From 1d36483e486e286f8322fe699e9b5889b4955079 Mon Sep 17 00:00:00 2001 From: Deepak Date: Wed, 8 Jan 2025 00:07:27 +0530 Subject: [PATCH] updated docs --- docs/about-deepak-sood/intros.md | 16 +++++- .../meetups-talks-sessions.md | 19 ++++++- docs/about-deepak-sood/social-links.md | 1 + docs/ai/content-moderation.md | 57 +++++++++++++++++++ docs/ai/libraries/mlops-model-deployment.md | 11 +++- docs/ai/libraries/tools.md | 1 + docs/ai/llm/limitations-problems.md | 2 - docs/ai/llm/models.md | 3 +- docs/ai/ml-algorithms/feature-engineering.md | 14 +++++ docs/ai/nlp/chatbot-saas.md | 2 + docs/ai/readme.md | 1 + docs/cloud/aws/aws-rekognition.md | 52 +++-------------- .../marketing-mix-modeling-MMM.md | 19 +++++++ docs/courses/readme.md | 1 + docs/databases/others/technologies-tools.md | 2 + docs/databases/sql-databases/mysql/others.md | 10 ++++ docs/databases/sql-databases/mysql/readme.md | 17 +++--- docs/frontend/seo/seo-tools.md | 1 + docs/management/project-management/intro.md | 20 +++++++ 19 files changed, 191 insertions(+), 58 deletions(-) create mode 100644 docs/ai/content-moderation.md create mode 100644 docs/courses/customer-analytics-in-python/marketing-mix-modeling-MMM.md diff --git a/docs/about-deepak-sood/intros.md b/docs/about-deepak-sood/intros.md index 3cba993dd63..63a3b14ebc4 100644 --- a/docs/about-deepak-sood/intros.md +++ b/docs/about-deepak-sood/intros.md @@ -1,6 +1,6 @@ # Intros -## Intro 1 +## Intro 1 - General **Deepak Sood: Data and AI Architect | Innovator in Intelligent Systems** @@ -15,14 +15,18 @@ With over 8 years of experience in Data and AI, Deepak Sood specializes in desig Deepak’s journey is marked by his evolution from a passionate learner to an accomplished AI expert. His commitment to staying ahead in the fast-paced tech landscape is evident through constant learning and sharing insights via his blog, inspiring others to explore the transformative potential of AI. -## Intro 2 +## Intro 2 - General Deepak Sood is a seasoned Data and AI Architect with over 8 years of experience in designing intelligent, scalable systems that address complex industry challenges. Currently at OpsTree Solutions, he leads the development of advanced AI-driven solutions, leveraging his deep expertise in data architecture and AI technologies. Previously, as an Engineering Lead at Stashfin, he played a key role in scaling the company’s loan portfolio from ₹5 Cr to ₹500 Cr per month and growing the tech team from 20 to 50 members. Deepak’s career journey reflects his evolution from a passionate learner to an AI expert, marked by continuous innovation, leadership, and knowledge-sharing through his blog, inspiring others to harness the transformative power of AI. +- 116 words 751 characters + ## Intro 1 - Education Deepak Sood is an accomplished AI, Data, and DevOps Architect with over 8 years of experience across diverse fields in the IT industry. Starting his career as a software developer and data engineer, he has evolved into a versatile professional, taking on roles like product manager and collaborating with business teams to achieve impactful objectives. A proud M.Tech graduate from IIIT Delhi, Deepak has worked across multiple industries, including Media, EdTech, IoT, Crypto, FinTech, and Service-based companies, gaining a broad perspective on technology’s role in solving real-world problems. Passionate about innovation and continuous learning, he inspires young minds to explore the exciting opportunities in AI, data, and software development, showcasing how diverse roles in IT contribute to shaping the future. +- 120 words 819 characters + ## Intro 2 - Education **Deepak Sood: AI, Data, and DevOps Architect | Exploring IT’s Endless Possibilities** @@ -41,3 +45,11 @@ An CSE M.Tech graduate from IIIT Delhi, Deepak has combined his academic excelle **Career Highlights** From developing innovative software solutions to managing products and teams, Deepak