🚀 Diving into the MLOps Tech Stack 🚀

🌐 Introduction to MLOps:

MLOps is the integration of machine learning with best DevOps practices. It streamlines the ML lifecycle, including model development, training, deployment, and monitoring. By automating these processes, teams can deploy ML models more efficiently.

🧰 Key Components of the MLOps Stacks:

1. Model Development & Training:

• Amazon SageMaker: A comprehensive AWS service that simplifies the building, training, and deployment of ML models.
• Frameworks: Tools like Caffe, PyTorch, and Keras offer extensive libraries for developing ML models.
• Jupyter: Widely used IDE for Python, perfect for interactive ML development.
• Hyperparameter Tuning: Utilize tools like tune to optimize your model's performance.

2. Collaboration & Versioning:

• Domino DA & iterative: MLOps platforms that bolster collaboration and automate ML processes.
• lakeFS: Offers version control for data lakes, ensuring changes are traceable.
• Labelling: Essential for annotating data, which in turn, trains ML models.

3. Data Management:

• Pandas: Esteemed Python…