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Machine Learning on Kubernetes
05Sep

Machine Learning on Kubernetes

How and Why to Run Machine Learning Workloads on Kubernetes Running machine learning (ML) model development and deployment on Kubernetes has become essential for optimizing resources and managing costs. As AI and ML tools gain mainstream acceptance, business and IT professionals are increasingly familiar with these technologies. With the growing buzz around AI, engineering needs in ML and AI have expanded, particularly in managing the complexities and costs associated with these workloads. The Need for Kubernetes in ML As ML use cases become more complex, training models has become increasingly resource-intensive and costly. This has driven up demand and costs for GPUs, a key resource for ML tasks. Containerizing ML workloads offers a solution to these challenges by improving scalability, automation, and infrastructure efficiency. Kubernetes, a leading tool for container orchestration, is particularly effective for managing ML processes. By decoupling workloads into manageable containers, Kubernetes helps streamline ML operations and reduce costs. Understanding Kubernetes The evolution of engineering priorities has consistently focused on minimizing application footprints. From mainframes to modern servers and virtualization, the trend has been towards reducing operational overhead. Containers emerged as a solution to this trend, offering a way to isolate application stacks while maintaining performance. Initially, containers used Linux cgroups and namespaces, but their popularity surged with Docker. However, Docker containers had limitations in scaling and automatic recovery. Kubernetes was developed to address these issues. As an open-source orchestration platform, Kubernetes manages containerized workloads by ensuring containers are always running and properly scaled. Containers run inside resources called pods, which include everything needed to run the application. Kubernetes has also expanded its capabilities to orchestrate other resources like virtual machines. Running ML Workloads on Kubernetes ML systems demand significant computing power, including CPU, memory, and GPU resources. Traditionally, this required multiple servers, which was inefficient and costly. Kubernetes addresses this challenge by orchestrating containers and decoupling workloads, allowing multiple pods to run models simultaneously and share resources like CPU, memory, and GPU power. Using Kubernetes for ML can enhance practices such as: Challenges of ML on Kubernetes Despite its advantages, running ML workloads on Kubernetes comes with challenges: Key Tools for ML on Kubernetes Kubernetes requires specific tools to manage ML workloads effectively. These tools integrate with Kubernetes to address the unique needs of ML tasks: TensorFlow is another option, but it lacks the dedicated integration and optimization of Kubernetes-specific tools like Kubeflow. For those new to running ML workloads on Kubernetes, Kubeflow is often the best starting point. It is the most advanced and mature tool in terms of capabilities, ease of use, community support, and functionality. 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 5, 2024in Salesforce
Change The Flow
05Sep

