SpaceX Starship prototype rocket explodes on landing after test launch

DataFloq

(Reuters) - A SpaceX Starship prototype rocket exploded on landing after an otherwise successful high-altitude experimental launch from Boca Chica, Texas, on Tuesday, in a repeat of an accident that destroyed a previous test rocket.

Experimentation and Testing: A Primer

Occam's Razor

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). There is a lot on the web about A/B or Multivariate testing but my hope in this post is to give you some rationale around importance and then a point of view on each methodology along with some tips. It is important to realize that experimentation and testing might sound big and complex but it is not.

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Experiment or Die. Five Reasons And Awesome Testing Ideas.

Occam's Razor

There is a tendency to think experimentation and testing is optional. Just don't fall for their bashing of all other vendors or their silly claims, false, of "superiority" in terms of running 19 billion combinations of tests or the bonus feature of helping you into your underwear each morning. For example I am quite fond of the fact that with Offermatica you can "trigger" tests based on behavior. I cannot recommend enough the wisdom of starting with a A/B test.

Six Nudges: Creating A Sense Of Urgency For Higher Conversion Rates!

Occam's Razor

Such is the case with A/B testing. I’m off to take a walk in the beautiful California sun, you go implement my recommendations for nudges as A/B tests—it is the only way to unlock the kind of imagination required to create profitable happy customer experiences. Marketing Tips Usability Voice of Customer conversion rate experimentation and testing user experienceBy every indicator available, ecommerce is continuing to grow at an insane speed.

Best Practices for Creating Long-Lasting and Continuous Discovery Habits

Speaker: Teresa Torres, Internationally Acclaimed Author, Speaker, and Coach at ProductTalk.org

Join internationally acclaimed author, speaker, and coach Teresa Torres as she explores the what, how, and why behind creating continuous discovery habits that give your team a clear benchmark to aspire to

The DataOps Vendor Landscape, 2021

DataKitchen

Testing and Data Observability. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Testing and Data Observability.

The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. The book introduces a wonderful process called the Lean Analytics Cycle, which aims to help you create a sustainable way to pick metrics that matter by tying them to fundamental business problems, creating hypotheses you can test and driving change in the business from the learnings you identify. Testing out a new feature. This should not be news to you.

Measuring Incrementality: Controlled Experiments to the Rescue!

Occam's Razor

Then they isolated regions of the country (by city, zip, state, dma pick your fave) into test and control regions. People in the test regions will participate in our hypothesis testing. So for variation #3, no catalogs or email were sent to the customers in the test group. The nice thing is that you can also test that! Work as hard as you can, and then some, to ensure that there are as few "disturbances" in your test and control group.

Some highlights from 2020

Data Science and Beyond

My main "day job" focus in 2020 was on being the tech lead for Automattic’s new experimentation platform (ExPlat). Among other things, it gave me an opportunity to apply my favourite approach to Bayesian A/B testing in the wild, and get excited about other interesting causal inference work we have in the pipeline. Causal inference Environment a/b testing career causality reef life survey remote work sustainability

Eight Silly Data Things Marketing People Believe That Get Them Fired.

Occam's Razor

If the simple A/B (test/control) experiment demonstrates that delivering display banner ad impressions to the test group delivers increased revenue, buy impressions to your heart's content. competitive intelligence Digital Analytics Digital Marketing Marketing Tips Search Engine Marketing Social Media Web Metrics actionable web analytics digital marketing experimentation and testing marketing metrics

Why CEOs should test big digital business ideas in tiny countries.

Mark Raskino

He was talking about something we call the ‘compound uncertainty’ that must be navigated when we want to test and introduce a real breakthrough digital business idea. Next time you have a truly breakthrough digital business idea in front of you, and you are wondering whether it’s yet safe to risk the money, brand capital and personal reputations on an experimental foray into an unknown future, take second look at your map of the world.

Four Popular AI Implementations to Revolutionise Healthcare

DataFloq

It involves effective administration of the processes, be it finding experimental subjects, monitoring test sites, or identifying potential health risks of processes and.

Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure.

