Hi, I’ve written a paper that is related to protecting the intellectual property of machine learning models. It is ML heavy but since Security conferences are less crowded compared to the ML ones I initially had a series of submissions there but received poor quality of reviews since people were not understanding the basics of ML itself over there. Then I have tried to submit to AAAI which was way worse this year in terms of review quality. My paper is very strong in terms of the breadth of experiments and reproducibility. I’m considering to submit it to TMLR since i’ve heard great things about the review quality and their emphasis on technical correctness over novelty. But I’m worried about my how a TMLR paper would look on a grad school application which is why I’m also considering ICML which is in 3 months. But again I’m also worried about the noisy reviews from ICML based on my past experience with my other papers. I would love to get any opinions on this topic! submitted by /u/Pranav_999 [link] [comments]

I though it better to discuss reviews of ICLR 2026 here. It will be released on tomorrow submitted by /u/Alternative_Art2984 [link] [comments]

What does the AAAI-26 Student Scholar Volunteer Program involve, and approximately how much support does it provide? submitted by /u/Extension-Aspect9977 [link] [comments]

No, you didn't read that wrong. I'm going to train Street Fighter 4 using the new Citra training option in SDLArch-RL and use transfer learning to transfer that learning to Street Fighter 6!!!! In short, what I'm going to do is use numerous augmentation and filter options to make this possible!!!! I'll have to get my hands dirty and create an environment that allows me to transfer what I've learned from one game to another. Which isn't too difficult, since most of the effort will be focused on Street Fighter 4. Then it's just a matter of using what I've learned in Street Fighter 6. And bingo! Don't forget to follow our project: https://github.com/paulo101977/sdlarch-rl And if you like it, maybe you can buy me a coffee 🙂 Sponsor u/paulo101977 on GitHub Sponsors Next week I'll start training and maybe I'll even find time to integrate my new achievement: Xemu!!!! I managed to create compatibility between Xemu and SDLArch-RL via an interface similar to RetroArch. https://github.com/paulo101977/xemu-libretro submitted by /u/AgeOfEmpires4AOE4 [link] [comments]

The last few months I've been doing a deep-dive into information geometry and I've really, thoroughly enjoyed it. Understanding models in higher-dimensions is nearly impossible (for me at least) without breaking them down this way. I used a Fisher information matrix approximation to "watch" a model train and then compared it to other models by measuring "alignment" via top-k FIM eigenvalues from the final, trained manifolds. What resulted was, essentially, that task manifolds develop shared features in parameter space. I started using composites of the FIM top-k eigenvalues from separate models as initialization points for training (with noise perturbations to give GD room to work), and it positively impacted the models themselves to train faster, with better accuracy, and fewer active dimensions when compared to random initialization. Some of that is obvious- of course if you initialize with some representation of a model's features you're going to train faster and better. But in some cases, it wasn't. Some FIM top-k eigenvalues were strictly orthogonal between two tasks- and including both of them in a composite initialization only resulted in interference and noise. Only tasks that genuinely shared features could be used in composites. Furthermore, I started dialing up and down the representation of the FIM data in the composite initialization and found that, in some cases, reducing the representation of some manifold's FIM top-k eigenspace matrix in the composite actually resulted in better performance by the under-represented model. Faster training, fewer active dimensions, and better accuracy. This is enormously computationally expensive in order to get those modest gains- but the direction of my research has never been about making bigger, better models but rather understanding how models form through gradient descent and how shared features develop in similar tasks. This has led to some very fun experiments and I'm continuing forward- but it has me wondering, has anyone else been down this road? Is anyone else engaging with the geometry of their models? If so, what have you learned from it? Edit: Adding visualization shared in the comments: https://imgur.com/a/sR6yHM1 submitted by /u/SublimeSupernova [link] [comments]

submitted by /u/dragandj [link] [comments]

