𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗶𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴

In this video, we dive deep into the crucial phase of the machine learning lifecycle — Model Deployment and Prediction Service. This stage is all about taking your developed model and turning it into a real-world application, ready to make predictions that matter.

𝗬𝗼𝘂’𝗹𝗹 𝗹𝗲𝗮𝗿𝗻:

• The process of 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 and making them accessible in production environments.
• The difference between 𝗯𝗮𝘁𝗰𝗵 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 and 𝗼𝗻𝗹𝗶𝗻𝗲 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻, and when to use each.
• How 𝗺𝗼𝗱𝗲𝗹 𝘀𝗲𝗿𝘃𝗶𝗻𝗴 works and how to expose your model’s predictions through an endpoint.
• Techniques for 𝗺𝗼𝗱𝗲𝗹 𝗰𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 to reduce size and improve efficiency for deployment.
• Key decisions about deploying models on the 𝗰𝗹𝗼𝘂𝗱 𝘃𝘀. 𝗲𝗱𝗴𝗲 𝗱𝗲𝘃𝗶𝗰𝗲𝘀, and their impact on performance.
• How to optimize 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 for faster, more efficient predictions.
• The potential failures in machine learning systems like 𝗱𝗮𝘁𝗮 𝗱𝗿𝗶𝗳𝘁, 𝗲𝗱𝗴𝗲 𝗰𝗮𝘀𝗲𝘀, and 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀, and how to address them.

If you’re looking to understand the deployment process in machine learning and learn how to make your models work efficiently in real-wo