World Innovation and Change Management Institute

I continue supporting  the technology and data advisory group of the WICMI.  This includes participating in the annual conference and contributing with technical articles.

Technology & Data Advisory Group Member

I served for the technology and data advisory group of the World Innovation and Change Management Institute, Geneva. The institute aims to be a catalyst for change by providing a global forum accessible by organisations and individuals managing sustainable and responsible change. 

This included co-steering their knowledge exchange programme through a series of talks and blog articles authored by founding advisory members and with contributions from recognised technical experts.

Technology and Data Advisory Group Chair

Angel AI UK

Angel AI Ltd provides greenfield solution development services and mentoring and advice on agile development and capability development on a range of software automation and analytics technologies. Clients include companies in global e-commerce, biopharma, healthcare, fintech, and e-commerce. Very resourceful, capable of shifting mindset from business decision-making to technology development.

Check out Angel_AI series:

https://www.linkedin.com/feed/update/urn:li:activity:6966763826805415936

Full Stack Python Data Science

Dataworkout

At Dataworkout, I led a team creating educational films on various technology topics, including artificial intelligence and blockchain.

CTO: Architecture and AI/NLP

The Manchester Metropolitan University

I worked in technology management and data analytics, teaching and developing educational programmes on business analytics and visualisation, quantitative methods, technology development and data science, and leading e-commerce and data analytics projects.

Senior lecturer technology and data analytics

AIDA Tech...

AIDA Technology developed a fitness tracking solution using geolocation, gamification and data analytics to nudge families to walk outdoors. It was the precursor of Pokemon GO. The company was awarded Top-Ten NHS health innovator award for its novel fitness tracking solution promoting active lifestyles.

CTO (Fractional):Software architecture and fitness analytics

Full-Stack Data Engineering & AI Lifecycle. Compact Data Science - No 1 Mathematical and Statistics Skills on Amazon (June)

Following a career in prestigious British Universities, I am a product-savvy, die-hard engineer and educator, with experience as applied AI engineering leader and researcher. My expertise in statistical analysis, machine learning and AI, complements my deep software engineering knowledge. 

I work with disruptive health-tech scale-ups and pharmaceutical corporations to envision and spark responsible innovation at scale, supporting data science teams developing end-to-end data-centric solutions that leverage large language models and knowledge graphs. 

I hold a PhD in Innovation Management applied to Health Informatics from the University of Manchester, and a MSc in Electronics with Biomedical and AI specialisations from the University of Hertfordshire. I was the founding technical director for bioelectronics and optics of the National Centre for Marine Life Research, Manglaralto, Peninsula of Santa Elena, Ecuador, which was managed by an international consortium including government and academic institutions from Japan and Europe.

My doctoral research at the University of Manchester was underpinned by pragmatism and symbolic interactionism in context to healthcare information systems -besides statistical analysis and machine learning methods. Continuing with innovation and technology management, I led projects focusing on the direct and mediating effects on firm performance applying methods like random-effects meta-regression and network analysis.

I have certifications in healthcare data science and NLP with John Snow Labs. For instance, their SparkNLP library leverages the scalability and efficiency of Apache Spark and Tensorflow to process large datasets, enabling researchers to perform sophisticated NLP analyses that can be integrated with knowledge graphs.

"The Quest for Super Intelligence" documentary with Andrea Howarth-Salazar.

AI-for-Good: Soteria (mental health) and BlissSymbolics (U. Toronto).

Compact Data Science series on Kindle:

USA: https://tinyurl.com/compactdatascienceusa

UK: https://tinyurl.com/compactdatascienceuk

#angelsalazarphd #compactdatascience #limitless #aiquest #ebooks #amazon #questmasteralgorithm #NLP #MachineLearning #DataScience #Linguistics #ComputationalLinguistics #SyntaxAnalysis #Morphology #Sociolinguistics #DiscourseAnalysis #GroundedTheory #Phenomenology #HealthcareInformatics #TextMining #ArtificialIntelligence #QualitativeResearch #StatisticalAnalysis #Python #NLTK #SpaCy #SparkNLP #CleanCode #NoTechDebt

⚡ LangSmith Evaluations ⚡

With the rapid pace of AI, developers are often faced with a paradox of choice: how to choose the right prompt, how to trade-off LLM quality vs cost?