New preprint "Measuring Individual Differences in Meaning: The Supervised Semantic Differential" https://doi.org/10.31234/osf.io/gvrsb_v1 Trigger warning - the preprint is written for psychologists so expect a difference in format to classical ML papers After multiple conferences (ISSID, PSPS, ML in PL), getting feedback, and figuring out how to present the results properly the preprint we've put together with my wonderful colleagues is finally out, and it introduces a method that squares semantic vector spaces with psychology-sized datasets. SSD makes it possible to statistically test and explain differences in meaning of concepts between people based on the texts they write. This method, inspired by deep psychological history (Osgood's work), and a somewhat stale but well validated ML language modeling method (Word Embeddings), will allow computational social scientists to extract data-driven theory-building conclusions from samples smaller than 100 texts. Comments appreciated. https://preview.redd.it/mjzt7belb90g1.png?width=1210&format=png&auto=webp&s=02b4338ab35e05e23e07bd169391ba63b9cb25cc submitted by /u/Hub_Pli [link] [comments]

https://preview.redd.it/a9a5cmud890g1.png?width=320&format=png&auto=webp&s=4d3b35fe360f74ce16de394f4cce37ac00ca6acf Hello everyone, I am training a captcha recognition model using CRNN. The problem now is that there are occasional spikes in my validation loss, which I'm not sure why it occurs. Below is my model architecture at the moment. Furthermore, loss seems to remain stuck around 4-5 mark and not decrease, any idea why? TIA! input_image = layers.Input(shape=(IMAGE_WIDTH, IMAGE_HEIGHT, 1), name="image", dtype=tf.float32) input_label = layers.Input(shape=(None, ), dtype=tf.float32, name="label") x = layers.Conv2D(32, (3,3), activation="relu", padding="same", kernel_initializer="he_normal")(input_image) x = layers.MaxPooling2D(pool_size=(2,2))(x) x = layers.Conv2D(64, (3,3), activation="relu", padding="same", kernel_initializer="he_normal")(x) x = layers.MaxPooling2D(pool_size=(2,2))(x) x = layers.Conv2D(128, (3,3), activation="relu", padding="same", kernel_initializer="he_normal")(x) x = layers.BatchNormalization()(x) x = layers.MaxPooling2D(pool_size=(2,1))(x) reshaped = layers.Reshape(target_shape=(50, 6*128))(x) x = layers.Dense(64, activation="relu", kernel_initializer="he_normal")(reshaped) rnn_1 = layers.Bidirectional(layers.LSTM(128, return_sequences=True, dropout=0.25))(x) embedding = layers.Bidirectional(layers.LSTM(64, return_sequences=True, dropout=0.25))(rnn_1) output_preds = layers.Dense(units=len(char_to_num.get_vocabulary())+1, activation='softmax', name="Output")(embedding ) Output = CTCLayer(name="CTCLoss")(input_label, output_preds) submitted by /u/sparttann [link] [comments]

Hi all, I implemented Reinforcement Learning from Human Feedback (RLHF) including Supervised Fine-Tuning (SFT), Reward Modeling (RM), and Proximal Policy Optimization (PPO) step-by-step in three notebooks. I used these steps to train a GPT-2 model on Stanford Sentiment Treebank v2 (SST2), a dataset of movie reviews. After the SFT step, GPT-2 model learns to generate sentences that look like movie reviews. Next, I build a reward model from another instance of GPT-2 model with a reward head attached on top and train it to predict the sentiment associated with a movie review. Finally, in the PPO step, I further train the SFT model and use the reward from the reward model to encourage the SFT model to generate only the movie reviews with positive sentiment. All the Jupyter notebooks are available on GitHub: https://github.com/ash80/RLHF_in_notebooks For those curious, I also created a video walkthrough explaining each step of the implementation in detail on YouTube here: https://www.youtube.com/watch?v=K1UBOodkqEk Happy to discuss or receive any feedback! submitted by /u/ashz8888 [link] [comments]

