass/fail grade.”. Demand transparency on everything — especially on metrics — and be transparent back. says the multinational food manufacturer hosts internal summits to show colleagues how advanced data and analytics can help drive growth. . Keep calm and lead on.

IT 133

IT Cost-Benefit Uncertainty Digital Transformation 133

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. 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. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

AWS Big Data

JUNE 15, 2023

With automated data preparation in QuickSight Q, the model will do a lot of the topic setup for you, but there is some context that is specific to your business that you need to provide. For example, the AWS Analytics sales leadership team uses QuickSight and Q to track key metrics for their region as part of their monthly business review.

Sales 66

Sales Dashboards Visualization Testing 66

O'Reilly on Data

JANUARY 25, 2021

Both SRE and DevOps emphasize similar practices: version control (62% growth for GitHub, and 48% for Git), testing (high usage, though no year-over-year growth), continuous deployment (down 20%), monitoring (up 9%), and observability (up 128%). It’s particularly difficult if testing includes issues like fairness and bias.

Machine Learning 363

Machine Learning Software Testing Technology 363

Occam's Razor

MAY 22, 2006

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers.

Experimentation 103

Experimentation Testing Optimization Measurement 103

Insight

MARCH 25, 2020

After reading this, I hope you can learn how to build deep learning models using TensorFlow Keras, productionalize the model as a Streamlit app, and deploy it as a Docker container on Google Cloud Platform (GCP) using Google Kubernetes Engines (GKE).

Deep Learning 53

Deep Learning Metrics Data Processing Interactive 53