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Deploy Scikit-Learn & TensorFlow Models with FastAPI
Data Science With FastapiIn the previous lesson, we explored how to deploy machine learning models using FastAPI. We learned how to set up a basic FastAPI app, integrate a simple model, and handle API requests. Now, we’ll take it a step further by deploying more advanced models trained with Scikit-Learn and TensorFlow....Read more
Deploy Machine Learning Models with FastAPI: A Step-by-Step Guide
Data Science With FastapiIn the previous lesson, we explored how to handle data with FastAPI and Pandas. We learned how to process, clean, and serve data using FastAPI endpoints, which is a crucial step before deploying machine learning models. Now, we’ll take it a step further and dive into deploying machine learning...Read more
Handling Data with FastAPI and Pandas: CSV, JSON, and DataFrames
Data Science With FastapiIn the previous lesson, we explored how to create your first FastAPI endpoint. We built a simple API that returned a message, and we learned how to set up routes and run the server. Now, we will dive deeper into handling data, which is a core part of building data science applications. In this...Read more
Creating Your First FastAPI Endpoint with Pydantic
Data Science With FastapiIn the last lesson, we set up FastAPI for data science projects. We installed FastAPI, configured the environment, and created a basic app structure. Now, it's time to dive deeper and create our first FastAPI endpoint. This lesson will teach you how to handle requests and responses using...Read more
Setting Up FastAPI for Data Science Projects
Data Science With FastapiIn the previous lesson, we explored what FastAPI is and why it's a great choice for building data science applications. We learned that FastAPI is fast, easy to use, and works well with Python libraries like Pandas and Scikit-Learn. Now, it's time to dive into the practical side of things. In...Read more
Why FastAPI is the Best Framework for Data Science Applications
Data Science With FastapiIn the first lesson of our course, Building Data Science Applications with FastAPI, we dive into what FastAPI is and why it stands out as a top choice for data science projects. If you're new to FastAPI, this lesson will help you understand its core features, how it compares to Flask and Django,...Read more
Deploy Machine Learning Models to Google Cloud, AWS, and Heroku
Python Machine LearningIn the previous lesson, we explored how to serve machine learning models using Flask and FastAPI. These tools helped us create APIs that could run predictions locally. However, when it comes to real-world use, we need to deploy models to the cloud for better scalability and reliability. This...Read more
Deploy Machine Learning Models with Flask and FastAPI
Python Machine LearningIn the previous lesson, we learned how to export trained machine learning models using TensorFlow and Scikit-Learn. This step is crucial because it allows us to save our models in a format that can be reused later. Now, we will take the next step: deploying these models as web services. This is...Read more
Export Machine Learning Models with TensorFlow & Scikit-Learn
Python Machine LearningIn the last lesson, we explored text classification using BERT, a powerful transformer model that excels in understanding context in text data. We built a model that could classify text into categories, which is a common task in natural language processing. Now, it's time to take the next step:...Read more