The 12 Rules of DataOps to Avert a DataOops

Kirk Borne

This can be overcome with small victories (MVP minimum viable products, or MLP minimum lovable products) and with instilling ( i.e., encouraging and rewarding) a culture of experimentation across the organization. DataOps accepts a fail-fast, learn-fast culture of experimentation.

Don’t Just Sit There, Experiment!

Decision Management Solutions

Randomly select groups of customers and use the experimental approach on them, to prevent bias, and ensure a clean test Keep information on both groups – what you would normally do and what you experimented on – so you can compare the approaches later.

Do You Need a DataOps Dojo?

DataKitchen

Develop/execute regression testing . Test data management and other functions provided ‘as a service’ . A COE typically has a full-time staff that focuses on delivering value for customers in an experimentation-driven, iterative, result-oriented, customer-focused way.

Bringing ML to Agriculture: Transforming a Millennia-old Industry

Domino Data Lab

Experimentation and collaboration are built into the core of the platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Guest post by Jeff Melching, Distinguished Engineer / Chief Architect Data & Analytics.

Machine Learning Product Management: Lessons Learned

Domino Data Lab

Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. It is more experimental because it is “an approach that involves learning from data instead of programmatically following a set of human rules.” Because the nature and approach of ML projects is more experimental, industry people and their companies won’t know what will happen until they try it (i.e.,

Omdia Selects DataRobot as Recommended MLOps Vendor

DataRobot

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.

Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments. Finally, the test and learn approach and accountability should be strengthened so that algorithm-driven insights are given a fair shot to be incorporated into decision making. It is also important to have a strong test and learn culture to encourage rapid experimentation.

MNIST Expanded: 50,000 New Samples Added

Domino Data Lab

Recently, Chhavi Yadav (NYU) and Leon Bottou (Facebook AI Research and NYU) indicated in their paper, “ Cold Case: The Lost MNIST Digits ”, how they reconstructed the MNIST (Modified National Institute of Standards and Technology) dataset and added 50,000 samples to the test set for a total of 60,000 samples. Many data scientists and researchers have used the MNIST test set of 10,000 samples for training and testing models for over 20 years. Did they overfit the test set?

How We Teach The Leaders of Tomorrow To Be Curious, Ask Questions and Not Be Afraid To Fail Fast To Learn Fast

Rocket-Powered Data Science

We build models to test our understanding, but these models are not “one and done.” 5) Is your organizational culture ready for this (for data-informed decisions; an experimentation mindset; continuous learning; fail fast to learn fast; with principled AI and data governance)? (6)

Methods of Study Design – Experiments

Data Science 101

Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Suppose we want to test the effectiveness of a new drug against a particular disease. Bias can cause a huge error in experimentation results so we need to avoid them.

Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t. Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning.

Humans and AI: AI and Individuality

DataRobot

Although modern medicine is founded on rigorous experimental design and statistical analysis, and many research studies have shown the superiority of objective analysis over human intuition, medical AI adoption will depend on consumer receptivity and trust in this new technology.

Announcing Domino 3.3: Datasets and Experiment Manager

Domino Data Lab

Models are so different from software — e.g., they require much more data during development, they involve a more experimental research process, and they behave non-deterministically — that organizations need new products and processes to enable data science teams to develop, deploy and manage them at scale. And “Experiment Manager” gives data scientists a way to track, find, and organize all the ideas they have tested over the course of their research.

How Analytics by Design Tackles The Yin and Yang of Metrics and Data

Kirk Borne

By applying an agile methodology, we are able to adopt a culture of experimentation that permits us to fail fast in order to learn fast and that delivers both the minimum viable product and the minimum lovable product. Therefore, in sequential order, the four principles of Analytics by Design are: Adopt a culture of experimentation — “ test or get fired ” is the mantra within one successful analytics-driven organization. Written by Dr. Kirk Borne.

Ask Why! Finding motives, causes, and purpose in data science

Data Science and Beyond

Causality and experimentation. Making Bayesian A/B testing more accessible. Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling.

Drug Discovery Needs AI To Discover More Treatments

Smart Data Collective

Phase 0 is the first to involve human testing. Phase I involves dialing-in the proper dosage and further testing in a larger patient pool. Improving human health, longevity, and satisfaction are some of the primary purposes of technology.