Evaluations can accelerate development with structured process for making these decisions.

But, we've heard that it is challenging to get started. We're excited to launch a series of short videos focused on explaining how to perform evaluations using LangSmith.

1. Why Evals Matter
Lays out 4 general considerations for evaluation: (1) dataset, (2) evaluator, (3) task, (4) how to apply eval to improve your product (e.g., unit tests, A/B tests, etc).

📽️: https://lnkd.in/gBXqseae
📓: https://lnkd.in/gu_mWjwX

2. Evaluation Primitives
Introduces the primary components of LangSmith evaluation, including tracing (along with metadata, feedback, tags), datasets, and evaluators.

📽️: https://lnkd.in/gzcHWYmU
📓: https://lnkd.in/gumShhVe

3. Dataset Creation: Manual Curation
Users often want to build custom eval sets (e.g., of QA pairs for RAG, or prompt-expected response pairs). This shows how to create, edit, and version your own evaluation dataset using the LangSmith SDK.

📽️: https://lnkd.in/ggsVvn_i
📓: https://lnkd.in/gjPQq9rh

4. Dataset Creation: From Logs
Users often want to capture user logs as good and / or challenging examples to re-test their application on. This shows how to create datasets directly from logs (e.g., user interactions with your app that are captured in LangSmith).

📽️: https://lnkd.in/gf56-NYF
📓: https://lnkd.in/gU28eYYv

5. Evaluators: Pre-built
Users often want to quickly get started with eval; for this you can use many of LangSmith's pre-built evaluators (e.g., that use LLM-as-a-judge) for tasks such as RAG (question answering), evaluating LLM output based upon user-supplied criteria, etc.

📽️: https://lnkd.in/gHA25FSM
📓: https://lnkd.in/gDxFPDyF

6. Evaluators: Custom
Users often want to define custom evals that are domain specific to a particular app. This shows how to define your own custom evaluation logic in LangSmith.

📽️: https://lnkd.in/gfK3ge9k
📓: https://lnkd.in/gn7_83dC

7. Eval Comparisons
Once a user has run a few different experiments, it is common to compare results (both using metrics and with manual inspection of the examples that show the most difference). This shows how to compare results of multiple experiments in the LangSmith UI, using review of traces to inspect run outputs or the grader decisions.

📽️: https://lnkd.in/gcyQ3T2B
📓: https://lnkd.in/gHBq8w_V

Notebook used in videos:
https://lnkd.in/gefdfSey

Angel Salazar, PhD

The OECD-African Union (AU) Artificial Intelligence (AI) Dialogue, held in Paris from 5 to 7 March 2024, set a new benchmark in collaboration between two central entities in international AI governance.

Amr Safwat, Sam Hall from the UK Government and OECD.AI's Karine Perset give their perspective on the AU/OECD AI Dialogue in this blog post:
READ MORE/WATCH THE VIDEO 👉 https://lnkd.in/ewBg6MAk

The closed-door event, supported by the United Kingdom, highlighted the need for truly global cooperation to foster trustworthy AI. 

The dialogue facilitated discussions on AI governance and addressed joint challenges, serving as a platform for sharing ideas, strategies, and expectations from across continents.

As the dialogue concluded, it was clear that this event was not an endpoint but a milestone in the ongoing journey towards responsible AI governance. The discussions, insights, and partnerships offer a solid foundation for future collaboration between the OECD, the AU, and other global stakeholders. 

