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Fivetrans Hybrid Deployment
10Oct

Fivetrans Hybrid Deployment

Fivetran’s Hybrid Deployment: A Breakthrough in Data Engineering In the data engineering world, balancing efficiency with security has long been a challenge. Fivetran aims to shift this dynamic with its Hybrid Deployment solution, designed to seamlessly move data across any environment while maintaining control and flexibility. Fivetrans Hybrid Deployment. The Hybrid Advantage: Flexibility Meets Control Fivetran’s Hybrid Deployment offers a new approach for enterprises, particularly those handling sensitive data or operating in regulated sectors. Often, these businesses struggle to adopt data-driven practices due to security concerns. Hybrid Deployment changes this by enabling the secure movement of data across cloud and on-premises environments, giving businesses full control over their data while maintaining the agility of the cloud. As George Fraser, Fivetran’s CEO, notes, “Businesses no longer have to choose between managed automation and data control. They can now securely move data from all their critical sources—like Salesforce, Workday, Oracle, SAP—into a data warehouse or data lake, while keeping that data under their own control.” How it Works: A Secure, Streamlined Approach Fivetran’s Hybrid Deployment relies on a lightweight local agent to move data securely within a customer’s environment, while the Fivetran platform handles the management and monitoring. This separation of control and data planes ensures that sensitive information stays within the customer’s secure perimeter. Vinay Kumar Katta, a managing delivery architect at Capgemini, highlights the flexibility this provides, enabling businesses to design pipelines without sacrificing security. Beyond Security: Additional Benefits Hybrid Deployment’s benefits go beyond just security. It also offers: Early adopters are already seeing its value. Troy Fokken, chief architect at phData, praises how it “streamlines data pipeline processes,” especially for customers in regulated industries. AI Agent Architectures: Defining the Future of Autonomous Systems In the rapidly evolving world of AI, a new framework is emerging—AI agents designed to act autonomously, adapt dynamically, and explore digital environments. These AI agents are built on core architectural principles, bringing the next generation of autonomy to AI-driven tasks. What Are AI Agents? AI agents are systems designed to autonomously or semi-autonomously perform tasks, leveraging tools to achieve objectives. For instance, these agents may use APIs, perform web searches, or interact with digital environments. At their core, AI agents use Large Language Models (LLMs) and Foundation Models (FMs) to break down complex tasks, similar to human reasoning. Large Action Models (LAMs) Just as LLMs transformed natural language processing, Large Action Models (LAMs) are revolutionizing how AI agents interact with environments. These models excel at function calling—turning natural language into structured, executable actions, enabling AI agents to perform real-world tasks like scheduling or triggering API calls. Salesforce AI Research, for instance, has open-sourced several LAMs designed to facilitate meaningful actions. LAMs bridge the gap between unstructured inputs and structured outputs, making AI agents more effective in complex environments. Model Orchestration and Small Language Models (SLMs) Model orchestration complements LAMs by utilizing smaller, specialized models (SLMs) for niche tasks. Instead of relying on resource-heavy models, AI agents can call upon these smaller models for specific functions—such as summarizing data or executing commands—creating a more efficient system. SLMs, combined with techniques like Retrieval-Augmented Generation (RAG), allow smaller models to perform comparably to their larger counterparts, enhancing their ability to handle knowledge-intensive tasks. Vision-Enabled Language Models for Digital Exploration AI agents are becoming even more capable with vision-enabled language models, allowing them to interact with digital environments. Projects like Apple’s Ferret-UI and WebVoyager exemplify this, where agents can navigate user interfaces, recognize elements via OCR, and explore websites autonomously. Function Calling: Structured, Actionable Outputs A fundamental shift is happening with function calling in AI agents, moving from unstructured text to structured, actionable outputs. This allows AI agents to interact with systems more efficiently, triggering specific actions like booking meetings or executing API calls. The Role of Tools and Human-in-the-Loop AI agents rely on tools—algorithms, scripts, or even humans-in-the-loop—to perform tasks and guide actions. This approach is particularly valuable in high-stakes industries like healthcare and finance, where precision is crucial. The Future of AI Agents With the advent of Large Action Models, model orchestration, and function calling, AI agents are becoming powerful problem solvers. These agents are evolving to explore, learn, and act within digital ecosystems, bringing us closer to a future where AI mimics human problem-solving processes. As AI agents become more sophisticated, they will redefine how we approach digital tasks and interactions. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 10, 2024in Data, Enterprise, Salesforce
Small Language Models Explained
30Sep

