O'Reilly on Data

JULY 28, 2020

In this article, we turn our attention to the process itself: how do you bring a product to market? Rachel Thomas writes about these challenges in “ The problem with metrics is a big problem for AI.” If you’re an AI product manager (or about to become one), that’s what you’re signing up for. Identifying the problem.

Marketing 362

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

OCTOBER 1, 2022

In summer 2022, P&G sealed a multiyear partnership with Microsoft to transform P&G’s digital manufacturing platform. The new IIoT platform uses machine telemetry and high-speed analytics to continuously monitor production lines to provide early detection and prevention of potential issues in the material flow.

Manufacturing 145

Manufacturing Digital Transformation IoT Internet of Things 145

CIO Business Intelligence

NOVEMBER 14, 2023

It’s no longer a question of whether organizations are moving to the cloud but rather how well it’s going. IT leaders at the 60-year-old fleet management firm determined that cloud could support its rapid growth without the challenges of maintaining data centers. Cloud isn’t that shiny new object in the distance, full of possibility.

Strategy 145

Strategy Cost-Benefit IT Key Performance Indicator 145

Cloudera

APRIL 24, 2023

Unwelcome… … are platform instability, downtime, hardware failure, poor performance, cluster resource contention, repeated process failures, runaway live queries, critical services alarms, invisibility into alarm cacophony… the list goes on. Visibility and Transparency Into the cluster, platform, services, and processes.

Scorecard 79

Scorecard Data Architecture Dashboards Measurement 79

Domino Data Lab

JULY 19, 2021

One of the primary challenges of any ML/AI project is transitioning it from the hands of data scientists in the develop phase of the data science lifecycle into the hands of engineers in the deploy phase. Who takes responsibility for the operationalized models? Data engineers assist by providing clean data.

Enterprise 85

Enterprise Data Science Testing Modeling 85

Sisense

APRIL 15, 2021

Augmented analytics (according to Gartner, which would know), uses technologies “such as machine learning [ML] and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms.” Tools of the AI trade.

Analytics 98

Analytics Data Science Modeling Machine Learning 98

CIO Business Intelligence

JULY 14, 2022

The challenges of managing data, the lifeblood of any enterprise, are continuously evolving and require attention because ignoring them only makes the “pain points” worse. The continuous attempts at comprehensive theft and hostage-taking of valuable corporate data can be overwhelming. . Otherwise, what is its value? 1 concern of CEOs.

Enterprise 119

Enterprise Snapshot Strategy Technology 119

CIO Business Intelligence

JUNE 7, 2022

An effective enterprise AI team is a diverse group that encompasses far more than a handful of data scientists and engineers. Carlos Anchia, co-founder and CEO of AI startup Plainsight, agrees that AI success rests largely on establishing a well-rounded team with a diverse range of advanced skills, but doing so is challenging.

Machine Learning 145

Machine Learning Data Science Consulting Metrics 145

CIO Business Intelligence

DECEMBER 20, 2023

“Gartner’s surveys and data from client inquiries confirm that developer productivity remains a top priority for software engineering leaders.” Therefore, engineering leadership should measure software developer productivity, says Mann, but also understand how to do so effectively and be wary of pitfalls.

Measurement 124

Measurement Software IT Metrics 124

DataKitchen

MARCH 16, 2023

How to measure your data analytics team? At DataKitchen, we have talked with many CDOs, data leaders, and other data team managers, and they have, ironically, been very un-analytic about how they run their teams. Under Velocity, the Mean Time to Deliver Data metric measures the time it takes to deliver data. Introduction.

Metrics 130

Metrics Data Analytics Analytics Measurement 130

IBM Big Data Hub

JUNE 26, 2023

To address this waste, consider implementing FinOps (Finance + DevOps). This cloud financial management practice is a way for cross-functional teams—like Engineering, Finance and Product—to work together and take ownership of cloud usage. But where to start? of global electricity use.

Digital Transformation 56

Digital Transformation Cost-Benefit ROI Data-driven 56

CIO Business Intelligence

NOVEMBER 7, 2022

But CIOs, CDOs, and chief scientists can take an active role in improving how many AI projects go from pilot to production. Developing models isn’t trivial, and data scientists certainly have challenges cleansing and tagging data, selecting algorithms, configuring models, setting up infrastructure, and validating results.

