How to Fine-Tune AI Models with Amazon Bedrock

🚀 Curious how to make a pre-trained AI model specialize in your own data? In my latest video, I demonstrate fine-tuning in Amazon Bedrock with a full hands-on walkthrough.   📌 Topics covered: What fine-tuning is and why it matters Preparing a dataset in JSONL format and uploading your dataset to Amazon S3 Creating a fine-tuning job in Bedrock Setting up inference: on-demand vs provisioned throughput Testing the custom model in the playground Interpreting validation results from S3   💡 Video link is in the first comment — check it out for the full demo!   #AI #MachineLearning #AmazonBedrock #FineTuning #CustomModels #DataScience

🎭 “You don’t have the software, you’re not going to make it.” I.T. Bee, aka" the Data Puppet by SCOTT TAYLOR, dropped some sharp truths at Big Data LDN 2025. From AI vs. ML confusion to the race for a modern data stack, this quick-hit interview reminds us: it’s not just about data or process, it is about the technology that holds it all together. 💬 “We tell the businesses it’s AI, but it’s actually machine learning.” 💬 “We can’t stack it high enough.” Catch the full clip below 👇 #BigDataLDN #ModernDataStack #MachineLearning #EM360Tech

How do you keep your ML experiments organized when you’re tuning hundreds of models? We’ve just launched a new video series on Experiment Tracking with MLflow, led by Franco Matzkin, Machine Learning Engineer at Azumo, as part of our Level Up with AI initiative. In this hands-on series, Victor breaks down: • What makes experiment tracking essential in every ML workflow • How to manage hyperparameters, version models, and avoid “parameter chaos” • How MLOps connects everything — from training to production — using MLflow If you’re an ML engineer, data scientist, or just getting started with MLOps, this is a must-watch. 🎥 Watch the full series here: https://hubs.la/Q03NGRXM0 #MachineLearning #MLOps #MLflow #DataScience #AI #Azumo

Day 4 of My 100 Days of AI/ML Challenge ⚡ Today’s focus was on Model Selection & Training — finally putting the preprocessed data to work with different machine learning models! What I worked on today: Train/Test split using Scikit-learn to avoid data leakage Trained baseline models (Logistic Regression, KNN, Decision Tree, Random Forest) Compared performance using accuracy, precision, recall, and F1-score Used cross-validation to get more reliable performance estimates Experimented with GridSearchCV for simple hyperparameter tuning Hands-On Work in Google Colab: ✅ Built multiple models using Scikit-learn ✅ Evaluated them with classification reports and confusion matrices ✅ Compared baseline models to understand trade-offs ✅ Started tuning hyperparameters to squeeze out extra performance ✨ Key Takeaway Model selection isn’t just about accuracy — it’s about picking the right trade-offs depending on the problem. Baselines first, then tune and optimize! Excited for Day 5: Model Evaluation & Validation — diving deeper into metrics beyond accuracy. 🔗 I’ve shared today’s notebook here:https://lnkd.in/geV6VDqz #100DaysofML #AI #MachineLearning #ModelSelection #100DaysofCode #UniversityofCincinnati

Big news in AI + Data! Databricks and OpenAI have entered a $100M partnership, bringing OpenAI’s latest models natively into the Databricks platform. This means enterprises can now run powerful AI securely on their own data — a real game-changer for the future of data + intelligence. Exciting times ahead! #OpenAI #Databricks #AI #DataIntelligence #Innovation

🚀 Curious how OpenAI Uses Databricks? Check out this video on OpenAIs DocGPT, which has cut down contract review times by over 50%! Using Databricks, their finance teams can now process thousands of contracts automatically, turning long PDFs into easy-to-read tables. 🎥 Watch the full story here: https://lnkd.in/ebG7iKEA #OpenAI #Databricks #AI #DataEngineering

🚀 Curious how OpenAI Uses Databricks? Check out this video on OpenAIs DocGPT, which has cut down contract review times by over 50%! Using Databricks, their finance teams can now process thousands of contracts automatically, turning long PDFs into easy-to-read tables. 🎥 Watch the full story here: https://lnkd.in/ebG7iKEA #OpenAI #Databricks #AI #DataEngineering

🎥 Our new Collate Clue covers reverse metadata, a powerful and relatively unique feature of Collate that enables you to push metadata back to the source systems from which the metadata was ingested initially. This allows you to leverage all the advanced AI and machine learning features in Collate to maintain metadata in systems such as #Snowflake, #Redshift, #BigQuery, #Databricks, and more. This short video will show you how it's done. Watch here: https://buff.ly/FRDzfde

I have a new Collate Clue for you, covering how Reverse Metadata works, and why it is such an awesome feature in Collate.