In large organizations, the sheer amount of workflows, applications, and data platforms can monopolize all of your development resources. Projects that are simple patch-stitch-release do not generate long term revenue. Building a data science pipeline on top of shaky consolidation efforts can wreak havoc on data governance. Latency, while moving data from one part of the pipeline to another, can impede even the best data science efforts.

Read more: https://towardsdatascience.com/how-to-build-a-culture-of-experimentation-for-effective-ai-ml-de843752dae9

In the past, AI/ML research engineers led the development of AI features. Their efforts were largely focused on offline testing: building and refining training data, engineering specific features, and tuning hyperparameters to optimize metrics like precision and recall. This process took months and was often completed without direct user feedback.

Today, we live in a world where new, pre-trained foundation models come out each week, and AI features can be built by any engineer. In this rapidly changing environment, speed is far more important than precision. The companies that will win this wave of AI development will embrace a new paradigm of testing—one focused on quickly launching features, rapidly testing new combinations of models, prompts, and parameters, and continuously leveraging user interactions to improve performance.

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Conducting online experiments is like training your digital property. It not only enhances your conversion rate but also provides valuable opportunities to gain insights into various facets of the digital landscape, resulting in the overall growth and development of your company.

Read more: https://vwo.com/blog/experimentation-induces-learning-5-examples-that-are-proof/

A/B testing is no longer a new field. Finding a proper A/B testing tool isn’t the problem anymore. Now, the problem is choosing the right one.

Google has announced that it will shut down Google Optimize in September 2023, putting an end to one of the most used A/B testing tools ever.

We’ve assembled a list of the top A/B testing tools, with corresponding reviews from A/B testing experts to help you in your decision making process.

Read more: https://cxl.com/blog/ab-testing-tools/

Experimentation is simply applying the scientific principles to establish causation between changes to product and their outcomes. This topic has become more relevant in the past decade as products have become more complex and product managers have had to continually deliver incremental value. Running experiments adds a structured approach to discovering unbiased learnings and uncovering the real cause to changes in metrics, even when they are too small to be measured independently.

Read more: https://productcoalition.com/the-ultimate-guide-to-experimentation-for-product-teams-13bae40db313

Online experimentation platforms abstract away many of the details of experimental design, and their users do not have to worry about sampling, randomisation, subject tracking, data collection, metric definition and interpretation of results. The rapid adoption of these platforms in the industry might in part be attributed to the ease-of-use these abstractions provide. However, there are common pitfalls to avoid when running controlled experiments on the web, and one needs experts familiar with the entire software stack to be involved in the process.

Read more: https://booking.ai/leaky-abstractions-in-online-experimentation-platforms-ae4cf05013f9