O'Reilly on Data

APRIL 2, 2019

Recall the following key attributes of a machine learning project: Unlike traditional software where the goal is to meet a functional specification , in ML the goal is to optimize a metric. We are still in the early days for tools supporting teams developing machine learning models. Model governance. there aren’t enough of them.

Machine Learning 271

Machine Learning Modeling Data Science Software 271

Analytics Vidhya

JUNE 14, 2024

Introduction Evaluation of models and medical tests is significant in both data science and medicine. However, these two domains use different metrics, which is confusing. When it comes to the relationship between these metrics, they differ.

Data Science 232

Data Science Metrics Testing Modeling 232

CIO Business Intelligence

JANUARY 14, 2025

Understanding and tracking the right software delivery metrics is essential to inform strategic decisions that drive continuous improvement. They achieve this through models, patterns, and peer review taking complex challenges and breaking them down into understandable components that stakeholders can grasp and discuss.

Enterprise 119

Enterprise Technology Metrics Measurement 119

O'Reilly on Data

JUNE 15, 2023

The world changed on November 30, 2022 as surely as it did on August 12, 1908 when the first Model T left the Ford assembly line. If we want prosocial outcomes, we need to design and report on the metrics that explicitly aim for those outcomes and measure the extent to which they have been achieved.

Metrics 360

Metrics Reporting Measurement Finance 360

datapine

SEPTEMBER 29, 2022

6) Data Quality Metrics Examples. Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. These needs are then quantified into data models for acquisition and delivery. Table of Contents. 1) What Is Data Quality Management?

Data Quality 241

Data Quality Metrics Data-driven Management 241

DataKitchen

APRIL 13, 2021

Testing and Data Observability. DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Testing and Data Observability.

Testing 300

Testing Machine Learning Consulting Data Science 300

CIO Business Intelligence

NOVEMBER 19, 2024

From obscurity to ubiquity, the rise of large language models (LLMs) is a testament to rapid technological advancement. Just a few short years ago, models like GPT-1 (2018) and GPT-2 (2019) barely registered a blip on anyone’s tech radar. In our real-world case study, we needed a system that would create test data.

Testing 135

Testing Cost-Benefit Interactive ROI 135

CIO Business Intelligence

JANUARY 30, 2025

While there is a lot of effort and content that is now available, it tends to be at a higher level which will require work to be done to create a governance model specifically for your organization. Governance is action and there are many actions an organization can take to create and implement an effective AI governance model.

Testing 93

Testing Metrics Cost-Benefit Modeling 93

CIO Business Intelligence

MARCH 25, 2025

Using the companys data in LLMs, AI agents, or other generative AI models creates more risk. What CIOs can do: Avoid and reduce data debt by incorporating data governance and analytics responsibilities in agile data teams , implementing data observability , and developing data quality metrics.

Risk 140

Risk Cost-Benefit Data-driven Digital Transformation 140

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

CIO Business Intelligence

APRIL 8, 2025

In many cases, companies should opt for closed, proprietary AI models that arent connected to the internet, ensuring that critical data remains secure within the enterprise. Soby recommends testing the enterprises current risk management program against real-world incidents.

Risk Management 131

Risk Management Risk Management Business Objectives 131

DataKitchen

SEPTEMBER 20, 2021

The best way to ensure error-free execution of data production is through automated testing and monitoring. The DataKitchen Platform enables data teams to integrate testing and observability into data pipeline orchestrations. Automated tests work 24×7 to ensure that the results of each processing stage are accurate and correct.

Testing 246

Testing Metrics Measurement Dashboards 246

AWS Big Data

DECEMBER 5, 2024

Solution overview The MSK clusters in Hydro are configured with a PER_TOPIC_PER_BROKER level of monitoring, which provides metrics at the broker and topic levels. These metrics help us determine the attributes of the cluster usage effectively. We then match these attributes to the relevant MSK metrics available.

Metrics 90

Metrics Dashboards Testing Optimization 90

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. The Need for Fine Tuning Fine tuning solves these issues.

Cost-Benefit 87

Cost-Benefit Data Processing Machine Learning Testing 87

datapine

NOVEMBER 15, 2022

1) What Are Product Metrics? 2) Types Of Product Metrics. 3) Product Metrics Examples You Can Use. 4) Product Metrics Framework. The right product performance metrics will give you invaluable insights into its health, strength and weaknesses, potential issues or bottlenecks, and let you improve it greatly.

Metrics 141

Metrics KPI Measurement Dashboards 141

O'Reilly on Data

MAY 14, 2020

Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model. In reality, many candidate models (frequently hundreds or even thousands) are created during the development process. Modelling: The model is often misconstrued as the most important component of an AI product.

Management 364

Management Experimentation B2B Machine Learning 364

AWS Big Data

NOVEMBER 27, 2024

The company’s business analysts want to generate metrics to identify ticket movement over time, success rates for sellers, and the best-selling events, venues, and seasons. After the data is in Amazon Redshift, dbt models are used to transform the raw data into key metrics such as ticket trends, seller performance, and event popularity.

Data Warehouse 104

Data Warehouse Analytics Testing Sales 104

Jedox

JULY 6, 2021

When considering the performance of any forecasting model, the prediction values it produces must be evaluated. This is done by calculating suitable error metrics. An error metric is a way to quantify the performance of a model and provides a way for the forecaster to quantitatively compare different models 1.

Forecasting 104

Forecasting Metrics Testing Modeling 104

CIO Business Intelligence

FEBRUARY 7, 2024

Key AI companies have told the UK government to speed up its safety testing for their systems, raising questions about future government initiatives that too may hinge on technology providers opening up generative AI models to tests before new releases hit the public.

