Luis J Pinto B’s Post

Llama 3.1 405B has made history by narrowing the gap with closed-source models like never before. While it isn’t completely open-source, the details available about its architecture and open hyperparameters are highly beneficial for developers. Hyperparameters are crucial settings configured before the model's training or fine-tuning. They play a significant role in the model's performance and learning. Examples include model size, tokenizer settings, learning rate, and optimizer. Having access to open hyperparameters helps developers understand the training process and replicate or fine-tune the model according to their needs. Fine-tuning involves updating the model's parameters (weights) to adapt it to specific tasks while keeping the hyperparameters constant. In simpler terms, hyperparameters set the stage for the model’s training, while model parameters are adjusted to tailor the model to specific data or tasks. Understanding these concepts helps in effectively utilizing and refining models for various applications. #opensource #generativeai #technology #innovation #artificialintelligence

Bem Secures $3.7M to Automate Unstructured Data Conversion - Funding Announcement: Bem raises $3.7 million from Uncork Capital and other investors to develop its AI-driven data interface. - AI Data Interface: Bem’s API converts unstructured data into structured formats, targeting engineers and developers. - Founders’ Vision: Founders Upal Saha and Antonio Bustamante aim to streamline data pipelines and eliminate manual data processing. Subscribe to our daily newsletter here for more AI news https://lnkd.in/dqBqB7EY #AI #Startups #VentureCapital #DataScience #BigData #SoftwareEngineering #Analytics #Technology #Innovation #ArtificialIntelligence #Business

Say goodbye to tedious data prep tasks and simplify your workflow with Kranium’s no-code tools! Our AI platform simplifies and speeds up every step of the data preparation process, from data loading to cleaning, balancing, and transformation. With Kranium, you can automate these critical tasks, freeing up your time to focus on analysis and insights. Whether you're a data scientist, AI engineer or a business analyst, Kranium is your go-to solution for seamless data management. #DataPreparation #NoCode #AIPlatform #Kranium #DataScience #Automation #TechInnovation

Data preparation is often a time-consuming and complex task that can hinder the efficiency of your data-driven projects. With Kranium, our advanced AI platform, you can accelerate this process using our intuitive no-code tools. Kranium provides a comprehensive suite of features designed to streamline and automate every step of data preparation, including: - Data Loading: Seamlessly import data from various sources with just a few clicks. - Data Cleaning: Automatically identify and correct errors, inconsistencies, and missing values to ensure your data is accurate and reliable. - Data Balancing: Achieve balanced datasets effortlessly, improving the quality and performance of your models. - Data Transformation: Easily apply transformations to your data to make it ready for analysis and modeling. By leveraging Kranium’s no-code tools, you can significantly reduce the time and effort required for data preparation, allowing you to focus on extracting valuable insights and driving impactful decisions. Whether you are a data scientist, analyst, or business professional, Kranium empowers you to handle data preparation with ease and efficiency. Experience the future of data preparation with Kranium and unlock the full potential of your data today. #DataPreparation #NoCodeTools #AIPlatform #Kranium #DataAutomation #DataScience #TechInnovation

In the realm of problem-solving, models stand as invaluable allies, offering structured frameworks to navigate vast seas of data and hypotheses: 🧭 They're the compass guiding us through the complexity, providing clarity and direction. But what truly makes problem-solving soar is when these models join forces with domain expertise, creating a powerful synergy that propels us towards effective solutions. Models, with their ability to crunch numbers and simulate scenarios, offer us a bird's-eye view of intricate systems and processes.  They unravel patterns hidden within the data, illuminating pathways forward and identifying potential pitfalls. However, alone, they can sometimes feel like a map without legends – comprehensive but lacking in the nuanced understanding of the terrain. This is where expertise steps in: Like a seasoned navigator, domain knowledge lends depth and context to the analysis. It's the compass that ensures we stay grounded in reality, steering us away from the treacherous shoals of oversimplified assumptions. With expertise as our guide, decisions become not just informed, but enriched with practical wisdom, ensuring they resonate with the real-world intricacies of your industry. One real-life example is the development of new pharmaceutical drugs:  📌 Scientists use computer models to simulate the effects of different compounds on cells, while medical experts provide their knowledge and experience to guide the selection and testing process. 📌 By integrating modeling and expertise, decisions are informed by both theoretical concepts and practical knowledge from those with experience in the field. This helps ensure that decisions are not solely based on abstract theories but also take into account real-world considerations. Together, the model and expertise create a roadmap for efficient production that's not just theoretically sound but pragmatically feasible. 🤝 In essence, when modeling meets expertise, magic happens. It's the fusion of data-driven analysis with real-world insight, propelling us towards innovative solutions and transformative outcomes.  Would you like to know more? Contact us https://lnkd.in/eEsxbUJA #compliag #simulation #modeling #expertise #ProblemSolving #DataDriven #ExpertInsight #Innovation