Machine Learning 128

Machine Learning Data Science Experimentation IoT 128

AWS Big Data

JANUARY 26, 2023

Apache Flink is a popular open-source framework and distributed processing engine for stateful computations over unbounded and bounded data streams. The final solution includes the following steps: The platform (DevOps) team and data engineering team push their source code to their respective code repositories.

Data Analytics 85

Data Analytics Analytics IoT Publishing 85

CIO Business Intelligence

APRIL 25, 2022

The market researchers at Gartner say that “Only half of AI projects make it from pilot into production, and those that do take an average of nine months to do so.”. So how can enterprises overcome these challenges? At its simplest, MLOps is defined as applying the principles of the DevOps movement to machine learning.

Machine Learning 134

Machine Learning Enterprise Deep Learning Cost-Benefit 134

IBM Big Data Hub

DECEMBER 19, 2023

Overall Cloud Capability Centre (CCC), or the equivalent core team, plays a significant role in driving change with application and platform teams. Codify security and compliance requirements into the patterns and guardrail them on the platform as set of policies. This helps adoption and acceleration.

Testing 82

Testing Enterprise Modeling Software 82

IBM Big Data Hub

OCTOBER 25, 2023

But simultaneously, generative AI has the power to transform the process of application modernization through code reverse engineering, code generation, code conversion from one language to another, defining modernization workflow and other automated processes. Much more can be said about IT operations as a foundation of modernization.

Cost-Benefit 90

Cost-Benefit ROI Modeling Testing 90

AWS Big Data

MARCH 12, 2024

At GoDaddy, we take pride in being a data-driven company. In this post, we discuss how we enhanced operational efficiency with Amazon EMR Serverless. Background In late 2020, GoDaddy’s data platform initiated its AWS Cloud journey, migrating an 800-node Hadoop cluster with 2.5 PB of data from its data center to EMR on EC2.

Cost-Benefit 92

Cost-Benefit Optimization Big Data Metrics 92

CIO Business Intelligence

AUGUST 1, 2023

Tapping their longstanding relationship with Miami University’s IT services, Baldwin and his team gained access to servers and other necessary hardware, along with student developers to help build and maintain the platform. “We

Risk 98

Risk IT Software Digital Transformation 98

CIO Business Intelligence

MAY 28, 2022

Yet the volume of demands placed on IT can make it challenging for IT leaders to concentrate IT resources on the right efforts. CIO.com talked with four IT leaders about what’s driving their efforts to streamline IT for greater agility, how they’re doing it, what the biggest challenges are, and the tricks they’ve uncovered for doing it well.

IT 137

IT ROI Cost-Benefit Technology 137

IBM Big Data Hub

JULY 31, 2023

We already covered how mainframe modernization isn’t just for the financial industry , so why not address the elephant in the room? The world’s biggest modernization challenges are concentrated in the banking industry. Banks large and small have all likely failed on one or more modernization or migration initiatives.

Digital Transformation 54

Digital Transformation Uncertainty Risk Data Warehouse 54

CIO Business Intelligence

FEBRUARY 21, 2023

If you think you’re keeping up, think again: One recent study from research firm Gartner found that the majority of CEOs (59%) say digital initiatives take too long and 52% take too long to realize value. The pressure is on to accelerate digital transformation, according to CIOs, researchers, and analysts.

Digital Transformation 145

Digital Transformation Marketing Technology Software 145

CIO Business Intelligence

MAY 25, 2022

Yet the volume of demands placed on IT can make it challenging for IT leaders to concentrate IT resources on the right efforts. CIO.com talked with four IT leaders about what’s driving their efforts to streamline IT for greater agility, how they’re doing it, what the biggest challenges are, and the tricks they’ve uncovered for doing it well.

IT 116

IT ROI Cost-Benefit Technology 116

Insight

APRIL 10, 2020

Insight is a community of over 3,000 data scientists, data engineers, machine learning engineers, consensus, DevOps, and security engineers who have completed an Insight Fellows Program and are now members of our professional network. The challenge is that this more hands-on approach is very resource intensive.

Uncertainty 74

Uncertainty Statistics Machine Learning Publishing 74

CIO Business Intelligence

APRIL 21, 2022

This has tended to make CISOs value their own choice of global, regional or national standards for addressing isolated aspects of the challenges they face. Despite these challenges, I am actually more optimistic about the outlook for improving cyber security through global standards than I have been for some years.

