Mark Relph

Amazon Web Services (AWS) • 8K followers

re:Invent Day 1 is done and it was packed!My day began with a roundtable with some of our best partners talking about how we can better help customers migrate and modernize their AI workloads and use cases. Then Priya Arora and I presented on-stage on how Agentic AI is opening opportunities for partners, from ISVs and SIs, to startups. We shared data from our survey we did with BCG showing the trends in customer adoption of AI, focusing on the industries and use cases with the highest momentum. We also uncovered areas where customers need the most help and support as they roll out their agentic use cases.After that I sat down with 3 key partners, grabbed lunch with a few AWS peers I don't get to see often enough, and had a lot of ad hoc hallway meetings. (It's hard to walk 10 feet at re:Invent without seeing someone you know)But my highlight was launching the new AI Competency and Agentic AI categories for partners.I had a chance to join the AWS OnAir team to talk about it. My team was the driving force behind the launch and I'm proud of what this means for our partner community.We spent months listening to partners tell us they needed a way to stand out in customer conversations around agentic AI. The new AI Competency creates three distinct specialization paths. Agentic AI Applications recognizes partners delivering production-ready autonomous systems. Agentic AI Tools validates partners providing the infrastructure and tooling that makes agent development possible. Agentic AI Consulting Services distinguishes partners with proven expertise helping enterprises design, build, and scale agentic deployments. Each path requires demonstrated technical depth and validated customer outcomes, not just certifications or marketing claims.We launched with 60 partners who achieved the AI specialization. That's the highest number of launch partners in any AWS Competency program so far. Partners like Loka, Anthropic, LangChain, and Mission are already proving their ability to deploy AI systems that handle real business processes autonomously. That validation matters when enterprises are making critical technology decisions.Partners achieving these specializations gain access to funding, co-marketing resources, and priority placement in customer engagements. But what matters most is the market differentiation. When customers are evaluating dozens of partners claiming agentic AI expertise, this competency provides a clear signal about who has actually done the work.For enterprises evaluating partners, this competency provides the differentiation signal you need. These partners have solved the hard problems of moving agents from demo to production, from single use case to enterprise platform. The timing matters. Enterprises are moving past whether to deploy agentic AI and into how to do it at scale. Partners with proven expertise become increasingly valuable as deployment complexity increases. That's what makes this competency so important.

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Samuel Bonamigo

Databricks • 17K followers

#Training and #upskilling our customers and partners with the latest data and AI skills is incredibly important to Databricks. That’s why we have opened a new Data + AI Academy in #India, which will support the training of more than 500,000+ data and AI professionals over the next three years.With an increasing need for AI talent, the academy will meet the demands for advanced data and AI skill sets and have a big impact on our customer and partner ecosystem in EMEA. Take a look at the full story here 👉https://lnkd.in/dEGaAgJR Ed Lenta, Rochana Golani, Ankush Korla, Vinod Marur, Anna Lewis

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Dr. Habib Shaikh, PhD (AI)

