Timescale’s Post

𝐒𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩, 𝐦𝐚𝐧𝐲 𝐚 𝐥𝐢𝐭𝐭𝐥𝐞 𝐦𝐚𝐤𝐞𝐬 𝐚 𝐦𝐢𝐜𝐤𝐥𝐞. When I recall the past 3 months, this time is filled with continuous practice 👩💻 , exploration with curiosity 🤓 , and support from peers 🤝 in the same cohort. Today, I am proud that I have passed the final project evaluation and got certified by DataTalksClub for the 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐙𝐨𝐨𝐦𝐜𝐚𝐦𝐩. 🎉 A heartfelt 𝐓𝐡𝐚𝐧𝐤 𝐲𝐨𝐮 👍 to all the instructors Michael Shoemaker, MBA, Matt Palmer, Luis Oliveira, Victoria Perez Mola, Ankush Khanna, Adrian Brudaru, Noel Kwan, and particularly Alexey Grigorev, you have no idea how confused I was when I wanted to dive into Data Engineering before. Each week, I was pretty satisfied with the comprehensive tutorials and hidden production strategy. 🙌 Data Warehousing, Orchestration, Analytics Engineering, dbt, Mage, dlt, Spark, Docker, Terraform, Commands, Python, SQL, etc. Some technologies were often heard in Meetups and now I am happy I am becoming friends with them. 😊 Learning never stops, this is one time when I am 100% interested in and pushed by my passion 😍 . See you soon in the Data Engineering World. 😘

Currently going through the survey results from Kubernetes Community Days Zurich! Thank you to the 87 people who helped us improve our event. 🙌 Some feedback was a given, some was new, and some issues from last year didn’t come up again because we’ve already made improvements! Here are some highlights and questions for our community: 🌟 Highlights: - Overall Event Rating: 4.4 ⭐⭐⭐⭐ - 95% are somewhat or very likely to attend our future events ⭐⭐⭐⭐ 📋 Feedback and Questions: - There’s still a debate between more vegan and more meat options. Should we consider vegan/meat instead of vegetarian next time to make everyone happy? - We will most certainly host KCD Rejects next year, but it will be very technical, aimed at those who felt the main event sessions weren’t technical enough. 💻🔧 - The biggest issue was, once again, the food queues (I agree!). But don’t worry. Viktor Farcic had a great idea for solving this next time! 🍽️🕒 Thank you all for your invaluable feedback and support. Let’s make the next KCD Zürich even better! 💪✨

🌍 Exciting News from the Hack4BioHeritage Hackathon! 🐾 I'm thrilled to share that our team, Team-XO, has successfully developed EcoExplorer, a groundbreaking portal designed to classify animals based on their images and predict the conservation status of various species using key characteristics like height, weight, average speed, diet, lifespan, and gestation period. This innovative solution leverages the power of machine learning to aid in the conservation of wildlife. Tech Stack: Flask, jQuery, CSS, JavaScript, TensorFlow, HTML Future Scope: 1. We are implementing long-term forecasting for strategic planning. 2. We are enhancing model interpretability with explainable AI techniques. 3. We are integrating with cloud platforms and weather APIs. A big thank you to my teammates Vaibhav Chhillar, Ansh Kant, and Virat Samdarshi for their dedication and hard work. Together, we’re making strides toward a more sustainable future. #Hack4BioHeritage #MachineLearning #WildlifeConservation #EcoExplorer #AI #Innovation #TeamWork #Biodiversity #Sustainability

Hello connections.I am here to share Task 2 of my domain DataScience.I am very thankful to #codsoft for this great opportunity. Task2 Title IRIS FLOWER CLASSIFICATION. The Iris flower dataset consists of three species: setosa, versicolor, and virginica. These species can be distinguished based on their measurements.My objective is to train a machine learning model that can learn from thesemeasurements and accurately classify the Iris flowers into their respective species.

Hey Everyone!! Happy to introduce all of you to my new project. 1.This is a user friendly web page which has been integrated with Machine Learning model which is used to predict multiple diseases, which predicts the occurrence of disease based on user input. This project uses Support Vector Machine Algorithm to predict the values. 2.The second project focuses on predicting salary based on the experience of the person. This project uses Linear Regression to predict the values.

Excited to share my Portfolio!🚀 From diving into insightful exploratory data analysis to implementing machine learning models, every project has been a journey of learning and growth. Check it out to explore the power of data-driven insights: samsonayankunle.github.io #datascience #dataanalytics #portfolio #machinelearning

🎉 Flyte has reached 4k #Github stars! ⭐ We are so grateful to our amazing community of #Flyte users and contributors who have supported us along the way. If you are interested in learning more about Flyte, or want to join our growing community, visit our Github repo here 👉 https://lnkd.in/g6sdBDdv And if you like what you see 👀 , please show your support by starring ⭐ the repo!

We are excited to announce huggingface-langchain 🚀 A new open-source package to seamlessly integrate the latest open Models from Hugging Face into LangChain, supporting local models hosted models! 🤗🦜 TL;DR: 🛠️ Easy Installation: Install with a simple `pip install langchain-huggingface`. 🚪 Open Models: Easily access open LLMs and Embedding models. 🌐 Flexibility: Utilize Hugging Face models via API, Inference Endpoints, or self-hosted Text Generation Inference. 💬 Chat Models: Support conversational models using ChatHuggingFace. 🧠 Embeddings: Leverage sentence-transformers embeddings locally or via Hugging Face endpoints. 🏎️ Fast Integration: Models can be loaded directly using the from_model_id method or by defining the pipeline manually. Get started here: https://lnkd.in/eNvd3SqW Big Kudos to Harrison Chase, Erick Friis, Kirill Kondratenko, Joffrey THOMAS, Andrew Reed, Aymeric Roucher for this big release and the awesome integration! 🤗🦜

Thanks for this milestone on my personal YouTube channel Data Science Demonstrated! Great to see traction in learning AI and data science using demos and real life examples ! https://lnkd.in/eVQFzHJa #datascience #artificialintelligence

We got API access to Groq; thanks, guys! Very excited to test out their LPU Inference Engine; it's supposed to be fast (it's really fast in the playground). And all the open-source models. Ps this is with a Q, not the same as Elon’s Grok #groq #artificialintelligence