has gained a 360-degree view of the IT landscape. His journey inspires students to explore the vast opportunities in technology, showing how roles in Software Development, Data Engineering, AI, and DevOps come together to shape the future of the industry. + +## Intro 1 - DevOps + +Deepak Sood is a seasoned AI, Data, and DevOps Architect with over 8 years of experience in designing, scaling, and optimizing complex systems across diverse industries, including Media, FinTech, EdTech, IoT, and Crypto. With a robust foundation in data engineering and software development, Deepak excels in implementing microservices architectures, leveraging cutting-edge technologies like Kubernetes, Elasticsearch, Kafka, Prometheus, and GraphQL to enhance scalability and efficiency. + +A proud M.Tech graduate from IIIT Delhi, he has successfully led and mentored multi-disciplinary teams, driving innovation and fostering collaboration across business and technical domains. Deepak's expertise in building resilient infrastructures and his passion for automation and optimization enable organizations to deliver agile, reliable, and impactful solutions. A natural mentor and communicator, he inspires professionals and young minds alike, emphasizing the transformative potential of technology in solving real-world challenges. + +- 133 words 1,030 characters diff --git a/docs/about-deepak-sood/meetups-talks-sessions.md b/docs/about-deepak-sood/meetups-talks-sessions.md index d7f4c24cacb..cd8d2496c0c 100644 --- a/docs/about-deepak-sood/meetups-talks-sessions.md +++ b/docs/about-deepak-sood/meetups-talks-sessions.md @@ -1,5 +1,22 @@ # Meetups / Talks / Sessions +### Kong in Action: Simplifying API Management for Modern Applications - CNCG (18 January 2024) + +In today’s rapidly evolving digital landscape, efficient API management is the cornerstone of seamless application performance, scalability, and security. Join us for an insightful session on **"Kong in Action: Simplifying API Management for Modern Applications"**, where we unravel the power of Kong as a leading API gateway and management solution. + +This session introduces Kong’s key features, including load balancing, traffic control, authentication, observability, and its plugin-driven architecture. Learn why API management is critical for modern application ecosystems and how Kong empowers developers to streamline API lifecycle management effortlessly. + +**Key Takeaways** + +- Explore real-world use cases showcasing Kong’s transformative capabilities in diverse industries. +- Understand the "why" behind API management and its role in enabling secure, scalable, and efficient integrations. +- Dive into API lifecycle management with Kong, covering design, deployment, monitoring, and iteration. +- Get actionable tips for getting started with Kong effectively, whether you're deploying on-premises or in the cloud. + +Whether you’re a developer, architect, or tech enthusiast, this session offers valuable insights into leveraging Kong to simplify API management, reduce operational overhead, and future-proof your applications. Let’s simplify API management together! + +[See API Kong-Versations:Gateway to 2025 at CNCF New Delhi](https://community.cncf.io/events/details/cncf-new-delhi-presents-api-kong-versationsgateway-to-2025/) + ### Podcast - Stream Processing using Kafka and Flink (20 December 2024) - Transcript - [Podcast - Stream Processing using Kafka and Flink](about-deepak-sood/projects/43-podcast-stream-processing-using-kafka-and-flink.md) @@ -24,7 +41,7 @@ Deepak Sood is a Senior AI, Data, and DevOps Architect with over 8 years of expe - [Simplifying API Management for modern Application , Sat, Dec 14, 2024, 11:00 AM | Meetup](https://www.meetup.com/kong-delhi/events/304930016/?slug=kong-delhi&eventId=304930016) - [Deepak Sood on LinkedIn: #kongmeetup #apimanagement #techtalks #networking #delhitechcommunity](https://www.linkedin.com/feed/update/urn:li:share:7272973093516582912/) - [Kong](devops/others/kong.md) -- [\[Meetup\] Mastering APIOps:From spec to portal with kong's Tools & Best Practices, Sat, Dec 7, 2024, 10:30 AM | Meetup](https://www.meetup.com/kong-bengaluru/events/302975712/) +- [Meetup - Mastering APIOps:From spec to portal with kong's Tools & Best Practices, Sat, Dec 7, 2024, 10:30 AM | Meetup](https://www.meetup.com/kong-bengaluru/events/302975712/) ### Neo4j Enablement Session at Opstree (12 December 2024) diff --git a/docs/about-deepak-sood/social-links.md b/docs/about-deepak-sood/social-links.md index 6034a6172c6..1f119f6cb26 100755 --- a/docs/about-deepak-sood/social-links.md +++ b/docs/about-deepak-sood/social-links.md @@ -40,6 +40,7 @@ - Instagram - https://instagram.com/deepaksood619/ - Facebook - https://www.facebook.com/deepaksood619 - Twitter - https://twitter.com/deepaksood619 + - https://x.com/deepaksood619 - Skype Username - deepaksood619@gmail.com - Github Personal - https://github.com/deepaksood619 - Gitlab Personal - [Deepak Sood · GitLab](https://gitlab.com/deepaksood619) diff --git a/docs/ai/content-moderation.md b/docs/ai/content-moderation.md new file mode 100644 index 00000000000..6fe087066a5 --- /dev/null +++ b/docs/ai/content-moderation.md @@ -0,0 +1,57 @@ +# Content Moderation + +Community Moderation / Profanity Detection (Profane) / Abusive / Toxicity Detection + +## Categories + +| **Top-Level Category** | **Second-Level Category** | +|------------------------|------------------------------| +| Explicit Nudity | Nudity | +| | Graphic Male Nudity | +| | Graphic Female Nudity | +| | Sexual Activity | +| | Illustrated Explicit Nudity | +| | Adult Toys | +| Suggestive | Female Swimwear Or Underwear | +| | Male Swimwear Or Underwear | +| | Partial Nudity | +| | Barechested Male | +| | Revealing Clothes | +| | Sexual Situations | +| Violence | Graphic Violence Or Gore | +| | Physical Violence | +| | Weapon Violence | +| | Weapons | +| | Self Injury | +| Visually Disturbing | Emaciated Bodies | +| | Corpses | +| | Hanging | +| | Air Crash | +| | Explosions And Blasts | +| Rude Gestures | Middle Finger | +| Drugs | Drug Products | +| | Drug Use | +| | Pills | +| | Drug Paraphernalia | +| Tobacco | Tobacco Products | +| | Smoking | +| Alcohol | Drinking | +| | Alcoholic Beverages | +| Gambling | Gambling | +| Hate Symbols | Nazi Party | +| | White Supremacy | +| | Extremist | + +[Moderating content - Amazon Rekognition](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html) + +## Toxic Speech Categories + +- **Profanity**: Speech that contains words, phrases, or acronyms that are impolite, vulgar, or offensive. +- **Hate speech**: Speech that criticizes, insults, denounces, or dehumanizes a person or group on the basis of an identity (such as race, ethnicity, gender, religion, sexual orientation, ability, and national origin). +- **Sexual**: Speech that indicates sexual interest, activity, or arousal using direct or indirect references to body parts, physical traits, or sex. +- **Insults**: Speech that includes demeaning, humiliating, mocking, insulting, or belittling language. This type of language is also labeled as bullying. +- **Violence or threat**: Speech that includes threats seeking to inflict pain, injury, or hostility toward a person or group. +- **Graphic**: Speech that uses visually descriptive and unpleasantly vivid imagery. This type of language is often intentionally verbose to amplify a recipient's discomfort. +- **Harassment or abusive**: Speech intended to affect the psychological well-being of the recipient, including demeaning and objectifying terms. This type of language is also labeled as harassment. + +[Detecting toxic speech - Amazon Transcribe](https://docs.aws.amazon.com/transcribe/latest/dg/toxicity.html) diff --git a/docs/ai/libraries/mlops-model-deployment.md b/docs/ai/libraries/mlops-model-deployment.md index 