Sagemaker Tutorial - 1 | Build a Sklearn Model with AWS Sagemaker & Custom Training Script

Hello, Guys, I am Spidy. I am back with another video. In this video, I am explaining "How you can deploy your custom Sklearn model into the AWS Sagemaker" Steps that we followed - 1. Initialize Boto3 SDK and create S3 bucket. 2. Upload data in Sagemaker Local Storage. 3. Data Exploration and Understanding. 4. Split the data into Train/Test CSV File. 5. Upload data into the S3 Bucket. 6. Create a Training Script 7. Train script inside Sagemaker container. 8. Store Model Artifacts(model.tar.gz) into the S3 Bucket. 9. Deploy Sagemaker Endpoint(API) for the trained model, and test it. Code ► https://github.com/Spidy20/Sagemaker-Tutorials AWS Tutorials ► https://youtube.com/playlist?list=PLsT53VV2LIIGnKRdYHMo-KO9uQcIhbFPs Donate ► machinelearninghubai@okhdfcbank Note: If you want me to solve your errors and make the project run into the system, I will do it using a remote desktop, and it will be paid. You can reach me at [email protected] for your queries. 🔥 Don't forget to Subscribe My GitHub for free projects► https://github.com/Spidy20 My store for buying paid projects► https://bit.ly/3hXSZxQ Facebook► https://www.facebook.com/machinelearninghubai/ Instagram► https://www.instagram.com/machine_learning_hub.ai Paypal► https://www.paypal.com/paypalme/spidy1820 Buy Coffee for me► https://www.buymeacoffee.com/spidy20 Playlist that you should check👇 Machine Learning College Projects► https://www.shorturl.at/mpIJY Python College Projects► https://www.shorturl.at/nDR25 Android App using Python► https://www.shorturl.at/pzCMQ "The video thumbnails were created using publicly available images from Google images and are used solely for thumbnail purposes. I do not claim ownership of these images. If you are the owner of any copyrighted content used in these thumbnails and want them removed or changed, please contact me and I will comply promptly. Thank you." #aws #sagemaker