And i finally got the accesss to my blog back..

-
will start to blog again...

Comments

Post a Comment

Popular posts from this blog

-
Image
https://www.youtube.com/watch?v=q8d9uuO1Cf4   Data Science Stack: A Quick Overview In data science, we use various tools and techniques to work with data. Here's a breakdown: Hardware: Storage: We store data in data warehouses (structured) or data lakes (unstructured). Processing: We use GPUs (graphic processing units) to process data. Software: Programming Languages: Python is the most popular language, followed by JavaScript for simpler applications. Rust and Mojo are newer options. Libraries: Python has many libraries like Pandas, NumPy, scikit-learn, TensorFlow, PyTorch, Keras, and Matplotlib to help with data analysis, machine learning, and visualization. Development Environments: We use IDEs like Jupyter Notebook and PyCharm to write and run code. Version Control: We use tools like GitHub to manage different versions of our code. Data Exchange: We use JSON format to exchange data between systems. Deployment: Centralized: Model is hosted on a server and acces...
Read more
-
Image
  Data Analytics and Machine Learning in Digital Transformation 1. The Role of Data Analytics and Machine Learning: Digital transformation creates value by transforming data into useful information . Internal data is gathered through IoT sensors , while external data is obtained from system APIs . Analytics and machine learning are critical for processing this data, enabling smart products and operations . 2. How Data Analytics and Machine Learning Work: Data Analytics combines traditional statistics and modern machine learning to analyze data. Linear Regression is an example that can function as both a statistical and machine learning method: Statistics : A one-time calculation. Machine Learning : Continuously trained with large datasets to make predictions. Example: A model can predict tire blowouts by analyzing tire pressure and miles driven. The system uses linear regression to create a model: Y = M X + B Y = MX + B Y = MX + B , where: Y = Ti...
Read more
-
 The evolution of Continuous Integration/Continuous Delivery (CI/CD) and continuous delivery was not the result of a single genius but a gradual, industry-wide shift driven by the need for better software delivery practices. In the early days, automation was seen as a luxury due to limited tools and infrastructure, making automated deployments challenging. However, over the past 15 years, advancements like Git, AWS, Heroku, GitHub, and Docker transformed software development, enabling seamless CI/CD workflows. This shift wasn’t a sudden revolution but a natural progression driven by developers and toolmakers striving for efficiency. Continuous delivery has replaced outdated, siloed approaches, bringing consistency, collaboration, and pride to software development. Automation not only simplifies workflows but also enhances productivity and team alignment. Today, CI/CD is essential for delivering functional software quickly and reliably, highlighting the importance of automation ...
Read more