Qubic researchers just released Neuraxon. Bio-inspired AI blueprint with trinary neurons (+1/0/-1) for brain-like computation. Aims to let AI evolve itself on decentralized Aigarth (Qubics Ai system).Currently training their own AI “Anna” using computational power from miners under this system. Open-source; can anyone confirm if it’s legit? • Code: github.com/DavidVivancos/Neuraxon • Demo: huggingface.co/spaces/DavidVivancos/Neuraxon • X post: x.com/VivancosDavid/status/1986370549556105336 Could be worth discussing for its potential implications on neuromorphic computing and AGI paths. (sharing something intriguing I found.) submitted by /u/Defiant-Industry-626 [link] [comments]

People tend to exaggerate on LinkedIn, in CVs, and in Stack Overflow surveys about how many programming languages they actually work with. What I’m interested in is: which other languages are really used in professional settings? Let me start. In our unit, data scientists, machine learning engineers, and data engineers work exclusively with Python, while our front-end developers use JavaScript with React — and that’s it. I’ve experimented with a few other languages myself, but since our team is quite large (70+ people in total), the lowest common denominators are Python and JavaScript. That makes it practically impossible to introduce a new language without a very strong reason — and such a reason hasn’t appeared yet. Elsewhere in the company, the general tech stack is mostly Java-based, and new projects are written in Kotlin as far as I know. Data projects, however, are all written exclusively in Python. In my previous unit, we also had a few services written in Go, but I haven’t heard of any in-house Go usage since then. submitted by /u/DataPastor [link] [comments]

Hey everyone! A short academic survey has been prepared to gather insights from the community regarding Neural Architecture Search (NAS) and RNN-based models. It’s completely anonymous, takes only a few minutes to complete, and aims to contribute to ongoing research in this area. You can access the survey here: 👉 https://forms.gle/sfPxD8QfXnaAXknK6 Participation is entirely voluntary, and contributions from the community would be greatly appreciated to help strengthen the collective understanding of this topic. Thanks to everyone who takes a moment to check it out or share their insights! submitted by /u/PittuPirate [link] [comments]

Hey all, Recently I made a post about Knowledge graph traversal: https://www.reddit.com/r/MachineLearning/s/RAzcGCatN6 I got a ton of constructive criticism about the research and I thank everyone for the comments. The main thing I realized was that it’s not a knowledge graph (ontological facts) but just a cosine/semantic similarity graph (cosine similarities). I have seen a lot of people in the sub here talk about fact/ontological knowledge graphs significantly more though. And I wanted to kind of spark a conversation about something. I did most of my research into cosine similarity graphs, but I’m curious if it’s possible to do some kind of combination of cosine similarity AND fact/ontology. Or if there’s even necessarily a use case for something like that. Additionally, and this was the big thing I found interesting, was having an LLM traverse a similarity graph proved very very effective at recall. I’m wondering if anyone has wanted to explore fact/ontological knowledge graph traversal. Or a combined graph that would ALSO contain cosine similarities. Has anyone explored or wanted to explore this? What about LLM traversal of combined knowledge graphs? I know that I’ve seen some people mentioned having an LLM build a knowledge graph from corpus which is very cool and doable, but I’m more talking about trying to make LLMs highly accurate via knowledge/information retrieval. submitted by /u/Alieniity [link] [comments]

I have been doing some research and I found out that TPUs are much cheaper than GPUs and apparently they are made for machine learning tasks, so why are google and TPUs not having the same hype as GPUs and NVIDIA. submitted by /u/DryHat3296 [link] [comments]

Created a slide deck with relevant paper links to illustrate brief history of LLM Post Training https://github.com/samrat3264/llm_post_training_history/blob/main/Post-Training%20Soup.pdf submitted by /u/Internet_Problems [link] [comments]

Organizations need seamless access to their structured data repositories to power intelligent AI agents. However, when these resources span multiple AWS accounts integration challenges can arise. This post explores a practical solution for connecting Amazon Bedrock agents to knowledge bases in Amazon Redshift clusters residing in different AWS accounts.

