Enroll Course: https://www.udemy.com/course/machine-learning-deep-learning-model-deployment/
Have you ever built a fantastic Machine Learning or Deep Learning model, only to be stumped on how to actually get it out into the real world? You’re not alone! Many aspiring data scientists and ML engineers focus heavily on model development, but the crucial step of deployment is often overlooked. That’s where Udemy’s ‘Machine Learning Deep Learning Model Deployment’ course shines.
This comprehensive course takes you on a journey far beyond the Jupyter notebooks, equipping you with the practical skills needed to make your models accessible and usable by various applications. It’s designed for beginners, even those with no prior ML/DL experience, and starts with fundamental Python and Scikit-learn concepts before diving into the exciting world of deployment.
The course structure is incredibly well-thought-out. You’ll learn to create classification models, save them along with essential components like scalers, and then export them to different environments, including local setups and Google Colab. The hands-on approach really comes to life as you build REST APIs using Python Flask, deploy them on cloud virtual servers, and even explore serverless options with Cloud Functions. For those working with popular frameworks, the course covers deploying TensorFlow and Keras models using TensorFlow Serving, and PyTorch models, including converting them to TensorFlow format via ONNX. You’ll even get to deploy sentiment analysis models for Twitter and explore model deployment with TensorFlow.js and JavaScript.
What truly sets this course apart is its forward-thinking inclusion of Generative AI. The latter part of the course delves into OpenAI, the history of GPT models, and practical applications like invoking text-to-speech, chat completion, text generation, and image generation models from Python. Building a chatbot with the OpenAI API and ChatGPT is a highlight, offering a glimpse into the future of AI. You’ll also gain insights into Large Language Models (LLMs) and the art of prompt engineering.
While a Google Cloud (GCP) free trial account is recommended for some of the cloud-based labs, the foundational knowledge and many of the practical examples can be followed without it. The course also touches upon tracking model training experiments and deployments with MLFlow, running it on Colab and Databricks, which is invaluable for managing the ML lifecycle.
If you’re looking to bridge the gap between building models and making them actionable, I highly recommend this ‘Machine Learning Deep Learning Model Deployment’ course on Udemy. It’s an investment that will significantly boost your skills and marketability in the ever-evolving field of artificial intelligence.
Enroll Course: https://www.udemy.com/course/machine-learning-deep-learning-model-deployment/