The OECD-AU AI Dialogue has laid down a marker for what can be achieved when diverse voices come together, united by a common goal of harnessing AI for the good of all.

A few attendees and organizers participated responded to questions about the dialogue's importance and possible outcomes:
Melody Musoni, PhD Rachel Adams Conrad Tucker Armando Guio Español Johannes Paul Jütting Nicole Gregory Keith Hanna Nagham El Houssamy
WATCH THE VIDEO 👉 https://lnkd.in/eYNkZNdZ

Amazouz Souhila Mehdi Snene, Ph.D. Keith Strier Celine Caira Luis Aranda Jamie Berryhill Orsolya Dobe Jacqueline Lessoff Yuki Yokomori  Lucia Russo Noah Oder Fabio Curi John Leo Tarver ⒿⓁⓉ Riccardo Rapparini Rashad Abelson Angélina Gentaz Valéria Silva Bénédicte Rispal Robin Staes-Polet Johannes Leon Kirnberger Eunseo Dana Choi  Pablo Gomez Ayerbe Sara Fialho Esposito Nikolas S. Sarah Bérubé Guillermo H.

#afrianunion #internationalcooperation #oecd #artificialintelligence #aipolicy #trustworthyai

Just published a new post on Towards AI explaining the Graph Attention Networks paper!

This post is an illustrated walkthrough of the "Graph Attention Networks" paper by Veličković et al., along with the step-by-step PyTorch implementation of the proposed model.
I hope you find it helpful!

Check it out here: https://lnkd.in/eywGZd_s
Original paper: https://lnkd.in/eg3qgbvn
Code repository: https://lnkd.in/ejfF68tK

I'd be happy to know what you think!

#GNN #GraphML #GraphRepresentationLearning #GAT #GraphAttentionNetworks 

pub.towardsai.net • 3 min read

Graph Attention Networks paper explained with illustration and PyTorch implementation

In a piece that highlights his co-authored research on which professions will be most impacted by AI, #NYUStern Professor Rob Seamans tells Financial Times that he expects AI tools to help academics become better at what they do. Read more: 

ft.com • 4 min read

Academics express confidence that they and AI can work together

A short break from AI coding.

#Cumbria #Lakes #Sketching

Join us in the next show and tell with Ameer Sohail S. (MSc) teaching an Introduction to Machine Learning: Understanding the basics. 

This Friday at 12pm!

To sign up please use the QR code or via our sign up form https://lnkd.in/e4hX5bxG

#Data #Analystics #machinelearning #python #NHS #introduction #community #showandtell #dataengineer #datascience

Delighted to see that the MOU between the Liverpool City Region Combined Authority and government to release millions of pounds of investment into the #LifeSciences and #Healthcare sector through the new Investment Zone.

#LCRLifeSciences | Millions of pounds of funding has been unlocked as Liverpool City Region's Life Sciences #LCRInvestmentZone opens for business.

#LCRInvestmentZone plans include 21 projects designed to supercharge the sector and create 8,000 new jobs. This will include funding to help with the development of two new buildings as part of the Violet phase 2 at Sci-Tech Daresbury. This will include a Class 2 laboratory building with lab units of 5000 sq ft upwards, so perfect for scaling lab-based businesses. Companies moving into this building will also get access to occupier incentives including business rates relief and NI contribution holidays.

To find out more about the Investment Zone👇
https://ow.ly/EuCY50ReK3W

To find out more about Violet phase 2 go to  https://lnkd.in/eBw5BF7q

liverpoolcityregion-ca.gov.uk • 3 min read

Millions of pounds of funding unlocked as Liverpool City Region Investment Zone opens for business

Clear and concise explanation of the fundamentals of Large Language Models .

In practice, real models use many more dimensions, but I hope this gives you an idea of how different aspects of a word could be quantified into numerical values in a natural language vector (i.e., embedding). 

