AI Cloud Data Pulse @AICloudData
Exploring the frontiers of AI and Big Data. Insights, guides, and trends from https://t.co/sQ48Y8SgwD. #AI #BigData #DataAnalytics aiclouddatapulse.com United States Joined January 2025-
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š¤ Whatās the hardest part of training your first ML model? š¹ Preprocessing data š¹ Building the model š¹ Training & tuning š¹ Evaluating performance I share my TensorFlow best practices here: š buff.ly/knWwH1w #MachineLearning #TensorFlow
ā” CloudTrail is more than compliance ā itās resilience. š Full breakdown: buff.ly/iUpp3cl #AWS #CloudSecurity
š Integrate with CloudWatch. Alarms + dashboards = real-time detection and response.
š Enable log validation. Tamper-proof logs build trust with auditors and investigators.
š Encrypt with KMS. Customer-managed keys = full control over who can decrypt your audit data.
š¦ Store logs in S3 with strict access controls. No public access. Use least-privilege IAM + bucket policies.
š” Use multi-trail setups. Separate security logs from operational logs to reduce noise and support compliance.
š Enable CloudTrail in ALL regions. Attackers love blind spots. Donāt give them one.
š AWS CloudTrail is your audit trail in the cloud ā but only if you set it up right. Here are key CloudTrail Best Practices you should know š§µ
š„ New on AI Cloud Data Pulse! Think your AWS logs are enough? Think again. AWS CloudTrail Best Practices you canāt afford to miss: ā All-region logging ā Security-focused monitoring ā Operational efficiency ā Pitfalls to avoid š Read here: buff.ly/iUpp3clā¦
š Training ML models isnāt just about crunching data ā itās about watching the process unfold. Hereās a quick look at the TensorFlow training loop in action š Full tutorial here ā buff.ly/knWwH1w #MLOps #AI #TensorFlow
If you could only use one metric to evaluate your ML modelā¦ š¤ ā” F1: Balanced precision & recall. š AUC: Measures ranking across thresholds. Which do you pick ā and why? š buff.ly/Vwh0uoS #MachineLearning #ModelEvaluation #AI
Ready to build your first model? Full tutorial here: š buff.ly/knWwH1w #DeepLearning #TensorFlow #AI
š Step 4: Save & reuse š¹ Save your model in .keras format š¹ Reload later without retraining Hereās the full workflow š
š§Ŗ Step 3: Train smart š¹ Use early stopping to avoid overfitting š¹ Experiment with learning rates for better results
āļø Step 2: Build your model š¹ Use the Sequential API for simple layer stacking š¹ Dense layers + activations = your neural net
āØ Step 1: Preprocess your data š¹ Normalize inputs for stable training š¹ Split into train/test for fair evaluation
š New to TensorFlow? Letās break down how to train your first model step by step. š§µ A thread for beginners ā
š Want to train your first ML model without drowning in complexity? My new step-by-step TensorFlow tutorial shows you how to go from raw data ā trained model ā saved pipeline. š Read it here: buff.ly/knWwH1w #TensorFlow #MachineLearning #AI
ML Engineers ā Whatās the most important factor when choosing an instance for training? š§ š Cost š Training time š Memory/GPU resources š Library compatibility Comment if youāve ever over-provisioned "just to be safe" š Poll inspired by our latest SageMaker guide: šā¦
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