The hottest Estimation Substack posts right now

Estimating work is hard, especially for complex tasks. It's okay to acknowledge that some work can't be easily estimated and to focus on learning instead.
Teams often have different opinions on estimates, which can lead to valuable discussions. These conversations help everyone align on the work and understand each other's perspectives.
Estimates shouldn't be treated as strict commitments. If people outside the team are pushing for deadlines based on estimates, it's important to push back and clarify that estimates are just rough calculations.

Quantile regression can be understood through the lens of loss optimization, specifically with the pinball loss function.
In machine learning, quantile regression is essentially regression with the unique pinball loss function that emphasizes absolute differences between actual and predicted values.
The asymmetry of the pinball loss function, controlled by the parameter tau, dictates how models should handle under- and over-predictions, making quantile regression a tool to optimize different quantiles of a distribution.

Project estimation is a critical skill influencing project success; it involves setting realistic expectations, aligning efforts, and managing resources effectively.
Key considerations in estimation include understanding project scope, conducting risk analysis, and utilizing estimation strategies like historical analysis and buffer times.
Transparency and communication are crucial in estimation; transparency helps manage stakeholder expectations while effective communication ensures clarity and trust in the estimation process.

SHAP values used in machine learning need to be estimated rather than calculated exactly, based on the concept of Shapley values from game theory.
Estimating SHAP values is necessary due to the exponential increase in possible coalitions with a high number of features, requiring sampling techniques.
The complexity of working with distributions in machine learning models necessitates the estimation of SHAP values using techniques like Monte Carlo integration.

Focus on managing engineering risk over managing the team.
Take responsibility for understanding and managing uncertainty, risk, and estimation in engineering.
Re-plan when things don't go according to plan to have more influence on outcomes as an engineer or tech lead.

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Estimating time complexity before fully solving a problem can act as a guide in coding interviews, especially when time-constrained.
Estimating complexity helps in ensuring close-to-optimal solutions and deciding whether to continue optimizing or move on to the next question.
Develop intuition and technical knowledge to effectively use this technique, but remember to focus on basics first if you're new to this approach.

Column order matters in the PostgreSQL sort cost model, impacting execution time significantly. Choosing the optimal order of sorting columns can vastly affect computational efforts.
Proposed modifications to the cost model aim to consider the number of columns in the sorting tuple and the number of duplicates, providing additional tools for query plan optimization.
Adjusting the PostgreSQL optimizer's cost model involves balancing costs for various operations, taking into account factors like extended statistics, column order, and the complexity of sorting.

User stories are informal explanations of software features from an end-user perspective.
Creating user stories keeps the team focused on real users, encourages collaboration, and drives creative solutions.
Story splitting is important to break down large user stories into smaller, valuable increments that can be completed within a sprint.

Having stakeholders collaborate in the estimation process can lead to successful outcomes in software development projects.
Involving stakeholders in the estimation process can help enhance collaboration and streamline the overall project.
Encouraging stakeholders to estimate alongside you as a software leader can make a significant difference in project success.

Estimation is a significant part of a software engineer's job, even though it might not always be explicitly stated in job descriptions.
Uninformed estimates can lead to significant issues, such as underestimating the time needed due to lack of understanding or unforeseen challenges.
Setting deadlines through estimates can promote productivity but may sometimes result in rushed work and a compromise on quality.

Surgical fine-tuning in ML makes algorithms better suited for specific business contexts through precise changes, an advancement over regular fine-tuning.
Entity-centric data modeling marries ML feature engineering with data engineering, improving data operations for companies.
Estimating efforts for ML projects can be simplified by considering the cost of delay and the real-time requirement of the algorithm.