Misadventures in experiments for growth

The Unofficial Google Data Science Blog

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. Now how is it possible for these fledgling products exist, do something, have enough users that one could contemplate experimentation, and yet still not have market fit?

Analytics On The Bleeding Edge: Transforming Data's Influence

Occam's Razor

This is very hard to do, we now have a proven seven-step experimentation process, with one of the coolest algorithms to pick matched-markets (normally the kiss of death of any large-scale geo experiment). The first component is a gloriously scaled global creative pre-testing program.

Snowflake and Domino: Better Together

Domino Data Lab

Now that we’ve created a Snowflake connection, we write a query to access the “DOMINO_TESTING” database and the table “wine_red” to print results to our Jupyter instance. Now we load the CSV File into a Pandas data frame and then write that table into the DOMINO_TESTING database in Snowflake.

The Role of Theory in Data Analysis

Simply Statistics

Many data analysts are not involved in the data collection process or the experimental design and so it is important to inquire about he process by which the data came to them. For example, one person told me a story of an analysis she did on a laboratory experiment that was ostensibly simple (basically, a t-test). In data analysis, we make use of a lot of theory, whether we like to admit it or not.

Augmented Data Discovery Provides Users with Crucial Answers

Smarten

Advanced Data Discovery allows business users to perform early prototyping and to test hypothesis without the skills of a data scientist. Advanced Data Discovery ensures data democratization and can drastically reduce the time and cost of analysis and experimentation. Advanced Data Discovery Can and Should Be Available to All!

Is Artificial Intelligence Revolutionizing Environmental Health?

Simply Statistics

Concurrently, while population data are booming, toxicology is creating a variety of experimental models to advance our understanding of how chemicals and environmental exposures may pose risks to human health. Rigorous data science requires a team science approach to achieve a variety of functions such as developing algorithms, formalizing common data platforms and testing protocols, and properly maintaining and curating data sources.

12 Marketing Reports Examples You Can Use For Annual, Monthly, Weekly And Daily Reporting Practice

datapine

A daily marketing report will also allow you for faster experimentation: running small operations to answer small questions. from your campaigns, various tests, and mistakes.

Teaching AI to Smell by Using DataRobot

DataRobot

Traditionally, experimentation and observation was the only way to understand the physical-chemical properties of the molecule. The competition metric is the maximum Tanimoto score of the top five recommendations to the ground truth averaged over the test dataset.

Unintentional data

The Unofficial Google Data Science Blog

A landscape of promise and peril The data scientist working today lives in what Brad Efron has termed the "era of scientific mass production," of which he remarks, "But now the flood of data is accompanied by a deluge of questions, perhaps thousands of estimates or hypothesis tests that the statistician is charged with answering together; not at all what the classical masters had in mind. [1]" We must correct for multiple hypothesis tests.

Machine Learning Integration Options

Paul DeBeasi

Machine learning projects are inherently different from traditional IT projects in that they are significantly more heuristic and experimental, requiring skills spanning multiple domains, including statistical analysis, data analysis and application development. Most organizations have defined the process to build, train and test machine learning models.

Open Data Science and Machine Learning for Business with Cloudera Data Science Workbench on HDP

Cloudera

Data scientists require on-demand access to data, powerful processing infrastructure, and multiple tools and libraries for development and experimentation. Run experiments with historical reference for hyperparameter tuning, feature engineering, grid searches, A/B testing and more. To move beyond laptop experimentation to impacting the business, data teams also need seamless sharing and native integration with the production data platform.

Product Management for AI

Domino Data Lab

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Testing. Testing is critical. It’s very hard to have stable tests.

Next Stop – Predicting on Data with Cloudera Machine Learning

Cloudera

To effectively leverage their predictive capabilities and maximize time-to-value these companies need an ML infrastructure that allows them to quickly move models from data pipelines, to experimentation and into the business. A/B testing). This is part 4 in this blog series.

RDF-star Implementation in GraphDB and How Synaptica Used It Within Graphite for Access Control

Ontotext

More than 10 years ago we even had experimental support of what we called triplesets , which allowed the association of metadata to sets of statements. Ontotext: What is RDF-star?