#LanguageModels #AI #ChatGPT #NeuralNetworks #MachineLearning #DeepLearning #TechInnovation #DataScience #ArtificialIntelligence #GPT3 #GPT4 #NLP #NaturalLanguageProcessing #Technology #TechTrends #BigData #AIResearch #SemanticAnalysis #VectorEmbeddings #WordEmbeddings #TextGeneration #AIWriting #OpenAI #TechCommunity #SmartTechnology #TechUpdates #FutureOfAI #AIInsights #TechDevelopment #AIEducation #DigitalTransformation #TechSavvy #AIPower #AIandEthics #AIForGood #ResponsibleAI #AITechnology #MachineIntelligence #CognitiveComputing #AIApplications #AILearning #AIDisruption #AIFuture #TechImpact #AITools #AITrends #AIAnalysis #AIStrategy #TechProgress #AIAdvancement #DeepTech #EmergingTech #InnovativeTech #TechRevolution #AIRevolution #TechExploration #AIThinking

Angel Salazar, PhD on LinkedIn • 3 min read

Deciphering the Mythology of Language Models: Word Embeddings

EXPOSING AI's ACHILLES

In 2016, #AlphaGo defeated the Go world champion #LeeSedol, winning four games to one. Machines had finally become superhuman at Go. 

Even superhuman AIs have blind spots and can be tripped up by surprisingly simple tricks. This new paper shows a way to automatically find vulnerabilities in a “victim” AI system by training an adversary AI system to beat it.

AI systems, from image classifiers to natural language processing systems, are vulnerable to adversarial inputs: seemingly innocuous changes like adding imperceptible static to an image or a distractor sentence to a paragraph can crater the performance of AI systems while not affecting humans. 

This is concerning according to the authors of the study:

If superhuman Go AIs can be hacked in this way, who’s to say that transformative AI systems of the future won’t also have vulnerabilities?

This is clearly problematic when AI systems are deployed in high-stakes situations like running critical infrastructure, or performing automated trades, where bad actors are incentivised to exploit them. 

It also poses significant problems when an AI system is tasked with overseeing another AI system, such as a learned reward model being used to train a reinforcement learning policy, as the lack of robustness may cause the policy to capably pursue the wrong objective (so-called reward hacking).

The researchers modified the AlphaZero training approach.

AlphaZero combines intuition and simulation for decision-makig, using a policy network for generating promising moves and Monte Carlo Tree Search (MCTS) for simulating future game states. The policy network directs MCTS towards likely beneficial moves, while a value network evaluates board states to enhance the efficiency of MCTS without needing exhaustive simulations. Training involves a feedback loop, where better network predictions enhance MCTS, which in turn provides better training data for the networks. Self-play generates training data, providing a curriculum of increasing challenges with the agent playing against copies of itself, mirroring how humans learn through gradually escalating difficulties.

The new approach uses an adapted MCTS to sample moves from the specific player's perspective. The challenge level escalates by confronting the AI with progressively skilled versions of KataGo.

Link to the full article: 👇🏼

https://lnkd.in/eTxWZcwK

#AI #GoGame #ArtificialIntelligence #AdversarialAI #MachineLearning #AIvulnerabilities #Cybersecurity #CriticalInfrastructure #AutomatedTrading #NaturalLanguageProcessing #ImageClassification #Research #AIsecurity #ReinforcementLearning #AdversarialInputs #RewardHacking #SuperhumanAI #AIdevelopment #AIsystems #Technology #Innovation #Science #CyberAttack #DataProtection #AIethics #AIsafety #MachineIntelligence #DeepLearning #TechNews #FutureOfAI #Robustness #DigitalSecurity #TechInnovation #AIresearch #AIstrategy #ArtificialGeneralIntelligence #HighStakesAI

Are you building a tech startup in #Manchester and the #NorthWest?

