Domino Data Lab

AUGUST 6, 2021

In the world of machine learning (ML) and artificial intelligence (AI), governance is a lifelong pursuit. It’s similar to corporate governance, except that it applies specifically to ML/AI models and should be viewed as a subset of model risk management. Purpose of Model Governance and Why It’s Important.

Machine Learning 145

Machine Learning Modeling Testing Risk 145

Cloudera

FEBRUARY 11, 2022

In recent years, Ethical AI has become an area of increased importance to organisations. This has led to Ethical AI being an increasingly popular search term and the subject of many industry analyst reports and papers. Machine learning is a promising subfield of Artificial Intelligence (AI), where models are not explicitly predefined.

Machine Learning 92

Machine Learning Sales Data Science Modeling 92

O'Reilly on Data

JULY 28, 2020

The Core Responsibilities of the AI Product Manager. Product managers for AI must satisfy these same responsibilities, tuned for the AI lifecycle. 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

DataRobot Blog

NOVEMBER 15, 2022

Organizations are looking to deliver more business value from their AI investments, a hot topic at Big Data & AI World Asia. At the well-attended data science event, a DataRobot customer panel highlighted innovation with AI that challenges the status quo. Closing the Value Gap: Reducing AI Cycle Time.

Big Data 52

Big Data Experimentation Data-driven Machine Learning 52

Cloudera

DECEMBER 3, 2021

In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI. An AI system trained on data has no context outside of that data. For example, consider the case of an AI system used to prioritise patients admitted to hospital. We briefly summarise each challenge below. Capturing Intent.

Uncertainty 97

Uncertainty Measurement Metrics Risk 97

Insight

FEBRUARY 27, 2019

Are you a company working in AI and would like to get involved in the Insight AI Fellows Program? While Silicon Valley is the AI capital of the West Coast, NYC has made it clear that it’s the East Coast counterpart. Here are some of the top reasons we’ve identified for why NYC is the go-to spot for cutting-edge AI work.

Machine Learning 64

Machine Learning Manufacturing Finance IoT 64

O'Reilly on Data

JANUARY 9, 2024

To people who programmed using circuit diagrams and switches, these early languages looked as radical as programming with generative AI looks today. While a “repository of determinism” sounds a bit evil (feel free to come up with your own name), it’s important to understand why it is needed. It must be completely predictable.

Modeling 282

Modeling Software Testing Optimization 282

BizAcuity

NOVEMBER 26, 2019

For simple SSRS reports, it is not hard to move the queries over to Power BI and then reproduce the same visuals (tables, charts, etc). There are a lot of things you can do with SSRS that you can’t easily recreate in Power BI (which is why a general conversion tool would be very difficult to write).

Reporting 84

Reporting Recreation/Entertainment Interactive Data Processing 84

BizAcuity

NOVEMBER 27, 2019

For simple SSRS reports, it is not hard to move the queries over to Power BI and then reproduce the same visuals (tables, charts, etc). There are a lot of things you can do with SSRS that you can’t easily recreate in Power BI (which is why a general conversion tool would be very difficult to write).

Reporting 52

Reporting Recreation/Entertainment Interactive Data Processing 52

BizAcuity

NOVEMBER 27, 2019

For simple SSRS reports, it is not hard to move the queries over to Power BI and then reproduce the same visuals (tables, charts, etc). There are a lot of things you can do with SSRS that you can’t easily recreate in Power BI (which is why a general conversion tool would be very difficult to write).

Reporting 52

Reporting Recreation/Entertainment Interactive Data Processing 52

Domino Data Lab

JANUARY 6, 2019

In business terms, why does this matter ? Why does this matter? “With talent being one of the biggest challenges to AI, no matter how advanced a company’s digital program, it’s perhaps not surprising that companies are leaving no stone unturned when sourcing people and skills. First off, congrats to Berkeley.

Data Science 56

Data Science Machine Learning Reporting Visualization 56

Domino Data Lab

JULY 28, 2021

Today, the most advanced techniques used in data science are grouped under the term Artificial Intelligence (AI) Due to their information-acquiring nature, machine learning, deep learning, natural language processing (NLP) and computer vision are all considered branches within the field of AI. None of these techniques are new.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Domino Data Lab

JULY 22, 2019

why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”. If you look into Tukey, why he’s on the socks there, he did incredible work in stats.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

O'Reilly on Data

FEBRUARY 20, 2024

Almost every company has undertaken AI projects, including companies that, at least on the face of it, have “no AI” policies. AI is everywhere, and even if the initial frenzy around ChatGPT has died down, the big picture hardly changes. AI will become part of almost every job, ranging from manual labor to management.

Insurance 182

Insurance Modeling Technology Reporting 182

IBM Big Data Hub

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. Such infrastructure should not only address these issues but also scale according to the demands of AI workloads, thereby enhancing business outcomes. Let’s delve into the database portfolio from IBM available on AWS. 