Imagine your ML development environment running inside a web platform where each tool such as Jupyter, VS Code, or a labeling app runs in its own container and opens directly in the web application. There are no virtual desktops or VDIs, no local setup, and no dependency conflicts. The underlying platform manages GPU scheduling, networking, and storage automatically. Each container would start in seconds on pooled GPU or CPU nodes, connect to centralized file or object storage for notebooks and datasets, and shut down cleanly when idle. Your code, libraries, and outputs would persist between sessions so that when you log back in, your workspace restores exactly where you left off without consuming any idle compute resources. The base infrastructure still includes the familiar layers of hypervisors, GPU drivers, and shared storage that most ML clusters rely on today, but users never need to interact with or maintain them. From a user’s point of view, it would feel like opening a new browser tab rather than provisioning a virtual machine. I am curious how this kind of setup would affect daily ML workflows: Would reproducibility improve if everyone launched from a common base image with standardized dependencies and datasets? Would faster startup times change how you manage costs by shutting down sessions more often? Where might friction appear first, such as in data access policies, custom CUDA stacks, or limited control over environments? Would you still prefer a dedicated VM or notebook instance for flexibility, or would this kind of browser-based environment be enough? How could this approach influence collaboration, environment drift, or scaling across teams? Not affiliated with any platform. Just exploring how a web platform that delivers ML tools as browser-based containers might change the balance between speed, reproducibility, and control. submitted by /u/Majestic_Tear2224 [link] [comments]

In this blog post, we explore how TR addressed key business use cases with Open Arena, a highly scalable and flexible no-code AI solution powered by Amazon Bedrock and other AWS services such as Amazon OpenSearch Service, Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB, and AWS Lambda. We'll explain how TR used AWS services to build this solution, including how the architecture was designed, the use cases it solves, and the business profiles that use it.

Today, we are excited to announce the addition of structured output to Custom Model Import. Structured output constrains a model's generation process in real time so that every token it produces conforms to a schema you define. Rather than relying on prompt-engineering tricks or brittle post-processing scripts, you can now generate structured outputs directly at inference time.

Decisions still haven’t been released. CVPR allows dual WACV submissions. How is it different than just a dual submission moment after WACV round 1 reviews were in. This has to be one hell of a serious mishap. submitted by /u/casualcreak [link] [comments]

Hi everyone, just sharing a couple of slides about Gemma3n architecture. I found it a very interesting architecture with a lot of innovations (e.g. Matryoshka Transformers, MobileNetV5, PLE, etc) that are very rare to see nowadays. Given that there weren't much information about the model, I decided to dig further and made a couple of slides for those interested. submitted by /u/perone [link] [comments]

Hey all, I am excited to share our new pre-print with you. GRAM: a General-purpose Real-world Audio Model to efficiently learn spatial audio representations. We tried to adress two main limitation of recent foundation models. (1) The performance drop of recent audio foundations models on real-world acoustic environments with reverberation and noise. (2) The inherent spatial nature of real-world sound scenes is overlooked and tasks involving sound localization ruled out. Therefore, we proposed GRAM-Binaural (A Binaural foundation model that can perform extremely well on general purpose audio representation learning, and do localization), and GRAM-Ambisonics (Similar to binaural, but has better localization properties). https://preview.redd.it/cqmwxkxobuzf1.png?width=1085&format=png&auto=webp&s=7bd8785f3efddd813115d22c56721de76e53f7c4 The results were very interesting. GRAMs showcased that naturalistic training (training with reverb + noise) is actually beneficial for both dry (HEAR) and naturalistic scene (Nat-HEAR) (audio with reverb + noise + spatial) performance. And, GRAMs surprassed state-of-the-art spectrogram foundation models with fraction of the data. Furthermore, GRAMs could localize sounds without specialized localization pre-training unlike other models. This marks GRAMs as the first audio foundation model that is available in both a two-channel, binaural format and a four-channel, first-order ambisonics format. To see more experiments, and read more in depth please see: Paper: https://arxiv.org/abs/2506.00934 Code: https://github.com/labhamlet/GRAM-T To try GRAMs, please use the huggingface endpoints: https://huggingface.co/labhamlet Looking forward to a nice discussion! submitted by /u/ComprehensiveTop3297 [link] [comments]