Change The Flow

Salesforce has long been a leader in providing tools to automate business processes, with Workflow Rules and Process Builder as the go-to solutions for many organizations. However, as business demands grow more complex, Salesforce has introduced Flow—a more powerful and flexible automation tool that’s quickly becoming the standard. This insight will explore the key differences between Salesforce Flow, Process Builder, and Workflow Rules, and why Flow is considered the future of Salesforce automation. Workflow Rules: The Foundation of Salesforce Automation For years, Workflow Rules served as a reliable tool for automating basic tasks in Salesforce. Based on simple “if/then” logic, Workflow Rules automate actions such as sending email alerts, updating fields, and creating tasks. While effective for straightforward needs, Workflow Rules have significant limitations. They can’t create or update related records, and each rule can only trigger a single action—constraints that hinder more complex business processes. Process Builder: A Step Up in Complexity and Functionality Process Builder was introduced as a more advanced alternative to Workflow Rules, offering a visual interface that simplifies building automations. It allows for multiple actions to be triggered by a single event and supports more complex logic, including branching criteria. Process Builder also introduces a broader set of actions, such as creating records, posting to Chatter, and invoking Apex code. However, as businesses pushed Process Builder’s capabilities, its limitations in terms of performance and scalability became clear. Salesforce Flow: The Future of Automation Salesforce Flow combines the capabilities of both Workflow Rules and Process Builder while introducing powerful new features. Flows can automate nearly any process within Salesforce, from simple tasks like updating records to intricate workflows involving multiple objects and even external systems. Flow can be triggered by a variety of events, including record changes, scheduled times, and platform events, providing far more flexibility than its predecessors. One of Flow’s key strengths is its versatility. It can include screen elements for user interaction or run entirely in the background, making it suitable for a wide range of use cases. Whether automating internal processes or creating customer-facing applications, Flow’s adaptability shines. Salesforce continues to enhance Flow, closing the feature gaps that once existed between Flow and the older automation tools. This, coupled with a clear migration path, makes Flow the logical choice for the future. Why Salesforce Flow is the Way Forward Salesforce has already announced plans to retire Workflow Rules and Process Builder in favor of Flow, signaling a shift toward a more unified and scalable automation platform. Businesses still relying on the older tools should transition to Flow sooner rather than later. Not only will this ensure continued support and access to new features, but it will also allow organizations to leverage Salesforce’s most advanced automation tool. When comparing Salesforce Flow vs. Process Builder and Workflow Rules, it’s evident that Flow offers the most robust, flexible, and future-proof solution. Its ability to handle complex processes and its continuous enhancements make it the ideal choice for modern businesses. As Salesforce phases out Workflow Rules and Process Builder, migrating to Flow will equip your organization with the latest in automation capabilities. Ready to Make the Switch? Start exploring Salesforce Flow today and discover how it can transform your business processes for the better. Contact Tectonic for assistance. 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 5, 2024in Salesforce

Linus Torvalds Insights

Linus Torvalds Shares Insights on the Future of Programming with AI Linus Torvalds, the mastermind behind Linux and Git—two cornerstones of modern software development—recently shared his perspective on how artificial intelligence (AI) is reshaping the world of programming. His candid insights offer a balanced view of AI’s capabilities and limitations, coming from one of the industry’s most influential voices. If you prefer a quick breakdown over watching a full interview, here are the key takeaways from Torvalds’ conversation. AI in Programming: Evolution, Not Revolution Torvalds describes AI, particularly large language models (LLMs), as “autocorrect on steroids.” These tools excel at predicting the next word or line of code based on established patterns but aren’t “intelligent” in the human sense. Rather than a seismic shift, AI represents the next step in a long history of automation in coding. From the days of machine language to today’s high-level languages like Python and Rust, tools have continuously evolved to make developers’ lives easier. AI is just another link in this chain—helping write, refine, and debug code while boosting productivity. AI as a Developer’s Supercharged Assistant Far from being a replacement for human programmers, Torvalds sees AI as a powerful assistant. Tools like GitHub Copilot are already enhancing the coding process by suggesting fixes, spotting bugs, and speeding up routine tasks. The vision? A future where programmers can abstract tasks even further, possibly instructing AI in plain English. Imagine simply saying, “Build me a tool to manage my expenses,” and watching it happen. However, for now, AI is an incremental improvement, not a groundbreaking leap. The Shift Toward AI-Generated Code One of Torvalds’ more intriguing predictions is that AI may eventually write code in ways incomprehensible to human programmers. Since AI doesn’t require human-readable syntax, it could optimize code in ways that only it understands. In this scenario, developers might transition from writing code to managing AI systems that generate and refine it—shifting from hands-on creators to overseers of automated processes. AI in Code Review: Smarter Intern or Future Partner? When it comes to code review, AI’s potential is clear. Torvalds notes that AI could efficiently catch simple errors—like typos or syntax mistakes—freeing up human reviewers to focus on more complex logic and functionality. While AI might streamline tedious tasks, it’s far from perfect. Issues like “hallucinations,” where AI confidently produces incorrect results, highlight the need for human oversight. AI can assist, but it still requires developers to verify its output. A Balanced Take on AI and Jobs Torvalds dismisses fears of AI taking over programming jobs, pointing out that technological advancements historically create new opportunities rather than eliminate roles. AI, in his view, is less about replacing humans and more about augmenting their abilities. It’s a tool to make developers more efficient—not a harbinger of obsolescence. Final Thoughts: Embrace AI, But Stay Grounded Linus Torvalds envisions AI as a valuable, evolving tool for programmers, not a threat to their livelihood. While it’s set to change how we code, the shift will be gradual rather than revolutionary. Whether you’re a seasoned developer or a newcomer, now is the time to explore AI-powered tools, embrace their potential, and adapt to this new era of programming. Instead of fearing change, we can use AI to push the boundaries of what’s possible. 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 4, 2024in Artificial Intelligence
Five9 Cautious Forward Looking
04Sep