Northern Trust • 21K followers

Just getting started with LLMs?Don’t let the jargon slow you down.This 5-part LLM Glossary is your shortcut to mastering the fundamentals👇1️⃣ Model Types• Foundation Model: Pretrained on large-scale, diverse datasets to learn general capabilities.• Instruction-Tuned Model: Further trained to follow user instructions precisely.• Multi-modal Model: Handles multiple input/output formats like text, images, and audio.• Reasoning Model: Optimized for logic, problem-solving, and step-by-step thinking.• Small Language Model (SLM): Lightweight models for fast, efficient, on-device tasks.2️⃣ Training & Fine-Tuning• Pretraining: Initial learning phase using massive, diverse data sources.• RLHF (Reinforcement Learning with Human Feedback): Aligns model behavior with human preferences.• DPO (Direct Preference Optimization): Uses ranked preferences instead of rewards for training.• Synthetic Data: AI-generated data used to supplement real datasets.• Fine-Tuning: Targeted retraining on specific domains or tasks.• LoRA / QLoRA: Techniques for efficient low-resource fine-tuning.• Guardrails: Rules or filters applied to enforce safety, ethics, and compliance.3️⃣ Prompt Engineering• System/User Prompts: Define model behavior and user context in a session.• Chain-of-Thought (CoT): Prompts that guide the model to reason step-by-step.• Few-Shot / Zero-Shot Learning: Demonstrating tasks with few or no examples.• Prompt Tuning: Optimizing prompts via training for specific outcomes.• Context Window: The total number of tokens the model can "remember" per interaction.4️⃣ Inference & Generation• Temperature: Adjusts output randomness; lower values are more deterministic.• Max Tokens: Limits the number of tokens in a generated response.• Seed: Controls reproducibility of outputs.• Latency: Time taken by the model to respond.• Hallucination: When the model generates factually incorrect but plausible-sounding information.5️⃣ Retrieval-Augmented Generation (RAG)• Retrieval: Fetching relevant data from external sources before generation.• Semantic Search: Search based on meaning, not just keywords.• Embeddings: Vector representations of text used for similarity matching.• Chunks: Dividing documents into smaller segments for better retrieval.• Vector Databases (VectorDBs): Stores embeddings for fast and accurate search.• Reranking: Rescoring retrieved documents to prioritize relevance.• Indexing: Structuring data for efficient retrieval during generation.🌟 Follow the AIKaDoctor (Free AI & Data Science Resources) channel on WhatsApp: https://lnkd.in/dCTCEKKc📌Follow Habib Shaikh For more such content.

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Harshil Vyas

Anyscale • 3K followers

"Every engineer is a Data/ML Engineer... if the Infra is right!" 🚀 Srikanth Sundarrajan's opening slide was a mic-drop moment that had every Platform and Infra engineer in the room (including me!) nodding in agreement. It perfectly captures our own (Anyscale) core mission: to build infrastructure so seamless it empowers everyone to innovate with data, AI & ML.InMobi's Encore'25 provided a fantastic look under the hood of their AI/ML infrastructure. For anyone obsessed with building and scaling machine learning systems, this was gold.Three things that stood out:Cost-Effective Model Training: Deep dive into their strategies for optimizing large-scale training jobs, intelligently leveraging spot instances and right-sizing resources without compromising on model development velocity.Real-Time Feature Stores: A key focus was their approach to building and maintaining a feature store that serves up-to-date data with single-digit millisecond latency, which is critical for their real-time bidding (RTB) models.The Rise of GenAI Infra: It was exciting to hear about their early infrastructure bets for supporting Generative AI and LLMs, particularly for dynamic creative optimization. This signals a major shift in the computational demands for the next generation of AdTech.Kudos to the InMobi engineering teams for their transparency and for building a truly impressive, high-performance ML platform.#AI #ML #Infrastructure #Encore25 #InMobi #MLOps #FeatureStore #GenAI #CloudComputing #AdTech

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Palantir Technologies

602K followers

Watch the full DevCon2 keynote from Palantir Chief Architect Akshay Krishnaswamy where he discussed what it takes to build AI products, how to maximize leverage for builders, and the architecture of Palantir AIP and Foundry.https://lnkd.in/gaWbmqas

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Deepak Maini

Walmart • 7K followers

The Clarity–Accuracy TradeoffWhy do smart teams get stuck?It’s the tension between clarity and accuracy.Clarity is simple, actionable — a clean framework you can remember under pressure. Accuracy is complex, comprehensive — every variable accounted for, every edge case covered.The paradox? The more we chase perfect information upfront, the longer we stay frozen.It’s like planning a road trip. You can spend days mapping every rest stop, or you can pick a direction and start driving.The shift that changed everything for me: Clarity first, precision later. Direction beats perfection every time.What’s one area where “good enough” clarity could unlock your next step?

