Tech Tips, Tricks & Trivia

There are many ways to visualise data . Below are some popular and free Data Visualization tools - Tableau Public is a free platform to explore, create and publicly share data visualizations online. Shark Tank India data set and analysis.  Tableau Public’s Viz of the Day highlights data visualizations created by members of the Tableau Community.  Datawrapper is a data visualization tool for everyone who wants to show their data in beautiful charts, maps, and tables. It has a free plan to publish charts, maps, and tables. TimelineJS  is an open-source tool from Northwestern University's Knight Lab that enables anyone to build interactive timelines.  RAWGraphs  is an open source data visualization framework. It was conceived as tool to produce semifinished visualizations and it provides tidy and open vector files that can be easily edited in any vector graphics editor. FastCharts is a tool from The Financial Times to create simple charts.  Chart on climate change generated with

This Week I Learned -  * MySQL HeatWave is a massively parallel, high performance, in-memory query accelerator that accelerates MySQL performance by orders of magnitude for analytics and mixed workloads. MySQL HeatWave on AWS is a fully managed  service that can help you create and manage a MySQL DB System with a HeatWave Cluster to use with AWS Applications. * Device42 Warranty Sync feature allows customers to take the discovered hardware devices in their environment and sync into Device42 the warranty and support contract information.  This helps in understanding which hardware is in support, what is coming up for expiration, and what devices are out of support all in one simple report.  You can also use this information to create automated email reports to alert you when hardware is coming up on expiration so you can take action to renew it or move off of that hardware platform. Device42 supports most major manufacturers like Dell, Lenovo, HP, IBM, and Meraki. *  A curated list of f

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How to Build a Career in AI is a 41 page ebook with great insights, advice and suggestions from Andrew Ng, the founder of DeepLearning.AI and Coursera. Highlights and excerpts: Keeping up-to-date with changing technology is more important in AI than fields that are more mature. The highly iterative nature of AI projects leads to special challenges in project management: How can you come up with a plan for building a system when you don’t know in advance how long it will take to achieve the target accuracy? Even after the system has hit the target, further iteration may be necessary to address post-deployment drift. Exploratory Data Analysis (EDA) — using visualizations and other methods to systematically explore a dataset — is particularly useful in data-centric AI development. The best way to build a new habit is to start small and succeed, rather than start too big and fail.  Five steps to help you scope projects: Identify a business problem (not an AI problem). Ask - “What are th

This Week I Learned -  *  Cloud Product Mapping (AWS vs Azure vs GCP vs OCI) * Although vector databases have been around long before ChatGPT, they have become an integral part of the GenAI technology stack, as vector databases can address some of LLMs’ key limitations, such as hallucinations and lack of long-term memory * Vector databases store and provide access to structured and unstructured data, such as text or images, alongside their vector embeddings. Vector embeddings are the data’s numerical representation as a long list of numbers that captures the original data object’s semantic meaning. Usually, machine learning models are used to generate the vector embeddings *  Django web applications can be run on almost any machine that can run the Python 3 programming language like Windows, macOS, Linux/Unix. For Django 4.2 the allowed versions are Python 3.8 to 3.11. Django officially supports the PostgreSQL, MariaDB, MySQL, Oracle, and SQLite databases, and there are community lib

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This Week I Learned -  *  Kaggle's 2023 AI Report is a collection of 20+ essays written by Kaggle community members covering the latest AI advancements and salient topics in modern ML. *  Digital Rights Management (DRM) is technology that enables online video and audio services to enforce that the content they provide is used in accordance with their requirements. This technology may restrict some of the things you can do in the browser. Many services are moving towards HTML5 video that requires a different DRM mechanism called a Content Decryption Module (CDM).  * Firefox for desktop supports the Google Widevine CDM for playing DRM-controlled content. Firefox downloads and enables the Google Widevine CDM by default to give users a smooth experience on sites that require DRM. The CDM runs in a separate container called a sandbox, and you will be notified when a CDM is in use.  *  Lossless Cut is probably the simplest way to cut out parts of a video without having to re-encode it

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This Week I Learned -  *  CNCF Cloud Native Glossary *  4 typical models with which open-source companies operate : Open core Professional services Hosting Marketplace * Red Hat’s subscription business started as primarily a “support subscription” but evolved into a product offering over time as companies like IBM started to undercut Red Hat for support services (IBM has since acquired Red Hat).  * Mozilla generates the bulk of their $500m annual revenue by providing lead generation to search engines. * Generative models can simulate the aging process of a face, which can be useful for matching faces across different ages. For example, a missing child's photo can be age-progressed to generate an image of what they might look like now. Besides Age Progression/Regression, Generative models can be used to generate privacy-preserving representations of faces. For example, facial images can be transformed into a privacy-preserving format, such as facial embeddings, while still enabling

Project Management Institute (PMI) is offering a Foundational level online course  Generative AI Overview for Project Managers  that can fetch 5 Professional Development Units (PDUs) for certifications holders.  The course is a hands-on learning experience for project professionals that will empower you with practical knowledge of AI in project management that you can apply right away to your project management approaches. What is covered:     Definitions of Artificial Intelligence (AI) and Generative AI     Understand Generative AI applications in project management     Discover the AI project manager tools that can use to maximize efficiency     Learn best practices for project management in AI as a way of working Some useful content from the course Resources : DO’s for Project Practitioners Using GenAI: Use AI to automate repetitive tasks to increase efficiency Use AI to make informed decisions based on large data sets Use AI to recommend corrective actions to keep a project on trac

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