5351fe490ba..4b5859cdf33 100755 --- a/docs/ai/libraries/mlops-model-deployment.md +++ b/docs/ai/libraries/mlops-model-deployment.md @@ -47,6 +47,16 @@ There are mainly two ways of deploying an ML model: [MLOps guide](https://huyenchip.com/mlops/) +### MLOps Components + +- Version Control +- CI/CD +- Orchestration +- Experiment Tracking & Model Registry +- Data Lineage & Feature Stores +- Model Training & Serving +- Monitoring & Observability + ## Tools ### KubeFlow @@ -160,4 +170,3 @@ https://www.seldon.io - [End-to-End Machine Learning Project – AI, MLOps - YouTube](https://www.youtube.com/watch?v=o6vbe5G7xNo) - ZenML - MLflow -- diff --git a/docs/ai/libraries/tools.md b/docs/ai/libraries/tools.md index a20406df446..3f57b65cfaa 100755 --- a/docs/ai/libraries/tools.md +++ b/docs/ai/libraries/tools.md @@ -194,6 +194,7 @@ https://www.cortex.dev - SHAP (SHapley Additive exPlanations) - FastAPI - Docker +- [GitHub - feast-dev/feast: The Open Source Feature Store for Machine Learning](https://github.com/feast-dev/feast) ## SAAS Tools diff --git a/docs/ai/llm/limitations-problems.md b/docs/ai/llm/limitations-problems.md index fd0c93577d0..55f72e397e7 100644 --- a/docs/ai/llm/limitations-problems.md +++ b/docs/ai/llm/limitations-problems.md @@ -17,10 +17,8 @@ First find relevant information, then answer the question based on the relevant - **Not Updated to the latest information:** Generative Al uses large language models to generate texts and these models have information only to date they are trained. If data is requested beyond that date, accuracy/output may be compromised. - **Hallucinations:** Hallucinations refer to the output which is factually incorrect or nonsensical. However, the output looks coherent and grammatically correct. This information could be misleading and could have a major impact on business decision-making. - **Domain-specific most accurate information:** LLMs' output lacks accurate information many times when specificity is more important than generalized output. For instance, organizational HR policies tailored to specific employees may not be accurately addressed by LLM-based Al due to its tendency towards generic responses. - - **Source Citations:** In Generative Al responses, we don't know what source it is referring to generate a particular response. So citations become difficult and sometimes it is not ethically correct to not cite the source of information and give due credit. - **Updates take Long training time:** information is changing very frequently and if you think to re-train those models with new information it requires huge resources and long training time which is a computationally intensive task. - - Presenting false information when it does not have the answer. - Non-deterministic - same request can give different response/solution/output - Confidence is low because of hallucination diff --git a/docs/ai/llm/models.md b/docs/ai/llm/models.md index b5a3053b454..027cefd40a9 100644 --- a/docs/ai/llm/models.md +++ b/docs/ai/llm/models.md @@ -46,6 +46,7 @@ - [Llama 3 cost more than $720 million to train : r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/1cyxdgc/llama_3_cost_more_than_720_million_to_train/) - [Llama 3.1 launched and it is gooooood! - by Bugra Akyildiz](https://mlops.substack.com/p/llama-31-launched-and-it-is-gooooood) - [SQLCoder-2–7b: How to Reliably Query Data in Natural Language, on Consumer Hardware | by Sjoerd Tiemensma | Use AI | Medium](https://medium.com/use-ai/sqlcoder-2-7b-how-to-reliably-query-data-in-natural-language-on-consumer-hardware-cb352a3cf3ab) +- [DeepSeek](https://www.deepseek.com/) | Model | Parameters | Size | | ------------------ | ---------- | ----- | @@ -103,7 +104,7 @@ Emotional prompting example - You are Dolphin, an uncensored and unbiased Al ass - [Hugging Face - The AI community building the future.](https://huggingface.co/) - [sentence-transformers/all-MiniLM-L6-v2 · Hugging Face](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) -## Evaluation +## Model Evaluation - [LMSYS Chatbot Arena (Multimodal): Benchmarking LLMs and VLMs in the Wild](https://lmarena.ai/) - [Hugging Face Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) diff --git a/docs/ai/ml-algorithms/feature-engineering.md b/docs/ai/ml-algorithms/feature-engineering.md index 283c5c5f137..2d8c161490e 100755 --- a/docs/ai/ml-algorithms/feature-engineering.md +++ b/docs/ai/ml-algorithms/feature-engineering.md @@ -1,11 +1,25 @@ # Feature Engineering +## Intro + - Scale to large datasets - Find good features - Synthetic features - Preprocess with Cloud MLE - Hyperparameter tuning +## Tools + +[GitHub - feast-dev/feast: The Open Source Feature Store for Machine Learning](https://github.com/feast-dev/feast) + +Feast (**Fea**ture **St**ore) is an open source feature store for machine learning. Feast is the fastest path to manage existing infrastructure to productionize analytic data for model training and online inference. + +Feast allows ML platform teams to: + +- **Make features consistently available for training and serving** by managing an _offline store_ (to process historical data for scale-out batch scoring or model training), a low-latency _online store_ (to power real-time prediction)_,_ and a battle-tested _feature server_ (to serve pre-computed features online). +- **Avoid data leakage** by generating point-in-time correct feature sets so data scientists can focus on feature engineering rather than debugging error-prone dataset joining logic. This ensure that future feature values do not leak to models during training. +- **Decouple ML from data infrastructure** by providing a single data access layer that abstracts feature storage from feature retrieval, ensuring models remain portable as you move from training models to serving models, from batch models to realtime models, and from one data infra system to another. + ## Good vs Bad features - **Good Feature** diff --git a/docs/ai/nlp/chatbot-saas.md b/docs/ai/nlp/chatbot-saas.md index 4c610539976..ecbfbd57c2d 100644 --- a/docs/ai/nlp/chatbot-saas.md +++ b/docs/ai/nlp/chatbot-saas.md @@ -100,6 +100,8 @@ Turn your unstructured chatbot data into immediate action. Identify unhandled an https://simplify360.com/ +[Conversational AI Chatbot Software for Your Digital Assets | Engati](https://www.engati.com/) + ## Examples https://goldenpi.com diff --git a/docs/ai/readme.md b/docs/ai/readme.md index a98eec50c04..7f8927239af 100755 --- a/docs/ai/readme.md +++ b/docs/ai/readme.md @@ -19,6 +19,7 @@ - [Others / Resources / Interview / Learning](ai/others-resources-interview-learning-courses.md) - [Hackathons](ai/hackathons.md) - [Solutions](ai/solutions.md) + - [Content Moderation](ai/content-moderation.md) - [Social Media Analytics Solution](ai/social-media-analytics-solution.md) ## Data & AI Landscape diff --git a/docs/cloud/aws/aws-rekognition.md b/docs/cloud/aws/aws-rekognition.md index 53350697c34..d63b292c641 100755 --- a/docs/cloud/aws/aws-rekognition.md +++ b/docs/cloud/aws/aws-rekognition.md @@ -1,53 +1,19 @@ # AWS Rekognition -## Moderating Content / Content Moderation / Community Moderation +Automate and lower the cost of your image recognition and video analysis with ML + +Amazon Rekognition is a cloud-based image and video analysis service that makes it easy to add advanced computer vision capabilities to your applications. The service is powered by proven deep learning technology and it requires no machine learning expertise to use. Amazon Rekognition includes a simple, easy-to-use API that can quickly analyze any image or video file that’s stored in Amazon S3. + +You can add features that detect objects, text, unsafe content, analyze images/videos, and compare faces to your application using Rekognition's APIs. With Amazon Rekognition's face recognition APIs, you can detect, analyze, and compare