Software 52

Software Digital Transformation Enterprise Optimization 52

Sisense

JULY 10, 2019

Here’s how we re-architected Sisense with the right technologies and frameworks for the task at hand without simply porting the code over. They started with C/C++ code, which usually takes the longest to migrate from Windows to Linux. Taking Stock and Restarting the Project. The First Few Months.

IT 69

IT Testing Technology Sales 69

CIO Business Intelligence

DECEMBER 20, 2023

Within a DevOps context, the current manifestation of Zen is organizations embracing platform engineering methodologies that enable them to standardize around a common set of tools and practices, all while empowering – without burdening – their developers. Platform engineering arose to address this exact problem.

Software 122

Software Optimization Management IT 122

AWS Big Data

JUNE 6, 2023

However, other customers running Spark applications have chosen Spark Operator or native spark-submit to define and run Apache Spark jobs on Amazon EKS, but without taking advantage of the performance gains from running Spark on the optimized EMR runtime. With EMR on EKS, Spark applications run on the Amazon EMR runtime for Apache Spark.

Optimization 70

Optimization Data Lake Cost-Benefit Management 70

O'Reilly on Data

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. Can’t we just fold it into existing DevOps best practices?

IT 346

IT Testing Experimentation Software 346

DataKitchen

NOVEMBER 29, 2021

Many in the data industry recognize the serious impact of AI bias and seek to take active steps to mitigate it. In other words, they will use DataOps principles to build a platform that creates a robust, transparent, efficient, repeatable analytics process hub that unifies all workflows. AI Accountability. Data Gets Meshier.

Testing 245

Testing Data Lake Data Architecture Manufacturing 245

CIO Business Intelligence

MAY 26, 2022

How can it be built? How can it be trained? As a result, it can take more than nine months on average to deploy an AI or ML solution, according to IDC data. “We We call this ‘model velocity,’ how much time it takes from start to finish,” says IDC analyst Sriram Subramanian. This is where MLOps comes in.

Machine Learning 145

Machine Learning Modeling Data-driven Enterprise 145

CIO Business Intelligence

MAY 28, 2022

How can it be built? How can it be trained? As a result, it can take more than nine months on average to deploy an AI or ML solution, according to IDC data. “We We call this ‘model velocity,’ how much time it takes from start to finish,” says IDC analyst Sriram Subramanian. This is where MLOps comes in.

Machine Learning 143

Machine Learning Modeling Data-driven Enterprise 143

Hurwitz & Associates

JUNE 28, 2018

Data silos, different data formats – and organizational changes combining disparate data systems – make the CDO’s tasks challenging. How do CDOs approach these challenges? How do they solve thorny data standardization issues across their organizations and businesses? IBM CDO Conference. Changing Role of the CDO.

Insurance 40

Insurance Data Lake Machine Learning Enterprise 40

Insight

AUGUST 17, 2020

Fellows now also enjoy the technical advantages of using industry-standard distributed tools such as presentations via Zoom, internal messaging platforms (like Slack), and Github to contribute to a shared codebase. Insight recently completed its first totally remote session. states, Canada, and around the world.

Data Science 63

Data Science Data Processing Cost-Benefit Strategy 63

datapine

MAY 31, 2022

2) The Challenges Of Cloud Computing. In this article, we have gathered the 12 most prominent challenges of cloud computing that will deliver fresh perspectives related to the market. These challenges of cloud computing are not merely roadblocks to overcome. Check out these 12 challenges and how to face them!

Risk 237

Risk Cost-Benefit Business Intelligence Data-driven 237

O'Reilly on Data

MAY 14, 2020

To understand the skills that product managers need, we’ll start with the process of product development, then consider how this process differs in different kinds of organizations. Whether or not that’s how your organization works, every AI PM must consider how their products relate to these phases. The AI Product Pipeline.

Management 363

Management Experimentation B2B Machine Learning 363

O'Reilly on Data

OCTOBER 13, 2020

Our previous articles in this series introduce our own take on AI product management , discuss the skills that AI product managers need , and detail how to bring an AI product to market. Finally, the AI PM must work with production engineering teams to design and implement the alerting and remediation framework. I/O validation.

Management 246

Management Metrics Machine Learning Modeling 246

O'Reilly on Data

FEBRUARY 11, 2020

The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Data scientists and analysts, data engineers, and the people who manage them comprise 40% of the audience; developers and their managers, about 22%. Analysts and engineers predominate. This is surprising.

Data Quality 304

Data Quality Metadata Data Governance Publishing 304