Testing 124

Testing Reporting Risk Publishing 124

Domino Data Lab

MARCH 3, 2021

It’s often stated that nothing changes inside an enterprise because you’ve built a model. In some cases, data science does generate models directly to revenue, such as a contextual deal engine that targets people with offers that they can instantly redeem. But what about good decisions?

Metrics 93

Metrics Modeling Data Science Sales 93

Domino Data Lab

JANUARY 11, 2021

Even for experienced developers and data scientists, the process of developing a model could involve stringing together many steps from many packages, in ways that might not be as elegant or efficient as one might like. the experience is still rooted in the same goal: simple efficiency for the whole model development lifecycle.

Modeling 145

Modeling Metrics Data Science Testing 145

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. This has serious implications for software testing, versioning, deployment, and other core development processes.

Management 363

Management Machine Learning Experimentation Metrics 363

Smart Data Collective

AUGUST 16, 2021

Analytics data can be very useful for companies trying to create profitable online business models. Pay attention to the following metrics in your analytics dashboard to help you achieve greater success with your store. This metric is the average number you have to put in to get new customers. Audience Information.

Metrics 135

Metrics Analytics Cost-Benefit Sales 135

DataKitchen

NOVEMBER 29, 2021

Model developers will test for AI bias as part of their pre-deployment testing. Quality test suites will enforce “equity,” like any other performance metric. Continuous testing, monitoring and observability will prevent biased models from deploying or continuing to operate. Companies Commit to Remote.

Testing 245

Testing Data Lake Data Architecture Manufacturing 245

DataKitchen

MARCH 16, 2023

But wait, she asks you for your team metrics. Where is your metrics report? What are the metrics that matter? Gartner attempted to list every metric under the sun in their recent report , “T oolkit: Delivery Metrics for DataOps, Self-Service Analytics, ModelOps, and MLOps, ” published February 7, 2023.

Metrics 130

Metrics Data Analytics Analytics Measurement 130

DataKitchen

JANUARY 20, 2021

Centralizing analytics helps the organization standardize enterprise-wide measurements and metrics. With a standard metric supported by a centralized technical team, the organization maintains consistency in analytics. Develop/execute regression testing . Test data management and other functions provided ‘as a service’ .

Metrics 243

Metrics Experimentation Measurement Testing 243

Smart Data Collective

MAY 18, 2022

A high-quality testing platform easily integrates with all the data analytics and optimization solutions that QA teams use in their work and simplifies testing process, collects all reporting and analytics in one place, can significantly improve team productivity, and speeds up the release. This is not entirely true. Data reporting.

Testing 126

Testing Data Analytics Analytics Big Data 126

DataKitchen

MARCH 12, 2024

Key Success Metrics, Benefits, and Results for Data Observability Using DataKitchen Software Lowering Serious Production Errors Key Benefit Errors in production can come from many sources – poor data, problems in the production process, being late, or infrastructure problems. Tests assess important questions, such as “Is the data correct?”

Metrics 117

Metrics Software Cost-Benefit Testing 117

DataKitchen

APRIL 26, 2021

GSK had been pursuing DataOps capabilities such as automation, containerization, automated testing and monitoring, and reusability, for several years. Furthermore, the introduction of AI and ML models hastened the need to be more efficient and effective in deploying new technologies. Multiple Metrics for Success.

Measurement 246

Measurement Metrics Data-driven Dashboards 246

AWS Big Data

NOVEMBER 19, 2024

These organizations often maintain multiple AWS accounts for development, testing, and production stages, leading to increased complexity and cost. Additionally, customers adopting a federated deployment model find it challenging to provide isolated environments for different teams or departments, and at the same time optimize cost.

Metadata 111

Metadata Cost-Benefit Metrics Optimization 111

O'Reilly on Data

JANUARY 17, 2023

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

Machine Learning 363

Machine Learning Predictive Modeling Software Modeling 363

CIO Business Intelligence

NOVEMBER 27, 2024

Data quality for AI needs to cover bias detection, infringement prevention, skew detection in data for model features, and noise detection. Not all columns are equal, so you need to prioritize cleaning data features that matter to your model, and your business outcomes. asks Friedman.

Enterprise 141

Enterprise Data Quality Structured Data Modeling 141

Rocket-Powered Data Science

SEPTEMBER 20, 2021

The data scientists need to find the right data as inputs for their models — they also need a place to write-back the outputs of their models to the data repository for other users to access. The semantic layer bridges the gaps between the data cloud, the decision-makers, and the data science modelers.

Data Science 286

Data Science Forecasting Business Intelligence Sales 286

Cloudera

DECEMBER 4, 2024

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. The Cloudera AI Inference service is a highly scalable, secure, and high-performance deployment environment for serving production AI models and related applications.

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

AWS Big Data

FEBRUARY 13, 2024

AWS Glue has made this more straightforward with the launch of AWS Glue job observability metrics , which provide valuable insights into your data integration pipelines built on AWS Glue. This post, walks through how to integrate AWS Glue job observability metrics with Grafana using Amazon Managed Grafana. Choose Save & test.

Metrics 122

Metrics Dashboards Visualization Key Performance Indicator 122

DataRobot

FEBRUARY 10, 2021

Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. These errors might seem small, but the effects can be disastrous when the model is used to make decisions in the real world.

Modeling 98

Modeling Deep Learning Machine Learning Metrics 98

CIO Business Intelligence

NOVEMBER 13, 2024

One is going through the big areas where we have operational services and look at every process to be optimized using artificial intelligence and large language models. But a substantial 23% of respondents say the AI has underperformed expectations as models can prove to be unreliable and projects fail to scale.

IT 141

IT Consulting ROI Cost-Benefit 141