https://preview.redd.it/7u5do1x19uzf1.png?width=1103&format=png&auto=webp&s=bfc314716f4e33593b16e6e131870dae62d7577a Hey All, We have just released our new pre-print on WavJEPA. WavJEPA is an audio foundation model that operates on raw waveforms (time-domain). Our results showcase that WavJEPA excel at general audio representation tasks with a fraction of compute and training data. In short, WavJEPA leverages JEPA like semantic token prediction tasks in the latent space. This make WavJEPA stand out from other models such as Wav2Vec2.0, HuBERT, and WavLM that utilize speech level token prediction tasks. In our results, we saw that WavJEPA was extremely data efficent. It exceeded the downstream performances of other models with magnitudes of less compute required. https://preview.redd.it/7uxj7wgz9uzf1.png?width=1084&format=png&auto=webp&s=6d05cf829a65bfaec5871dfe0487e4d11c80b132 We were further very interested in models with good robustness to noise and reverberations. Therefore, we benchmarked state-of-the-art time domain audio models using Nat-HEAR (Naturalistic HEAR Benchmark with added reverb + noise). The differences between HEAR and Nat-HEAR indicated that WavJEPA was very robust compared to the other models. Possibly thanks to semantically rich tokens. Furthermore, in this paper we proposed WavJEPA-Nat. WavJEPA-Nat is trained with naturalistic scenes (reverb + noise + spatial), and is optimized for learning robust representations. We showed that WavJEPA-Nat is more robust than WavJEPA on naturalistic scenes, and performs better on dry scenes. As we are an academic institution, we did not have huge amounts of compute available. We tried to make the best out of it, and with clever tricks we managed to create a training methadology that is extremely fast and efficent. To go more in-depth please refer to our paper and the code: Paper: https://arxiv.org/abs/2509.23238 Code: https://github.com/labhamlet/wavjepa And, to use WavJEPA models, please use our huggingface endpoint. https://huggingface.co/labhamlet/wavjepa-base Looking forward to your thoughts on the paper! submitted by /u/ComprehensiveTop3297 [link] [comments]

I see "Modified 5 November" on the latest updates on Openreview. This probably implies that AAAI-2026 results are imminent within a day or so. I'm opening up this thread for you to post your scores (and their associated confidences) and results, but please also mention what category (CV etc.) you submitted to, and whether or not you provided additional experimental results in your 2500-character rebuttal (even if the instructions said not to - I've noticed many authors in my review stack have done this anyway). Other points of discussion are also welcomed! submitted by /u/Adventurous-Cut-7077 [link] [comments]

Is OpenReview down for anyone else? Great timing — right ahead of the CVPR registration deadline. Here’s the funny (and painful) part: I submitted my paper earlier with only myself as the author, planning to add my co-authors and PI later once our final results were ready. And now… the site’s down, and I can’t access anything. P.S. The deadline is in just about 4 and a half hours. submitted by /u/Outrageous_Tip_8109 [link] [comments]

I just got an email from CVPR saying "For CVPR 2026, all authors are required to have a complete OpenReview profile and a complete author enrollment." But I don't understand. What is the meaning of "Complete OpenReview Profile"? I went through tens of reviews and submissions this year, and suddenly it is incomplete? Anyone has an idea about this?? submitted by /u/Public_Courage_7541 [link] [comments]

Change in policy: Attendance for authors of accepted papers is optional. After acceptance notifications, the authors will be able to decide by a specified date whether they wish to present their paper in person at the conference or they just wish to include their paper in the proceedings (without presentation at the conference). Regardless of this choice, all the accepted papers will receive equivalent treatment in the proceedings. They will all be eligible for ICML awards as well as for the designations of distinction corresponding to the past “oral presentations” and “spotlight posters.” For proceedings-only papers, at least one of the authors must obtain virtual registration. source: https://icml.cc/Conferences/2026/CallForPapers submitted by /u/Striking-Warning9533 [link] [comments]

