Loka’s Post

Do you love solving puzzles with data? Become a Machine Learning Engineer! Machine learning engineers are like data detectives who build cool tech using special algorithms. They take mountains of information and turn it into something amazing, like: 📌Super smart recommendations on your favorite shopping sites 📌Voice assistants that understand exactly what you're saying ️ 📌Self-driving cars that navigate the road safely If you're good at math, coding, and thinking creatively, this might be the perfect job for you! #EliteeXceed #MachineLearningEngineer #AI #TechCareers

You are not a Machine Learning engineer until you have: 🤧 Ran out of memory when the model was just about to converge 😤 Begged Data Scientists to stop mentioning Langchain 🥲 Pulled your hair multiple times trying to integrate all tools for your ML chain ☠️ Learned that your dependencies have been updated and are no longer supported, so now you have to redo everything 🫠Spent days running grid search or random search only to find minimal performance improvements  😫 Constantly wondered why your setup works perfectly in the development environment but never in production 😤 Struggled to explain to a non-technical stakeholder why your complex models behave the way they do ... What else makes you 'love' your job?

How can I become a machine learning engineer?

Attn: AI Software Engineer!

Attn: AI Software Engineer!

A Roadmap to Becoming a Machine Learning Engineer

Title: MLOps Platform for Data Scientists and ML Engineers Summary: In today's fast-paced tech landscape, the integration of machine learning

As A Machine Learning Engineers Should Know.

🔥 GroupBy 1️⃣ 9️⃣ : Worth consuming content in Data, Engineering, AI, ML, and Data Science, Week 15-21, January: 🚀 A side project to get your hands dirty: Build Machine Learning Pipelines with Airflow and Mlflow: Reservation Cancellation Forecasting From 🚀 Jeremy ARANCIO (thank 🚀 Jeremy ARANCIO for this fantastic project) 🧠 Highlight resource: - How Apple built iCloud to store billions of databases - Uber | Palette feature store - Definition of Data Modeling - Linkedin | Improving Recruiting Efficiency with a Hybrid Bulk Data Processing Framework - And more... Get all the links to these resources here: https://lnkd.in/gh8PVUup Hope you enjoy this week's issue. #dataengineer #dataengineering --- GroupBy is a weekly newsletter for data engineering that contains worthwhile content in Data, Engineering, AI, ML, and Data Science, which I compiled by myself. If you want to receive weekly issues, subscribe here: https://lnkd.in/gDjFNmK6

I'm excited to announce that I have completed Cisco's "Introduction to Data Science" course! This marks my first step into the expansive field of data science, and I gained a foundational understanding of several critical concepts and principles that underpin the discipline. One of the key takeaways from the course was exploring the essential dimensions of data, including Volume, Variety, Velocity, and Veracity. These aspects are crucial in understanding the complexity and characteristics of big data. I also acquired knowledge about pivotal technologies such as Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI). These technologies encompass both ML and DL, aiming to create systems capable of performing tasks that would typically require human intelligence. The course highlighted the diverse career opportunities in data science, ranging from data analysts and data engineers to data scientists and AI specialists. These roles are pivotal in transforming data into actionable insights and driving decision-making processes across industries. This comprehensive introduction has equipped me with a solid foundation in data science, and I'm excited to continue my journey and delve deeper into this dynamic and rapidly evolving field. #DataScience #MachineLearning #ArtificialIntelligence #DeepLearning #Cisco #Skillsforall