Small Language Models Explained

Exploring Small Language Models (SLMs): Capabilities and Applications Large Language Models (LLMs) have been prominent in AI for some time, but Small Language Models (SLMs) are now enhancing our ability to work with natural and programming languages. While LLMs excel in general language understanding, certain applications require more accuracy and domain-specific knowledge than these models can provide. This has created a demand for custom SLMs that offer LLM-like performance while reducing runtime costs and providing a secure, manageable environment. In this insight, we dig down into the world of SLMs, exploring their unique characteristics, benefits, and applications. We also discuss fine-tuning methods applied to Llama-2–13b, an SLM, to address specific challenges. The goal is to investigate how to make the fine-tuning process platform-independent. We selected Databricks for this purpose due to its compatibility with major cloud providers like Azure, Amazon Web Services (AWS), and Google Cloud Platform. What Are Small Language Models? In AI and natural language processing, SLMs are lightweight generative models with a focus on specific tasks. The term “small” refers to: SLMs like Google Gemini Nano, Microsoft’s Orca-2–7b, and Meta’s Llama-2–13b run efficiently on a single GPU and include over 5 billion parameters. SLMs vs. LLMs Applications of SLMs SLMs are increasingly used across various sectors, including healthcare, technology, and beyond. Common applications include: Fine-Tuning Small Language Models Fine-tuning involves additional training of a pre-trained model to make it more domain-specific. This process updates the model’s parameters with new data to enhance its performance in targeted applications, such as text generation or question answering. Hardware Requirements for Fine-Tuning The hardware needs depend on the model size, project scale, and dataset. General recommendations include: Data Preparation Preparing data involves extracting text from PDFs, cleaning it, generating question-and-answer pairs, and then fine-tuning the model. Although GPT-3.5 was used for generating Q&A pairs, SLMs can also be utilized for this purpose based on the use case. Fine-Tuning Process You can use HuggingFace tools for fine-tuning Llama-2–13b-chat-hf. The dataset was converted into a HuggingFace-compatible format, and quantization techniques were applied to optimize performance. The fine-tuning lasted about 16 hours over 50 epochs, with the cost around $100/£83, excluding trial costs. Results and Observations The fine-tuned model demonstrated strong performance, with over 70% of answers being highly similar to those generated by GPT-3.5. The SLM achieved comparable results despite having fewer parameters. The process was successful on both AWS and Databricks platforms, showcasing the model’s adaptability. SLMs have some limitations compared to LLMs, such as higher operational costs and restricted knowledge bases. However, they offer benefits in efficiency, versatility, and environmental impact. As SLMs continue to evolve, their relevance and popularity are likely to increase, especially with new models like Gemini Nano and Mixtral entering the market. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 30, 2024in Salesforce
Large and Small Language Models
13Sep

Large and Small Language Models

Understanding Language Models in AI Language models are sophisticated AI systems designed to generate natural human language, a task that is far from simple. These models operate as probabilistic machine learning systems, predicting the likelihood of word sequences to emulate human-like intelligence. In the scientific realm, the focus of language models has been twofold: While today’s cutting-edge AI models in Natural Language Processing (NLP) are impressive, they have not yet fully passed the Turing Test—a benchmark where a machine’s communication is indistinguishable from that of a human. The Emergence of Language Models We are approaching this milestone with advancements in Large Language Models (LLMs) and the promising but less discussed Small Language Models (SLMs). Large Language Models compared to Small Language Models LLMs like ChatGPT have garnered significant attention due to their ability to handle complex interactions and provide insightful responses. These models distill vast amounts of internet data into concise and relevant information, offering an alternative to traditional search methods. Conversely, SLMs, such as Mistral 7B, while less flashy, are valuable for specific applications. They typically contain fewer parameters and focus on specialized domains, providing targeted expertise without the broad capabilities of LLMs. How LLMs Work Comparing LLMs and SLMs Choosing the Right Language Model The decision between LLMs and SLMs depends on your specific needs and available resources. LLMs are well-suited for broad applications like chatbots and customer support. In contrast, SLMs are ideal for specialized tasks in fields such as medicine, law, and finance, where domain-specific knowledge is crucial. Large and Small Language Models’ Roles Language models are powerful tools that, depending on their size and focus, can either provide broad capabilities or specialized expertise. Understanding their strengths and limitations helps in selecting the right model for your use case. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 13, 2024in Salesforce
Small Language Models
10Aug