Five9 Cautious Forward Looking

Recently, Five9 reported its second-quarter FY24 results, revealing a strong performance for the period. However, the company’s cautious forward-looking guidance led to a significant drop in its stock price, which fell by over 25%. In response to queries about the conservative outlook, a Five9 spokesperson attributed the reduced 2024 revenue guidance—a 3.8% decrease—to macroeconomic headwinds. This cautious forecast stands in contrast to the more optimistic outlooks of Five9’s publicly traded peers. Economic factors such as global issues, talent shortages, AI uncertainty, and the upcoming election are influencing customers’ decisions on IT investments, which likely contributed to the reduced guidance. Additionally, sales execution challenges have prompted the company to take corrective measures. While Five9 might face unique challenges that other CCaaS providers do not, the full impact will become clearer in the next quarter. In response to these challenges, Five9 has taken steps to stabilize its operations, including promoting Matt Tuckness from VP of Global Customer Success to EVP of Sales and Customer Success. This move, described by leadership as promoting a “dedicated sales leader” with a decade of experience at Five9, aims to enhance sales execution. Scott Berg from Needham questioned the timing of the promotion, suggesting it might be a reaction to a single quarter’s results. Dan Burkland, Five9’s President, defended the decision, emphasizing that having a dedicated EVP of Sales is crucial for focusing on enterprise deals, especially given Five9’s efforts to grow its enterprise base. Five9 has also announced a 7% workforce reduction, affecting approximately 185 employees. This marks the company’s first layoff in its history, which is notable given its history of growth through acquisitions, such as the recent planned acquisition of Acqueon, a real-time revenue execution platform. Typically, acquisitions lead to headcount adjustments, but Five9 had managed to avoid such cuts until now. The company stated that the reduction was necessary to focus on profitable growth and long-term business resilience while continuing to serve global customers and innovate. Although layoffs are challenging, they are sometimes necessary for business adaptation. Many UCaaS and CCaaS providers expanded their workforces during the pandemic and later faced the need to trim excess staff as the market softened. Five9’s adjustment in headcount reflects changing market conditions. The acquisition of Acqueon is expected to accelerate Five9’s vision by integrating expertise in inbound and outbound communications to enhance personalized customer experiences across marketing, sales, and service. Acqueon will operate as a separate business unit within Five9, with plans to eventually integrate its brand into the larger Five9 brand. Overall, despite the quarter’s challenges, Five9 had a strong performance. It achieved a record-breaking billion ARR run rate for the first time, with total subscription revenue growing by 17%. The company maintains a robust balance sheet with over $1 billion in cash. The recent organizational changes, including new leadership and headcount adjustments, are indicative of Five9’s maturation and aim to return the company to its pattern of strong performance and growth. 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 4, 2024in Salesforce
2024 AI Glossary
02Sep