Join us on 17th April from 10 am to 12 pm 

We are special - we let you buy your own coffee 🤣 

No #recruiters please

https://lnkd.in/eC5Q-zEa

#techstartups #manchester #mcrtech #techmcr #techmanchester #techcelerate

Revolutionising Football Tactics: 

TacticAI for Enhanced Corner Kick Strategies

A novel application of graph neural networks and geometric deep learning to model football tactics, evaluated through quantitative benchmarks and qualitative expert feedback. The results demonstrate the potential for AI to provide valuable insights and suggestions to coaches.

Highlights:

- TacticAI uses geometric deep learning on graphs representing player positions and movements to learn efficient representations of corner kick tactics from spatio-temporal tracking data. This allows it to exploit symmetries of the football pitch for improved data efficiency.

- It has three main components: 

1) Predicting which player will receive the ball first after a corner kick
2) Predicting the probability a shot will be taken as a result of the corner  
3) Recommending adjustments to player positions and velocities to maximise or minimise shot probability

- Quantitative evaluation shows TacticAI accurately predicts corner kick receivers and shot probability. The recommended player adjustments are realistic and effective at influencing shot probability.

- A case study with expert coaches and analysts from Liverpool Football Club  provides strong evidence of TacticAI's practical utility. The generated player adjustments were judged as realistic and favored over existing tactics 90% of the time. TacticAI also serves as an effective system for retrieving similar corner kick situations.

- The authors believe TacticAI lays the groundwork for next-generation AI assistants to aid football coaches with set piece analysis and tactic development, despite limited availability of high-quality tracking data. The geometric deep learning approach allows data-efficient learning of salient tactical patterns.

#TacticAI #FootballTactics #GeometricDeepLearning #GraphNeuralNetworks #SpatioTemporalData #CornerKicks #AIinSports #PlayerPositioning #VelocityAdjustments #ShotProbability #FootballAnalytics #DataEfficiency #FootballCoaching #AIAssistants #TacticalAnalysis #SetPieces #FootballPitchSymmetry #PlayerMovements #SportScience #QuantitativeEvaluation #LiverpoolFC #FootballStrategy #AIRecommendations #FootballCoaches #TacticDevelopment #SportAnalytics #AIandSports #NextGenerationAI #FootballAnalysis #TacticalAdjustments #FootballTacticsAnalysis #PlayerAdjustments #SportTechnology #AIforFootball #FootballDataAnalysis #DeepLearningInSports #AIInsights #TacticOptimisation #FootballInnovation #CoachingTools #AIEnhancedCoaching #SportPerformanceAnalysis #TacticalFootballAI #FootballTacticAI #StrategicFootballAnalysis #AIInFootballCoaching #SportDataScience 
#FootballCoach #Tactics #Football
#Geometry #GNN #GAN 
#GraphAttentionNetworks

nature.com • 4 min read

TacticAI: an AI assistant for football tactics - Nature Communications

ACQUISITIONS NEWS

DataStax's acquisition effectively completes its effort to build a one-stop generative AI stack. 

It can now offer its users a single tool that combines built-in connections to DataStax’s own Astra DB and tools like the #LangChain toolkit and #LlamaIndex for connecting different data sources, with an easy-to-use visual editor for building #GenAI chatbots for internal and external use.

#DataStax #Logspace #Langflow #GenAI #NoSQL #ApacheCassandra #MachineLearning #AIApplications #VectorSearch #AstraDB #RAG #RetrievalAugmentedGeneration #TechAcquisitions #LowCodeTools #NoCode #OpenSource #AIStack #GenerativeAI #AIIntegration #TechInnovation #DatabaseTechnology #StartupGrowth #AIDevelopment #MachineLearningTools #BusinessTechnology #TechnologyTrends #AIConsultancy #TechLeadership #ChetKapoor #RodrigoNader #GabrielLuizFreitasAlmeida #Bitvore #AIIndustry #DatabaseSolutions #AIChatbots #LangChainToolkit #LlamaIndex #DataConnections #VisualEditor #TechAcquisition #TechExpansion #AIEnhancement #BusinessApplications #EnterpriseAI #TechnologyStrategy #SoftwareDevelopment #CloudServices #DatabaseManagement #ArtificialIntelligenceApps #TechEcosystem

techcrunch.com • 2 min read

DataStax acquires the startup behind low-code AI builder Langflow | TechCrunch

Revolutionising Football Tactics: 