faces for a wide variety of use cases, including user verification, cataloging, people counting, and public safety. + +The service is based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists, technology that can analyze billions of images and videos daily. Rekognition routinely learns from new data, and we frequently add new labels and features to the service. + +## Content Moderation You can use Amazon Rekognition to detect content that is inappropriate, unwanted, or offensive. You can use Rekognition moderation APIs in social media, broadcast media, advertising, and e-commerce situations to create a safer user experience, provide brand safety assurances to advertisers, and comply with local and global regulations. Today, many companies rely entirely on human moderators to review third-party or user-generated content, while others simply react to user complaints to take down offensive or inappropriate images, ads, or videos. However, human moderators alone cannot scale to meet these needs at sufficient quality or speed, which leads to a poor user experience, high costs to achieve scale, or even a loss of brand reputation. By using Rekognition for image and video moderation, human moderators can review a much smaller set of content, typically 1-5% of the total volume, already flagged by machine learning. This enables them to focus on more valuable activities and still achieve comprehensive moderation coverage at a fraction of their existing cost. To set up human workforces and perform human review tasks, you can use Amazon Augmented AI, which is already integrated with Rekognition. -| **Top-Level Category** | **Second-Level Category** | -|------------------------|------------------------------| -| Explicit Nudity | Nudity | -| | Graphic Male Nudity | -| | Graphic Female Nudity | -| | Sexual Activity | -| | Illustrated Explicit Nudity | -| | Adult Toys | -| Suggestive | Female Swimwear Or Underwear | -| | Male Swimwear Or Underwear | -| | Partial Nudity | -| | Barechested Male | -| | Revealing Clothes | -| | Sexual Situations | -| Violence | Graphic Violence Or Gore | -| | Physical Violence | -| | Weapon Violence | -| | Weapons | -| | Self Injury | -| Visually Disturbing | Emaciated Bodies | -| | Corpses | -| | Hanging | -| | Air Crash | -| | Explosions And Blasts | -| Rude Gestures | Middle Finger | -| Drugs | Drug Products | -| | Drug Use | -| | Pills | -| | Drug Paraphernalia | -| Tobacco | Tobacco Products | -| | Smoking | -| Alcohol | Drinking | -| | Alcoholic Beverages | -| Gambling | Gambling | -| Hate Symbols | Nazi Party | -| | White Supremacy | -| | Extremist | - -Abusive - -profane words - ## Amazon Transcribe [Amazon Transcribe](https://aws.amazon.com/transcribe/) is an automatic speech recognition (ASR) service that makes it easy for you to add speech-to-text capabilities to your applications. Starting today, when transcribing audio streams, you can instruct Amazon Transcribe to automatically mask, remove, or tag specific terms in the transcription results based on a vocabulary that you specify. For example, you can use a vocabulary filter to automatically remove profane words from the transcription results for content moderation or generating family-friendly captions. You can create a vocabulary filter once and use it when processing multiple audio streams. You can also create multiple vocabulary filters and choose which one should be used for a particular audio stream. With this launch, vocabulary filtering is now available for both Amazon Transcribe's batch and streaming transcription APIs. diff --git a/docs/courses/customer-analytics-in-python/marketing-mix-modeling-MMM.md b/docs/courses/customer-analytics-in-python/marketing-mix-modeling-MMM.md new file mode 100644 index 00000000000..d789926a1ca --- /dev/null +++ b/docs/courses/customer-analytics-in-python/marketing-mix-modeling-MMM.md @@ -0,0 +1,19 @@ +# Marketing mix modeling (MMM) + +Marketing mix modeling (MMM) is a statistical analysis technique that uses sales and marketing data to measure the impact of marketing activities on sales. It's a data-driven tool that helps marketers: + +- **Improve media performance** - MMM can help marketers understand the impact of their marketing and brand investments.  +- **Optimize future marketing plans** - MMM can help marketers predict the impact of future marketing efforts and adjust spending on in-flight campaigns. +- **Maximize ROI** - MMM can help marketers optimize advertising mix and promotional tactics to maximize their return on investment.  + +MMM uses statistical models, such as multivariate regressions, to analyze sales and marketing time-series data. It can also include multi-level analysis to provide a more comprehensive view of how marketing activities influence outcomes.  + +When selecting a modeling technique for MMM, you can consider things like:  + +- **Data complexity**: Whether the data is simple or complex, and whether it has non-linear patterns +- **Model interpretability**: How easy it is to understand the model and its drivers +- **Data availability**: Whether the data required for the technique is available +- **Resource constraints**: Whether you have the computing power and expertise to implement and maintain the model +- **Business objectives**: Whether the technique aligns with your business objectives + +[Market Mix Modeling (MMM) — 101. A primer on Market Mix Modeling. | by Ridhima Kumar | Towards Data Science](https://towardsdatascience.com/market-mix-modeling-mmm-101-3d094df976f9) diff --git a/docs/courses/readme.md b/docs/courses/readme.md index 18dff7cd360..35edc200bc6 100755 --- a/docs/courses/readme.md +++ b/docs/courses/readme.md @@ -26,6 +26,7 @@ 5. [Credit Risk Modeling in Python](courses/course-credit-risk-modeling/syllabus.md) 6. [Time Series Analysis in Python](courses/course-time-series-analysis/syllabus.md) 7. [Customer Analytics in Python](courses/customer-analytics-in-python/syllabus.md) + 1. [Marketing mix modeling (MMM)](courses/customer-analytics-in-python/marketing-mix-modeling-MMM.md) 8. [Mathematics](courses/365-ds-mathematics.md) ## Others diff --git a/docs/databases/others/technologies-tools.md b/docs/databases/others/technologies-tools.md index bf2567471ee..1fae0a94191 100755 --- a/docs/databases/others/technologies-tools.md +++ b/docs/databases/others/technologies-tools.md @@ -108,3 +108,5 @@ Jepsen is an effort to improve the safety of distributed databases, queues, cons [Rapydo | Cloud Database Automation](https://www.rapydo.io/) [Cloud Data Management Solution, AWS Backup and Recovery | NIMESA](https://nimesa.io/) + +[Sequel Pro](https://sequelpro.com/) diff --git a/docs/databases/sql-databases/mysql/others.md b/docs/databases/sql-databases/mysql/others.md index aaa309e6481..f618f946171 100755 --- a/docs/databases/sql-databases/mysql/others.md +++ b/docs/databases/sql-databases/mysql/others.md @@ -66,6 +66,16 @@ MariaDB intended to maintain high compatibility with MySQL, ensuring a drop-in r Its lead developer/CTO is [Michael "Monty" Widenius](https://en.wikipedia.org/wiki/Michael_Widenius), one of the founders of [MySQL AB](https://en.wikipedia.org/wiki/MySQL_AB) and the founder of Monty Program AB. On 16 January 2008, MySQL AB announced that it had agreed to be acquired by [Sun Microsystems](https://en.wikipedia.org/wiki/Sun_Microsystems) for approximately $1 billion. The acquisition completed on 26 February 2008. Sun was then bought the following year by Oracle Corporation. MariaDB is named after Monty's younger daughter, Maria. (MySQL is named after his other daughter, My.) +Editors + +- MySQLWorkbench +- Windows - [Database Workbench - MariaDB Knowledge Base](https://mariadb.com/kb/en/database-workbench/) +- [Sequel Pro](https://sequelpro.com/) +- [Heidi Sql](http://www.heidisql.com/) +- [SQLyog](https://www.webyog.com/) + +[mariadb-report - MariaDB Knowledge Base](https://mariadb.com/kb/en/mariadb-report/) + ## MySQL 5 vs MySQL 8 https://mysqlserverteam.com/whats-new-in-mysql-8-0-generally-available diff --git a/docs/databases/sql-databases/mysql/readme.md b/docs/databases/sql-databases/mysql/readme.md index 8d6ceafa626..7cfcf6aa4b1 100755 --- a/docs/databases/sql-databases/mysql/readme.md +++ b/docs/databases/sql-databases/mysql/readme.md @@ -18,18 +18,19 @@ - [Thread States](databases/sql-databases/mysql/thread-states.md) - [SQL / MySQL Tools](sql-mysql-tools) - [Percona Toolkit](databases/sql-databases/mysql/percona-toolkit.md) -- Backup - - [Backup Types](databases/sql-databases/mysql/backup-types.md) - - [Backup Policy](databases/sql-databases/mysql/backup-policy.md) - - [Backup Comparisons](databases/sql-databases/mysql/backup-comparisons.md) - - [mysqldump](databases/sql-databases/mysql/mysqldump.md) - - [mydumper](databases/sql-databases/mysql/mydumper.md) - - [percona-xtrabackup](databases/sql-databases/mysql/percona-xtrabackup.md) - [Others](databases/sql-databases/mysql/others.md) -[B.3.7 Known Issues in MySQL](https://dev.mysql.com/doc/refman/8.4/en/known-issues.html) +### Backup + +- [Backup Types](databases/sql-databases/mysql/backup-types.md) +- [Backup Policy](databases/sql-databases/mysql/backup-policy.md) +- [Backup Comparisons](databases/sql-databases/mysql/backup-comparisons.md) +- [mysqldump](databases/sql-databases/mysql/mysqldump.md) +- [mydumper](databases/sql-databases/mysql/mydumper.md) +- [percona-xtrabackup](databases/sql-databases/mysql/percona-xtrabackup.md) ### Others - [Amazon Aurora](databases/sql-databases/aws-aurora/readme.md) - [Amazon RDS](databases/sql-databases/amazon-rds.md) +- [B.3.7 Known Issues in MySQL](https://dev.mysql.com/doc/refman/8.4/en/known-issues.html) diff --git a/docs/frontend/seo/seo-tools.md b/docs/frontend/seo/seo-tools.md index 7099876f137..91ae058b373 100644 --- a/docs/frontend/seo/seo-tools.md +++ b/docs/frontend/seo/seo-tools.md @@ -44,6 +44,7 @@ - [Free Website Speed Test | Testing And Monitoring](https://www.debugbear.com/test/website-speed) - [Website Speed Test | Pingdom Tools](https://tools.pingdom.com/) +- [PageSpeed Insights](https://pagespeed.web.dev/) ## Tools diff --git a/docs/management/project-management/intro.md b/docs/management/project-management/intro.md index bb877962f3b..c1c72e103b6 100755 --- a/docs/management/project-management/intro.md +++ b/docs/management/project-management/intro.md @@ -210,6 +210,26 @@ A business sends an RFP when they need more information about a product or servi - https://blog.trello.com/50-project-management-terms-you-should-know - https://blog.trello.com/most-common-project-blockers +## Project Management Delivery + +If you are leading a project, your only responsibility is to ensure it is delivered, whatever it takes. Here are a few pointers that I have followed + +1. avoid being blocked, always find a way out +2. if there is a chance of a delay, communicate early +3. always look for trade-offs and make sure we pick the right one +4. estimate timelines well; good estimation reduces chaos +5. influence others so that they prioritize our tasks +6. always reiterate key details to ensure alignment, there is no such thing as over-communication. + +On the technical and execution side, here's what I ensure + +1. form a deep understanding and high clarity about the project +2. create a solid plan, reduce ambiguity, and keep the team focused +3. be agile, monitor progress, revise plan if required +4. make sure every single person involved in the project is aligned + +Delivering a project requires very high focus, clarity, and persistence. Keep the big picture in mind, but execute with attention to detail. + ## Links [The Complete Project Management Body of Knowledge in One Video (PMBOK 7th Edition) - YouTube](https://www.youtube.com/watch?v=2gmCr40uT4U&ab_channel=DavidMcLachlan)