The concept of FAIR was initially launched at a Lorentz Center workshop in 2014, but the formal principles were not published until 2016. Since their introduction, the FAIR principles have gained widespread acceptance and have been adopted as standards for data management across various domains So, as we celebrate a decade of FAIR data principles, why is drug discovery still catching on? Awareness? Training? Culture? Fragmentation? Data siloes? Sunken costs? Legacy software?... #datamanagement #reproducibility #datascience #drugdiscovery

Feeling overwhelmed by your machine learning projects? Let CriticalRiver Inc.'s MLOps-as-a-Service turn your complexities into clear pathways to success. Our approach simplifies your journey, transforming challenges into opportunities for innovation and growth. With CriticalRiver, it's not just about solving problems—it's about unlocking potential. Let's pave the way to digital excellence together. https://lnkd.in/gTVPtU_6 #CriticalRiver #FollowTheRiver #mlops #digitaltransformation #automationsolutions #aiml #digitalexcellence

The difference between 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗶𝗻 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻𝘀. 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: Expand your team by adding more machines to distribute the workload efficiently. Think of it as hiring more hands for the job. 𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: Enhance existing resources within a single machine, boosting its processing power, memory, or storage capacity. Empower your MVP to handle heavier tasks single-handedly. Choosing the right approach: -𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Horizontal scaling is ideal for surges in user traffic, allowing seamless expansion. Vertical scaling may hit limits depending on the machine's capacity. -𝗖𝗼𝘀𝘁 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Horizontal scaling offers a cost-effective solution, starting small and growing gradually. Vertical scaling requires upfront investment in high-end hardware. -𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Horizontal scaling allows for independent scaling of components, optimizing resource allocation. Vertical scaling might lead to resource underutilization. Tailor your strategy based on your system's needs, growth projections, and budget. 𝗖𝗵𝗲𝗰𝗸𝗼𝘂𝘁 𝘁𝗵𝗲 𝗮𝘁𝘁𝗮𝗰𝗵𝗲𝗱 𝗖𝗿𝗼𝘂𝘀𝗲𝗹 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗰𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗶𝗼𝗻 𝗰𝗵𝗮𝗿𝘁 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗯𝗼𝘁𝗵. 𝗖𝗿𝗮𝗰𝗸 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗮𝘁 𝗠𝗔𝗔𝗡𝗚 𝗮𝗻𝗱 𝗧𝗼𝗽 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗯𝗮𝘀𝗲𝗱 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 - Learn Data Structures, Algorithms & Problem-solving techniques - Domain Specialization in Data Science, Machine Learning & AI - System Design Preparation (HLD + LLD) Follow Logicmojo Academy for more such posts. #systemdesign #scaling #datascience #logicmojo

Model training seriously stresses data infrastructure, but preparing that data to be used is a much more difficult challenge. This episode of Utilizing Tech features Subramanian Kartik, Ph. D of VAST Data discussing the broad data pipeline with Jeniece Wnorowski of Solidigm and Stephen Foskett. #UtilizingTech #Sponsored #AIDataInfrastructure

“Right now... only 11% of data scientists say that they have gotten their own organization’s data into shape to produce useful answers.” WSJ, 5/9. Both in my board seat for FINOS and in day-to-day executive conversations to enable data/data platforms, THE DATA IS FOUNDATIONAL. See Databricks #deltalake #lakehouse and #unitycatalog to accelerate data democratization for your team. You cannot run a model w/ exceptional insight without getting your data right to start. Data democratization = getting clean, performant data faster to your teams in a secure and governed way. ✋ 🎤 https://lnkd.in/eDMvK2xi