AI Engineering: Customizing LLMs for Business (Fine-Tuning LLMs with QLoRA & AWS)
Course description
Master an in-demand skill that companies are looking for: developing and implementing custom LLMs. In this course, you will learn how to fine-tune open large language models on closed/corporate data and deploy your models using AWS (SageMaker, Lambda, API Gateway) and Streamlit to provide a convenient interface for employees and clients.
This is not "just another introductory AI course." It is a practical deep dive into the skills that set AI engineers apart on real projects. You will perform fine-tuning using QLoRA, a method that drastically reduces resource consumption, and then turn the model into a production service.
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What you will master:
- Fine-tuning open-source LLM on your own datasets (including corporate ones).
- Practice with QLoRA, bfloat16 training, chunking datasets, attention masks.
- The Hugging Face ecosystem (including Estimator API) and MLOps pipeline on AWS.
- Model deployment and integration: SageMaker endpoints, Lambda, API Gateway, monitoring.
- Creating a simple business UI on Streamlit.
Outcome: from theory to code and production - the complete development cycle of applied AI for business cases.
Who it benefits and what roles it prepares for:
- AI Engineer / ML Engineer - designing, fine-tuning, and producing models.
- AI Specialist - creating applied solutions based on AI.
- Data Scientist - data preparation, EDA, and building models for company tasks.
- AI Research Scientist - in-depth work with attention mechanisms and LLM.
- Cloud Engineer - architecture and best deployment practices in AWS.
- DevOps Engineer - automation, release, and monitoring of ML services (CloudWatch, etc.).
- Software Engineer - integrating models into applications with scalability in mind.
- Data Engineer - data pipelines, storage (S3), preprocessing.
- Technical Product Manager - planning and releasing ML products, metrics, and monitoring.
If you want to catch the "AI wave," customizing LLM for business tasks is a great entry point and growth opportunity.
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Watch Online AI Engineering: Customizing LLMs for Business (Fine-Tuning LLMs with QLoRA & AWS)
All Course Lessons (58)
# | Lesson Title | Duration | Access |
---|---|---|---|
1 | Course Introduction (What We're Building) Demo | 05:20 | |
2 | Signing in to AWS | 04:31 | |
3 | Creating an IAM User | 05:30 | |
4 | Using our new IAM User | 03:13 | |
5 | What To Do In Case You Get Hacked! | 01:31 | |
6 | Creating a SageMaker Domain | 02:29 | |
7 | Logging in to our SageMaker Environment | 04:54 | |
8 | Introduction to JupyterLab | 07:38 | |
9 | Sagemaker Sessions, Regions, and IAM Roles | 07:51 | |
10 | Examining Our Dataset from HuggingFace | 13:30 | |
11 | Tokenization and Word Embeddings | 09:09 | |
12 | HuggingFace Authentication with Sagemaker | 04:22 | |
13 | Applying the Templating Function to our Dataset | 08:44 | |
14 | Attention Masks and Padding | 15:56 | |
15 | Star Unpacking with Python | 04:04 | |
16 | Chain Iterator, List Constructor and Attention Mask example with Python | 10:23 | |
17 | Understanding Batching | 08:12 | |
18 | Slicing and Chunking our Dataset | 07:32 | |
19 | Creating our Custom Chunking Function | 16:07 | |
20 | Tokenizing our Dataset | 09:31 | |
21 | Running our Chunking Function | 04:31 | |
22 | Understanding the Entire Chunking Process | 08:33 | |
23 | Uploading the Training Data to AWS S3 | 05:54 | |
24 | Setting Up Hyperparameters for the Training Job | 06:48 | |
25 | Creating our HuggingFace Estimator in Sagemaker | 06:46 | |
26 | Introduction to Low-rank adaptation (LoRA) | 08:12 | |
27 | LoRA Numerical Example | 10:56 | |
28 | LoRA Summarization and Cost Saving Calculation | 09:09 | |
29 | (Optional) Matrix Multiplication Refresher | 04:46 | |
30 | Understanding LoRA Programatically Part 1 | 12:33 | |
31 | Understanding LoRA Programatically Part 2 | 05:49 | |
32 | Bfloat16 vs Float32 | 08:11 | |
33 | Comparing Bfloat16 Vs Float32 Programatically | 06:33 | |
34 | Setting up Imports and Libraries for the Train Script | 07:20 | |
35 | Argument Parsing Function Part 1 | 07:57 | |
36 | Argument Parsing Function Part 2 | 10:55 | |
37 | Understanding Trainable Parameters Caveats | 14:31 | |
38 | Introduction to Quantization | 07:36 | |
39 | Identifying Trainable Layers for LoRA | 07:20 | |
40 | Setting up Parameter Efficient Fine Tuning | 04:36 | |
41 | Implement LoRA Configuration and Mixed Precision Training | 10:35 | |
42 | Understanding Double Quantization | 04:22 | |
43 | Creating the Training Function Part 1 | 14:15 | |
44 | Creating the Training Function Part 2 | 07:17 | |
45 | Exercise: Imposter Syndrome | 02:57 | |
46 | Finishing our Sagemaker Script | 05:09 | |
47 | Gaining Access to Powerful GPUs with AWS Quotas | 05:11 | |
48 | Final Fixes Before Training | 03:55 | |
49 | Starting our Training Job | 07:16 | |
50 | Inspecting the Results of our Training Job and Monitoring with Cloudwatch | 11:24 | |
51 | Deploying our LLM to a Sagemaker Endpoint | 17:58 | |
52 | Testing our LLM in Sagemaker Locally | 08:19 | |
53 | Creating the Lambda Function to Invoke our Endpoint | 08:56 | |
54 | Creating API Gateway to Deploy the Model Through the Internet | 02:37 | |
55 | Implementing our Streamlit App | 05:12 | |
56 | Streamlit App Correction | 03:27 | |
57 | Congratulations and Cleaning up AWS Resources | 02:39 | |
58 | Thank You! | 01:18 |
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