TacticAI for Enhanced Corner Kick Strategies

A novel application of graph neural networks and geometric deep learning to model football tactics, evaluated through quantitative benchmarks and qualitative expert feedback. The results demonstrate the potential for AI to provide valuable insights and suggestions to coaches.

Highlights:

- TacticAI uses geometric deep learning on graphs representing player positions and movements to learn efficient representations of corner kick tactics from spatio-temporal tracking data. This allows it to exploit symmetries of the football pitch for improved data efficiency.

- It has three main components: 

1) Predicting which player will receive the ball first after a corner kick
2) Predicting the probability a shot will be taken as a result of the corner  
3) Recommending adjustments to player positions and velocities to maximise or minimise shot probability

- Quantitative evaluation shows TacticAI accurately predicts corner kick receivers and shot probability. The recommended player adjustments are realistic and effective at influencing shot probability.

- A case study with expert coaches and analysts from Liverpool Football Club  provides strong evidence of TacticAI's practical utility. The generated player adjustments were judged as realistic and favored over existing tactics 90% of the time. TacticAI also serves as an effective system for retrieving similar corner kick situations.

- The authors believe TacticAI lays the groundwork for next-generation AI assistants to aid football coaches with set piece analysis and tactic development, despite limited availability of high-quality tracking data. The geometric deep learning approach allows data-efficient learning of salient tactical patterns.

#TacticAI #FootballTactics #GeometricDeepLearning #GraphNeuralNetworks #SpatioTemporalData #CornerKicks #AIinSports #PlayerPositioning #VelocityAdjustments #ShotProbability #FootballAnalytics #DataEfficiency #FootballCoaching #AIAssistants #TacticalAnalysis #SetPieces #FootballPitchSymmetry #PlayerMovements #SportScience #QuantitativeEvaluation #LiverpoolFC #FootballStrategy #AIRecommendations #FootballCoaches #TacticDevelopment #SportAnalytics #AIandSports #NextGenerationAI #FootballAnalysis #TacticalAdjustments #FootballTacticsAnalysis #PlayerAdjustments #SportTechnology #AIforFootball #FootballDataAnalysis #DeepLearningInSports #AIInsights #TacticOptimisation #FootballInnovation #CoachingTools #AIEnhancedCoaching #SportPerformanceAnalysis #TacticalFootballAI #FootballTacticAI #StrategicFootballAnalysis #AIInFootballCoaching #SportDataScience #SportScience
#FootballCoach #Tactics #Football
#Geometry #GNN #GAN

GENERATIVE AI EXPLAINABILITY

Model interpretability and explainability have become significantly more important as AI  models are used in critical domains such as healthcare and law. It is insufficient to simply make a prediction through a black-box model that hides why the model made a particular decision. 

In this work, the authors introduce new features in the open source library Captum that are specifically designed to analyse the behavior of generative LLMs. 

The authors provide an overview of the available functionalities and example applications of their potential in understanding learned associations and evaluating effectiveness of few-shot prompts within generative LLMs. 

Demonstrate examples for using perturbation and gradient-based attribution methods with
Captum which highlight the API’s flexibility on aspects such as feature definition, baseline choice and masking. This flexibility in structure allows users to generalize to a variety of cases, simplifying their ability to conduct explainability experiments on generative LLMs.

Introduce simple APIs with perturbation-based attribution methods including
Feature Ablation, Lime, Kernel SHAP and Shapley Value Sampling.