2024 AI Glossary

Artificial intelligence (AI) has moved from an emerging technology to a mainstream business imperative, making it essential for leaders across industries to understand and communicate its concepts. To help you unlock the full potential of AI in your organization, this 2024 AI Glossary outlines key terms and phrases that are critical for discussing and implementing AI solutions. Tectonic 2024 AI Glossary Active LearningA blend of supervised and unsupervised learning, active learning allows AI models to identify patterns, determine the next step in learning, and only seek human intervention when necessary. This makes it an efficient approach to developing specialized AI models with greater speed and precision, which is ideal for businesses aiming for reliability and efficiency in AI adoption. AI AlignmentThis subfield focuses on aligning the objectives of AI systems with the goals of their designers or users. It ensures that AI achieves intended outcomes while also integrating ethical standards and values when making decisions. AI HallucinationsThese occur when an AI system generates incorrect or misleading outputs. Hallucinations often stem from biased or insufficient training data or incorrect model assumptions. AI-Powered AutomationAlso known as “intelligent automation,” this refers to the integration of AI with rules-based automation tools like robotic process automation (RPA). By incorporating AI technologies such as machine learning (ML), natural language processing (NLP), and computer vision (CV), AI-powered automation expands the scope of tasks that can be automated, enhancing productivity and customer experience. AI Usage AuditingAn AI usage audit is a comprehensive review that ensures your AI program meets its goals, complies with legal requirements, and adheres to organizational standards. This process helps confirm the ethical and accurate performance of AI systems. Artificial General Intelligence (AGI)AGI refers to a theoretical AI system that matches human cognitive abilities and adaptability. While it remains a future concept, experts predict it may take decades or even centuries to develop true AGI. Artificial Intelligence (AI)AI encompasses computer systems that can perform complex tasks traditionally requiring human intelligence, such as reasoning, decision-making, and problem-solving. BiasBias in AI refers to skewed outcomes that unfairly disadvantage certain ideas, objectives, or groups of people. This often results from insufficient or unrepresentative training data. Confidence ScoreA confidence score is a probability measure indicating how certain an AI model is that it has performed its assigned task correctly. Conversational AIA type of AI designed to simulate human conversation using techniques like NLP and generative AI. It can be further enhanced with capabilities like image recognition. Cost ControlThis is the process of monitoring project progress in real-time, tracking resource usage, analyzing performance metrics, and addressing potential budget issues before they escalate, ensuring projects stay on track. Data Annotation (Data Labeling)The process of labeling data with specific features to help AI models learn and recognize patterns during training. Deep LearningA subset of machine learning that uses multi-layered neural networks to simulate complex human decision-making processes. Enterprise AIAI technology designed specifically to meet organizational needs, including governance, compliance, and security requirements. Foundational ModelsThese models learn from large datasets and can be fine-tuned for specific tasks. Their adaptability makes them cost-effective, reducing the need for separate models for each task. Generative AIA type of AI capable of creating new content such as text, images, audio, and synthetic data. It learns from vast datasets and generates new outputs that resemble but do not replicate the original data. Generative AI Feature GovernanceA set of principles and policies ensuring the responsible use of generative AI technologies throughout an organization, aligning with company values and societal norms. Human in the Loop (HITL)A feedback process where human intervention ensures the accuracy and ethical standards of AI outputs, essential for improving AI training and decision-making. Intelligent Document Processing (IDP)IDP extracts data from a variety of document types using AI techniques like NLP and CV to automate and analyze document-based tasks. Large Language Model (LLM)An AI technology trained on massive datasets to understand and generate text. LLMs are key in language understanding and generation and utilize transformer models for processing sequential data. Machine Learning (ML)A branch of AI that allows systems to learn from data and improve accuracy over time through algorithms. Model AccuracyA measure of how often an AI model performs tasks correctly, typically evaluated using metrics such as the F1 score, which combines precision and recall. Natural Language Processing (NLP)An AI technique that enables machines to understand, interpret, and generate human language through a combination of linguistic and statistical models. Retrieval Augmented Generation (RAG)This technique enhances the reliability of generative AI by incorporating external data to improve the accuracy of generated content. Supervised LearningA machine learning approach that uses labeled datasets to train AI models to make accurate predictions. Unsupervised LearningA type of machine learning that analyzes and groups unlabeled data without human input, often used to discover hidden patterns. By understanding these terms, you can better navigate the AI implementation world and apply its transformative power to drive innovation and efficiency across your organization. Tectonic 2024 AI Glossary 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 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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September 2, 2024in Generative AI
Winter 25 Permission Set Groups
02Sep