Cost-Benefit 81

Cost-Benefit Metadata Optimization Management 81

Rocket-Powered Data Science

JULY 6, 2021

MLOps “done right” addresses sustainable model operations, explainability, trust, versioning, reproducibility, training updates, and governance (i.e., MLOps “done right” addresses sustainable model operations, explainability, trust, versioning, reproducibility, training updates, and governance (i.e.,

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

O'Reilly on Data

FEBRUARY 11, 2020

Adopting AI can help data quality. Almost half (48%) of respondents say they use data analysis, machine learning, or AI tools to address data quality issues. Can AI be a catalyst for improved data quality? Effect of dedicated data quality team on using AI tools. Most of those who don’t say they have no plans to start.

Data Quality 304

Data Quality Metadata Data Governance Publishing 304

O'Reilly on Data

MARCH 18, 2020

Last year, when we felt interest in artificial intelligence (AI) was approaching a fever pitch, we created a survey to ask about AI adoption. When we analyzed the results , we determined the AI space was in a state of rapid change, so we eagerly commissioned a follow-up survey to help find out where AI stands right now.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

bridgei2i

JANUARY 25, 2022

PODCAST: Making AI Real. How MLOps is Shaping AI in the Real World. How MLOps is Shaping AI in the Real World. His name is Kishore Kumar, and he is a senior AI solutions architect at our technology labs, and has been involved with us for the last seven years. Why do you think that happens? Kishore: Yes.

Machine Learning 52

Machine Learning Modeling Data Processing Enterprise 52

Domino Data Lab

APRIL 3, 2019

My interest in data governance (DG) began with the recent industry surveys by O’Reilly Media about enterprise adoption of “ABC” (AI, Big Data, Cloud). I introduced these themes in “ Three surveys of AI adoption reveal key advice from more mature practices ” and explored them in even more detail in “ Episode 7 ” of this column.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

IBM Big Data Hub

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

Machine Learning 65

Machine Learning Data Warehouse Modeling Cost-Benefit 65

Domino Data Lab

FEBRUARY 4, 2019

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. Silos top the list of “Why enterprise organizations fail?” That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

AWS Big Data

OCTOBER 26, 2023

Read on to understand why price-performance matters and how Amazon Redshift price-performance is a measure of how much it costs to get a particular level of workload performance, namely performance ROI (return on investment). Amazon Redshift delivers up to 4.9 times lower cost per user and up to 7.9

Data Warehouse 91

Data Warehouse Cost-Benefit Dashboards Optimization 91

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. How companies are building sustainable AI and ML initiatives ”.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

MARCH 29, 2021

This, for example, enables the Data Scientists to natively use NVIDIA RAPIDS – a suite of software libraries, built on CUDA-X AI, that gives them the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. GPU) and use bitnami/spark:2.4.6 as the base image. However, you could also use the bitnami/spark:3.0.0

Cost-Benefit 98

Cost-Benefit Machine Learning Testing Interactive 98

Smart Data Collective

NOVEMBER 9, 2022

Over 91% of leading businesses use AI technology. Based on the changes it has created, most experts agree that the primary goal of Machine Learning in this context is to reproduce more accurate predictions. As a result of the activity of artificial intelligence, the machine learns, remembers, and reproduces the correct option.

Machine Learning 97

Machine Learning Testing Cost-Benefit Data-driven 97

Domino Data Lab

AUGUST 29, 2019

The most significant new addition to the list is gym , the Open AI Gym itself. Let’s look at this code line by line: We use the Open AI Gym make() method to specify the particular environment that we’d like our agent to interact with. import Adam. As usual, we discuss each dependency in more detail as we apply it. Example 13.1

Optimization 111

Optimization Modeling Deep Learning Metrics 111

Domino Data Lab

AUGUST 1, 2021

the need for explainable AI is mainly motivated by the following three reasons: The need for trust – if a doctor is recommending a treatment protocol based on a prediction from a neural network, this doctor must have absolute trust in the network’s capability. Why should I trust you?: According to Fox et al., Ribeiro, M.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

O'Reilly on Data

MARCH 24, 2020

Why is high-quality and accessible data foundational? On the machine learning side, we are entering what Andrei Karpathy, director of AI at Tesla, dubs the Software 2.0 However, attempting to repurpose pre-existing data can muddy the water by shifting the semantics from why the data was collected to the question you hope to answer.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

O'Reilly on Data

JANUARY 25, 2021

What’s important isn’t the horse race so much as the features that languages are acquiring, and why. It’s easy to round up tens of thousands of nodes to train or deploy a complex AI application. Containerized deployment appears to minimize the problem of reproducible configuration, since a container is a complete software package.

Machine Learning 363

Machine Learning Software Testing Technology 363