Earlier this year, we introduced Amazon Bedrock AgentCore Gateway, a fully managed service that serves as a centralized MCP tool server, providing a unified interface where agents can discover, access, and invoke tools. Today, we're extending support for existing MCP servers as a new target type in AgentCore Gateway. With this capability, you can group multiple task-specific MCP servers aligned to agent goals behind a single, manageable MCP gateway interface. This reduces the operational complexity of maintaining separate gateways, while providing the same centralized tool and authentication management that existed for REST APIs and AWS Lambda functions.

TabPFN-2.5, a pretrained transformer that delivers SOTA predictions on tabular data without hyperparameter tuning is now available. It builds on TabPFN v2 that was released in the Nature journal earlier this year. Key highlights: 5x scale increase: Now handles 50,000 samples × 2,000 features (up from 10,000 × 500 in v2) SOTA performance: Achieves state-of-the-art results across classification and regression Rebuilt API: New REST interface & Python SDK with dedicated fit & predict endpoints, making deployment and integration significantly more developer-friendly Want to try it out? TabPFN-2.5 is available via an API and via a package on Hugging Face. We welcome your feedback and discussion! You can also join the discord here. submitted by /u/rsesrsfh [link] [comments]

In this post, we show you how Amazon Search optimized GPU instance utilization by leveraging AWS Batch for SageMaker Training jobs. This managed solution enabled us to orchestrate machine learning (ML) training workloads on GPU-accelerated instance families like P5, P4, and others. We will also provide a step-by-step walkthrough of the use case implementation.

Amazon Bedrock AgentCore is an agentic platform for building, deploying, and operating effective agents securely at scale. Amazon Bedrock AgentCore Runtime is a fully managed service of Bedrock AgentCore, which provides low latency serverless environments to deploy agents and tools. It provides session isolation, supports multiple agent frameworks including popular open-source frameworks, and handles multimodal

In this post, we examine the specific challenges Switchboard, MD faced with scaling transcription accuracy and cost-effectiveness in clinical environments, their evaluation process for selecting the right transcription solution, and the technical architecture they implemented using Amazon Connect and Amazon Kinesis Video Streams. This post details the impressive results achieved and demonstrates how they were able to use this foundation to automate EMR matching and give healthcare staff more time to focus on patient care.

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Enterprises in regulated industries often need mathematical certainty that every AI response complies with established policies and domain knowledge. Regulated industries can’t use traditional quality assurance methods that test only a statistical sample of AI outputs and make probabilistic assertions about compliance. When we launched Automated Reasoning checks in Amazon Bedrock Guardrails in preview at

In 2024, we launched the Custom Model Program within the AWS Generative AI Innovation Center to provide comprehensive support throughout every stage of model customization and optimization. Over the past two years, this program has delivered exceptional results by partnering with global enterprises and startups across diverse industries—including legal, financial services, healthcare and life sciences,

This post builds upon our previous post discussing how Clario developed an AI solution powered by Amazon Bedrock to accelerate clinical trials. Since then, Clario has further enhanced their AI capabilities, focusing on innovative solutions that streamline the generation of software configurations and artifacts for clinical trials while delivering high-quality clinical evidence.

こんにちは, G’day. The recent launch of Anthropic’s Claude Sonnet 4.5 and Claude Haiku 4.5, now available on Amazon Bedrock, marks a significant leap forward in generative AI models. These state-of-the-art models excel at complex agentic tasks, coding, and enterprise workloads, offering enhanced capabilities to developers. Along with the new models, we are thrilled to announce that

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AI agents need to browse the web on your behalf. When your agent visits a website to gather information, complete a form, or verify data, it encounters the same defenses designed to stop unwanted bots: CAPTCHAs, rate limits, and outright blocks. Today, we are excited to share that AWS has a solution. Amazon Bedrock AgentCore

In this post, we explore how to deploy NVIDIA's Parakeet ASR model on Amazon SageMaker AI using asynchronous inference endpoints to create a scalable, cost-effective pipeline for processing large volumes of audio data. The solution combines state-of-the-art speech recognition capabilities with AWS managed services like Lambda, S3, and Bedrock to automatically transcribe audio files and generate intelligent summaries, enabling organizations to unlock valuable insights from customer calls, meeting recordings, and other audio content at scale .