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Oliver Parker

8K followers

ROI is always a hot topic with AI. Recently in my conversations with executives I heard the conversation shift away from “if” there is value in AI to “how and where” AI drives value across an organization. And now we have new data on exactly how AI is benefiting enterprises. We partnered with the team at IDC to survey hundreds of  Google Cloud customers to see what quantifiable benefit it is driving. We found a wide array of customer use cases like content creation (32% quicker content editing), application development (50% more productive developers) and data enablement (40% of once manually collected data now automated). It is amazing to see how our customers are putting AI to use! Read more with “How businesses achieve strong ROI with Google Cloud AI.” : https://lnkd.in/g9hZSP8y#GenerativeAI #GoogleCloudAI #GoogleCloudNext

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Roger Barga

I am a cloud and AI executive… • 10K followers

We’re thrilled today to introduce the OCI Generative AI Agents platform to create, deploy, and manage custom agents on OCI as part of the OCI Generative AI Agents Service. Along with this platform release, we are also launching a new pre-built tool for interacting with structured data through SQL as well as major enhancements to our existing Retrieval-Augmented Generation (RAG) tool. Check it out and let us know what you think, we are always looking for feedback and ideas for additional features.https://lnkd.in/gHq9kBkr

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AIM

357K followers

Datadog has promoted Namit D'Cruz to Regional Vice President for Enterprise in India and SAARC, underscoring the company’s growing focus on South Asia as demand accelerates for observability, security and AI-driven operations. Based in Bengaluru, D’Cruz will now lead Datadog’s enterprise strategy across the region, expanding customer relationships and scaling teams across sales, support, alliances, marketing and channel partnerships.“Enterprises are increasingly moving beyond experimentation to deploy AI for customer experience, scale, automation, and operational efficiency. As this shift accelerates, visibility, governance, and reliability across AI-driven systems become critical to building digital resilience,” said D’Cruz. He added, “Datadog helps organizations gain end-to-end observability across the AI stack, from infrastructure and GPUs to models and agents, while addressing cost control, risk detection, and responsible AI adoption. I’m excited to lead our regional enterprise team and work closely with customers as they navigate this next phase of transformation.”Rob Thorne, VP APJ at Datadog, said India has emerged as a key growth market for the company. “India has become a critical growth market for Datadog, driven by strong enterprise adoption of cloud, security, and AI-led operations,” he said. “Namit’s appointment comes at a pivotal moment for our India and SAARC business - his leadership, enterprise experience, and understanding of the regional market will be key as we scale our teams, deepen customer relationships, and drive the next phase of growth.”

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Prashant R.

Motive • 3K followers

I had a small but very interesting moment of tech evolution recently.After 5 years of hearing my name butchered in reminders, Alexa suddenly started pronouncing "Prashant Ramarao" perfectly. That's when it clicked - they must have integrated or upgraded an LLM to improve speech synthesis.So I decided to test other AI models:ChatGPT voice: Nailed it ✅Siri: Still says "Pruh shaaawnt Romario" 😂 - seriously Apple Intelligence?It's fascinating how these subtle upgrades reveal the quiet integration of language models into everyday products. For those of us with non-English names, finally hearing our names pronounced correctly by technology feels surprisingly meaningful - a small but significant step toward more inclusive AI.The best innovations often aren't flashy new features but these simple nuanced improvements that make technology feel more human.#AI #MachineLearning #UserExperience #Inclusion #TechnologyTrends

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Marc Brooker

Amazon Web Services (AWS) • 17K followers

Another great post from Marc Bowes about SQL performance and scaling in DSQL.Marc says:"In particular, TPC-B sucks on DSQL because it runs into the hot for write key problem. Every transaction touches the single branch entry. If you’re interested in scaling OLTP workloads, you already know not to do this. Hot keys perform way better on single-node systems that on DSQL, but they’re still fundamentally a performance inhibitor. With DSQL, we’ve focused our initial release on patterns that scale well, so that you don’t run into gotchas down the line."Check out the post here: https://lnkd.in/gYwR825u

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