DevOps, Machine Learning, Python

DevOps Service

• Continuous integration and continuous deployment (CI/CD): the process of automating the building, testing, and deployment of code changes
• Infrastructure as code (IaC): the practice of managing infrastructure using code, allowing for automated provisioning and scaling
• Monitoring and logging: the practice of collecting and analyzing data from applications and infrastructure to identify and troubleshoot issue

Machine Learning Service

• Supervised and unsupervised learning algorithms, such as linear regression, decision trees, k-means clustering, and neural networks
• Data preprocessing and feature engineering techniques, such as data cleaning, normalization, and dimensionality reduction
• Model selection and evaluation techniques, such as cross-validation and performance metrics
• Applying machine learning to real-world problems, such as image classification, sentiment analysis, and fraud detection

Python Service

• Common Python libraries and frameworks, such as NumPy, Pandas, Flask, and Django
• Writing efficient and scalable Python code
• Testing and debugging Python code
• Using Python for data analysis, machine learning, and other applications.