The API provides flexibility to define units of attribution as custom interpretable features which
could be individual words, tokens, phrases, or even full sentences.

Overview of Explainability Methods:

Attribution or feature importance methods output a score corresponding to each input feature’s contribution to a model’s final output.

In feature ablation, each feature is substituted with the corresponding element of the baseline feature vector to estimate their importance.

Feature Ablation is a simple and straightforward method, but the resulting attributions may not fully capture feature interactions since features are ablated individually.

Shapley Values assign scores to input features, with payouts corresponding to a feature’s contribution to the model output

LIME or Locally Interpretable Model Explanations sample points in the neighborhood of the input point X and train an interpretable model (such as a linear model) based on the results of the local evaluation.

Saliency is a gradient-based approach, using the model’s gradient at the input point as the corresponding feature attribution.

Integrated Gradients estimate attribution by computing the path integral of model gradients between the baseline point and input point.



#GenerativeAI #AIExplainability #ModelInterpretability #MachineLearning #HealthcareAI #LegalTech #LargeLanguageModels #GPT4 #OpenAI #Captum #LLMAnalysis #FeatureAblation #LIME #KernelSHAP #ShapleyValues #PerturbationMethods #GradientAttribution #APIFlexibility #CustomFeatures #ExplainabilityTools #FeatureImportance #AttributionMethods #BaselineFeatureVector #FeatureInteractions #SaliencyMaps #IntegratedGradients #LearnedAssociations #FewShotPrompts #ModelExplanations #AIInnovation


XGenAI

🐾 *Health Alert for Dog Owners!* 🐾

Luna, our 8-month-old husky has been diagnosed with glaucoma after an eye infection. This has been a shock for us, highlighting the importance of eye health is for our dogs.

👀 🐾Key Takeaways for Pet Parents:🐾

- Watch for signs like redness or rubbing.
- Get vet help at any sign of eye trouble.
- Regular vet visits are a must for early detection.
- E-collars can protect your pets from injuring their eyes further.

Let's spread the word, not just for Luna, but for all our four-legged family members. Together, we can make a difference in their lives by staying informed and proactive about their health. 🐕💕

#DogHealth #GlaucomaInDogs #EyeCareForPets #HuskyLife
#GlaucomaAwareness #PetCare #HuskyLove #EyeHealthInDogs

SOFTWARE CULTURE

By establishing a healthy error culture and managing alert systems effectively, organisations can improve their operational efficiency and foster a more supportive and productive work environment.

To avoid a negative error culture, organisations can:

1. Encourage a blame-free environment where the focus is on problem-solving rather than penalising individuals.

2. Promote transparency in discussing and documenting errors to learn from them and prevent recurrence.

3. Implement systematic processes for error management and review, including root cause analysis and post-mortem meetings.

Navigating alert and notification fatigue involves strategies to ensure that team members do not become desensitised to warnings due to their frequency or irrelevance. This can be managed by:

1. Prioritising and categorising alerts to differentiate between critical issues and minor warnings.

2. Implementing thresholds that reduce noise by filtering out non-critical alerts.

3. Regularly reviewing and adjusting alert parameters to maintain relevance and effectiveness.

4. Encouraging regular breaks and shifts in monitoring responsibilities to prevent burnout.

#ErrorCulture #BlameFreeEnvironment #Transparency #RootCauseAnalysis #NotificationFatigue #AlertManagement #TechCulture #SoftwareDevelopment #ITManagement #ProblemSolving #OperationalEfficiency #TeamSupport #PythonCommunity #RealPython #PyCodersWeekly #TechInnovation #ContinuousImprovement #LearningFromMistakes #TechEducation #SystematicProcesses #TechBurnout #CriticalIssues #EffectiveAlerts #SoftwareEngineering #DeveloperTools #TechTrends #CodingBestPractices #OrganisationalCulture #TechWorkplace #ProductiveEnvironment

https://lnkd.in/dfz8aZzB

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