Small Language Models

Large language models (LLMs) like OpenAI’s GPT-4 have gained acclaim for their versatility across various tasks, but they come with significant resource demands. In response, the AI industry is shifting focus towards smaller, task-specific models designed to be more efficient. Microsoft, alongside other tech giants, is investing in these smaller models. Science often involves breaking complex systems down into their simplest forms to understand their behavior. This reductionist approach is now being applied to AI, with the goal of creating smaller models tailored for specific functions. Sébastien Bubeck, Microsoft’s VP of generative AI, highlights this trend: “You have this miraculous object, but what exactly was needed for this miracle to happen; what are the basic ingredients that are necessary?” In recent years, the proliferation of LLMs like ChatGPT, Gemini, and Claude has been remarkable. However, smaller language models (SLMs) are gaining traction as a more resource-efficient alternative. Despite their smaller size, SLMs promise substantial benefits to businesses. Microsoft introduced Phi-1 in June last year, a smaller model aimed at aiding Python coding. This was followed by Phi-2 and Phi-3, which, though larger than Phi-1, are still much smaller than leading LLMs. For comparison, Phi-3-medium has 14 billion parameters, while GPT-4 is estimated to have 1.76 trillion parameters—about 125 times more. Microsoft touts the Phi-3 models as “the most capable and cost-effective small language models available.” Microsoft’s shift towards SLMs reflects a belief that the dominance of a few large models will give way to a more diverse ecosystem of smaller, specialized models. For instance, an SLM designed specifically for analyzing consumer behavior might be more effective for targeted advertising than a broad, general-purpose model trained on the entire internet. SLMs excel in their focused training on specific domains. “The whole fine-tuning process … is highly specialized for specific use-cases,” explains Silvio Savarese, Chief Scientist at Salesforce, another company advancing SLMs. To illustrate, using a specialized screwdriver for a home repair project is more practical than a multifunction tool that’s more expensive and less focused. This trend towards SLMs reflects a broader shift in the AI industry from hype to practical application. As Brian Yamada of VLM notes, “As we move into the operationalization phase of this AI era, small will be the new big.” Smaller, specialized models or combinations of models will address specific needs, saving time and resources. Some voices express concern over the dominance of a few large models, with figures like Jack Dorsey advocating for a diverse marketplace of algorithms. Philippe Krakowski of IPG also worries that relying on the same models might stifle creativity. SLMs offer the advantage of lower costs, both in development and operation. Microsoft’s Bubeck emphasizes that SLMs are “several orders of magnitude cheaper” than larger models. Typically, SLMs operate with around three to four billion parameters, making them feasible for deployment on devices like smartphones. However, smaller models come with trade-offs. Fewer parameters mean reduced capabilities. “You have to find the right balance between the intelligence that you need versus the cost,” Bubeck acknowledges. Salesforce’s Savarese views SLMs as a step towards a new form of AI, characterized by “agents” capable of performing specific tasks and executing plans autonomously. This vision of AI agents goes beyond today’s chatbots, which can generate travel itineraries but not take action on your behalf. Salesforce recently introduced a 1 billion-parameter SLM that reportedly outperforms some LLMs on targeted tasks. Salesforce CEO Mark Benioff celebrated this advancement, proclaiming, “On-device agentic AI is here!” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more

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August 10, 2024in Generative AI
Rold of Small Language Models
02Aug

Role of Small Language Models

The Role of Small Language Models (SLMs) in AI While much attention is often given to the capabilities of Large Language Models (LLMs), Small Language Models (SLMs) play a vital role in the AI landscape. Role of Small Language Models. Large vs. Small Language Models LLMs, like GPT-4, excel at managing complex tasks and providing sophisticated responses. However, their substantial computational and energy requirements can make them impractical for smaller organizations and devices with limited processing power. In contrast, SLMs offer a more feasible solution. Designed to be lightweight and resource-efficient, SLMs are ideal for applications operating in constrained computational environments. Their reduced resource demands make them easier and quicker to deploy, while also simplifying maintenance. What are Small Language Models? Small Language Models (SLMs) are neural networks engineered to generate natural language text. The term “small” refers not only to the model’s physical size but also to its parameter count, neural architecture, and the volume of data used during training. Parameters are numeric values that guide a model’s interpretation of inputs and output generation. Models with fewer parameters are inherently simpler, requiring less training data and computational power. Generally, models with fewer than 100 million parameters are classified as small, though some experts consider models with as few as 1 million to 10 million parameters to be small in comparison to today’s large models, which can have hundreds of billions of parameters. How Small Language Models Work SLMs achieve efficiency and effectiveness with a reduced parameter count, typically ranging from tens to hundreds of millions, as opposed to the billions seen in larger models. This design choice enhances computational efficiency and task-specific performance while maintaining strong language comprehension and generation capabilities. Techniques such as model compression, knowledge distillation, and transfer learning are critical for optimizing SLMs. These methods enable SLMs to encapsulate the broad understanding capabilities of larger models into a more concentrated, domain-specific toolset, facilitating precise and effective applications while preserving high performance. Advantages of Small Language Models Applications of Small Language Models Role of Small Language Models is lengthy. SLMs have seen increased adoption due to their ability to produce contextually coherent responses across various applications: Small Language Models vs. Large Language Models Feature LLMs SLMs Training Dataset Broad, diverse internet data Focused, domain-specific data Parameter Count Billions Tens to hundreds of millions Computational Demand High Low Cost Expensive Cost-effective Customization Limited, general-purpose High, tailored to specific needs Latency Higher Lower Security Risk of data exposure through APIs Lower risk, often not open source Maintenance Complex Easier Deployment Requires substantial infrastructure Suitable for limited hardware environments Application Broad, including complex tasks Specific, domain-focused tasks Accuracy in Specific Domains Potentially less accurate due to general training High accuracy with domain-specific training Real-time Application Less ideal due to latency Ideal due to low latency Bias and Errors Higher risk of biases and factual errors Reduced risk due to focused training Development Cycles Slower Faster Conclusion The role of Small Language Models (SLMs) is increasingly significant as they offer a practical and efficient alternative to larger models. By focusing on specific needs and operating within constrained environments, SLMs provide targeted precision, cost savings, improved security, and quick responsiveness. As industries continue to integrate AI solutions, the tailored capabilities of SLMs are set to drive innovation and efficiency across various domains. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 2, 2024in Salesforce

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