Towards AI·1yHow to Deploy ML Models in Production (Flawlessly)
When deploying machine learning models in production, it is crucial to focus on reliability, scalability, security, and maintainability. Using version control systems helps track different versions of your models, ensuring you can revert to stable versions if issues arise. The post offers insights into achieving reliable deployment for ML models in production environments.
Data Engineer Things·1yBuilding Machine Learning Pipelines with the FTI Architecture: A Practical Step-by-Step Guide
FTI (Feature, Training, Inference) architecture offers a modular and scalable framework for building machine learning pipelines. It divides the workflow into three independent stages: Feature Pipeline, Training Pipeline, and Inference Pipeline. This approach ensures modularity, reusability, consistency, scalability, and reproducibility. The Feature Pipeline transforms raw data into engineered features, the Training Pipeline manages the model's lifecycle, and the Inference Pipeline serves real-time or batch predictions using the trained model.
JetBrains·1yThe State of Data Science 2024: 6 Key Data Science Trends
JetBrains and the Python Software Foundation's latest Python Developer Survey highlights key trends in data science for 2024. pandas remains the top choice for data processing, while Polars is gaining popularity. Popular data visualization tools include Plotly Dash, Streamlit, and the emerging HoloViz Panel. In the realm of machine learning, scikit-learn and PyTorch are prominent players. For MLOps, tools like Docker and TensorBoard are essential, though newer tools like MLflow are on the rise. The article also covers the importance of managing big data using tools like Spark and Databricks. PyCharm offers a range of features to support data science projects, from data processing and visualization to model deployment and integration with Hugging Face models.