In this post, we explore the critical design considerations for building responsible AI systems in healthcare and life sciences, focusing on establishing governance mechanisms, transparency artifacts, and security measures that ensure safe and effective generative AI applications. The discussion covers essential policies for mitigating risks like confabulation and bias while promoting trust, accountability, and patient safety throughout the AI development lifecycle.

In this post, we explore the Five V's Framework—a field-tested methodology that has helped 65% of AWS Generative AI Innovation Center customer projects successfully transition from concept to production, with some launching in just 45 days. The framework provides a structured approach through Value, Visualize, Validate, Verify, and Venture phases, shifting focus from "What can AI do?" to "What do we need AI to do?" while ensuring solutions deliver measurable business outcomes and sustainable operational excellence.

In this post, we explore an innovative approach that converts natural language to Gremlin queries using Amazon Bedrock models such as Amazon Nova Pro, helping business analysts and data scientists access graph databases without requiring deep technical expertise. The methodology involves three key steps: extracting graph knowledge, structuring the graph similar to text-to-SQL processing, and generating executable Gremlin queries through an iterative refinement process that achieved 74.17% overall accuracy in testing.

In this post, we explore how companies can systematically incorporate responsible AI practices into their generative AI project prioritization methodology to better evaluate business value against costs while addressing novel risks like hallucination and regulatory compliance. The post demonstrates through a practical example how conducting upfront responsible AI risk assessments can significantly change project rankings by revealing substantial mitigation work that affects overall project complexity and timeline.

In this post, we explore how product teams can leverage Amazon Bedrock and AWS services to transform their creative workflows through generative AI, enabling rapid content iteration across multiple formats while maintaining brand consistency and compliance. The solution demonstrates how teams can deploy a scalable generative AI application that accelerates everything from product descriptions and marketing copy to visual concepts and video content, significantly reducing time to market while enhancing creative quality.

In this post, we explore advanced cost monitoring strategies for Amazon Bedrock deployments, introducing granular custom tagging approaches for precise cost allocation and comprehensive reporting mechanisms that build upon the proactive cost management foundation established in Part 1. The solution demonstrates how to implement invocation-level tagging, application inference profiles, and integration with AWS Cost Explorer to create a complete 360-degree view of generative AI usage and expenses.

In this post, we introduce a comprehensive solution for proactively managing Amazon Bedrock inference costs through a cost sentry mechanism designed to establish and enforce token usage limits, providing organizations with a robust framework for controlling generative AI expenses. The solution uses serverless workflows and native Amazon Bedrock integration to deliver a predictable, cost-effective approach that aligns with organizational financial constraints while preventing runaway costs through leading indicators and real-time budget enforcement.

You don’t always need a heavy wrapper, a big client class, or dozens of lines of boilerplate to call a large language model.

Large dataset handling in Python is not exempt from challenges like memory constraints and slow processing workflows.

Before we begin, let's make sure you're in the right place.

Agentic artificial intelligence (AI) represents the most significant shift in machine learning since deep learning transformed the field.

Large language models (LLMs) are widely used in applications like chatbots, customer support, code assistants, and more.

You've written Python that processes data in a loop.

One of the claims made by OpenAI regarding its latest model, GPT-5 , is a breakthrough in reasoning for math and logic, with the ability to “think” more deeply when a prompt benefits from careful analysis.

Time series data have the added complexity of temporal dependencies, seasonality, and possible non-stationarity.

Imbalanced datasets are a common challenge in machine learning.

You've loaded your dataset and the distribution plots look rough.