The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Hurwitz & Associates

JULY 19, 2018

Edge-to-cloud is the central focus of Hewlett Packard Enterprise (HPE) marketing and go-to-market efforts in 2018/2019. The $4 billion investment will be used for R&D, product development, technical services and the development of new consumption models for Edge and cloud. Consumption models are changing. Key Takeaways.

Strategy 40

Strategy Data-driven Experimentation Software 40

Domino Data Lab

JUNE 13, 2019

Many data scientists and researchers have used the MNIST test set of 10,000 samples for training and testing models for over 20 years. 2018 , 2019 ], the rediscovery of the 50,000 lost MNIST test digits provides an opportunity to quantify the degradation of the official MNIST test set over a quarter-century of experimental research.”

Testing 83

Testing Data Science Experimentation Metadata 83

Domino Data Lab

JANUARY 24, 2019

Before we get too far into 2019, I wanted to take a brief moment to reflect on some of the changes we’ve seen in the market. These solutions help data analysts build models by automating tasks in data science, including training models, selecting algorithms, and creating features. Reflections. Jupyter) or IDEs (e.g.,

Data Science 65

Data Science Marketing Data-driven Statistics 65

FineReport

DECEMBER 19, 2019

If you want to learn more about self-service BI tools, you can take a look at this review: 5 Most Popular Business Intelligence (BI) Tools in 2019 , to understand your own needs and then choose the tool that is right for you. Of course, other BI tools such as Power BI and Qlikview also have their own advantages. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Sisense

MARCH 26, 2021

Companies surveyed by Harvard Business Review Analytic Services (HBR) report that two of the most important strategic benefits of using data analytics are (1) identifying new revenue and business models and (2) becoming more innovative. But there’s a gap between expectations and reality, and companies are falling short of their aspirations.

Analytics 98

Analytics Data-driven Dashboards Experimentation 98

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. . Meta-Orchestration .

Testing 307

Testing Machine Learning Consulting Data Quality 307

Decision Management Solutions

JUNE 25, 2019

I presented on Backwards Engineering – planning Machine Learning (ML) deployment in reverse. The last mile – getting ML models embedded into production systems – is critically important for analytic value and yet it is hard and often neglected. A great technical view of critical business issues!

Predictive Analytics 75

Predictive Analytics Analytics Data Science Experimentation 75

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.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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. Ensure a culture that supports a steady process of learning and experimentation. hill climbing ) ( Clue #1 ).

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Doesn’t this seem like a worthy goal for machine learning—to make the machines learn to work more effectively? Program Synthesis 101 ” – Alexander Vidiborskiy (2019-01-20). Introduction.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. An ML-related topic, “models,” was No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Corinium

APRIL 25, 2019

I’ve found many IT as well as Business leaders have a mental model of data in that it is simply part of, or belongs to, a specific database or application, and thus they falsely conclude that just procuring a tool to protect that given environment will sufficiently protect that data. This is a much more proactive and scalable model.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

DataRobot

JUNE 2, 2021

They went on to say that investing in MLOps directly answers one of the biggest questions facing AI practitioners in the enterprise: how to move from experimentation to transformation. The Growing Importance of MLOps, Including Operationalizing Models and Putting Models into Production.

Experimentation 52

Experimentation Machine Learning Reporting Modeling 52

O'Reilly on Data

JANUARY 7, 2020

In fact, in our 2019 surveys, more than half of the respondents said AI (deep learning, specifically) will be part of their future projects and products—and a majority of companies are starting to adopt machine learning. To stay competitive, data scientists need to at least dabble in machine and deep learning.

Deep Learning 179

Deep Learning Machine Learning Experimentation Modeling 179