Winter 25 Permission Set Groups

Salesforce’s Winter ’25 release introduces a host of updates across the platform, with a particular emphasis on security and user management improvements. Among these, the enhancements to Permission Set Groups stand out, offering more efficiency in managing user access and permissions. Let’s take a closer look at these updates and how they can benefit your Salesforce environment. What Are Permission Set Groups? Before diving into the new enhancements, it’s essential to understand Permission Set Groups. Salesforce created these groups to simplify the assignment of permissions to users. Instead of assigning multiple individual permission sets, administrators can bundle them into a Permission Set Group. This approach streamlines the process, making it easier to manage permissions for users with complex roles requiring access to multiple features and objects. What’s New in Winter ’25? The Winter ’25 release brings several key updates to Permission Set Groups, making them more robust and flexible. Here’s a breakdown of the major improvements: Key Benefits of the Winter ’25 Enhancements The Winter ’25 updates to Permission Set Groups offer several advantages for Salesforce admins and organizations: Getting Started To begin utilizing these new features, head to the Permission Set Group settings in Salesforce Setup. Review your current permission sets and explore how these new features can streamline your processes. The expiration date feature, in particular, will be valuable if you manage temporary roles or frequently changing project teams. Winter 25 Permission Set Groups The Winter ’25 Salesforce release delivers significant improvements to Permission Set Groups, equipping admins with enhanced tools to manage user permissions securely and efficiently. By incorporating these features into your Salesforce environment, you can strengthen security, optimize user access management, and ensure your organization operates smoothly. For a deeper dive into these updates, check the Salesforce Winter ’25 release notes or join discussions in Salesforce communities and forums. 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 2, 2024in Salesforce
AI and Big Data
31Aug

AI and Big Data

Over the past decade, enterprises have accumulated vast amounts of data, capturing everything from business processes to inventory statistics. This surge in data marked the onset of the big data revolution. However, merely storing and managing big data is no longer sufficient to extract its full value. As organizations become adept at handling big data, forward-thinking companies are now leveraging advanced analytics and the latest AI and machine learning techniques to unlock even greater insights. These technologies can identify patterns and provide cognitive capabilities across vast datasets, enabling organizations to elevate their data analytics to new levels. Additionally, the adoption of generative AI systems is on the rise, offering more conversational approaches to data analysis and enhancement. This allows organizations to extract significant insights from information that would otherwise remain untapped in data stores. How Are AI and Big Data Related? Applying machine learning algorithms to big data is a logical progression for companies aiming to maximize the potential of their data. Unlike traditional rules-based approaches that follow explicit instructions, machine learning systems use data-driven algorithms and statistical models to analyze and detect patterns in data. Big data serves as the raw material for these systems, which derive valuable insights from it. Organizations are increasingly recognizing the benefits of integrating big data with machine learning. However, to fully harness the power of both, it’s crucial to understand their individual capabilities. Understanding Big Data Big data involves extracting and analyzing information from large quantities of data, but volume is just one aspect. Other critical “Vs” of big data that enterprises must manage include velocity, variety, veracity, validity, visualization, and value. Understanding Machine Learning Machine learning, the backbone of modern AI, adds significant value to big data applications by deriving deeper insights. These systems learn and adapt over time without the need for explicit programming, using statistical models to analyze and infer patterns from data. Historically, companies relied on complex, rules-based systems for reporting, which often proved inflexible and unable to cope with constant changes. Today, machine learning and deep learning enable systems to learn from big data, enhancing decision-making, business intelligence, and predictive analysis. The strength of machine learning lies in its ability to discover patterns in data. The more data available, the more these algorithms can identify patterns and apply them to future data. Applications range from recommendation systems and anomaly detection to image recognition and natural language processing (NLP). Categories of Machine Learning Algorithms Machine learning algorithms generally fall into three categories: The most powerful large language models (LLMs), which underpin today’s widely used generative AI systems, utilize a combination of these methods, learning from massive datasets. Understanding Generative AI Generative AI models are among the most powerful and popular AI applications, creating new data based on patterns learned from extensive training datasets. These models, which interact with users through conversational interfaces, are trained on vast amounts of internet data, including conversations, interviews, and social media posts. With pre-trained LLMs, users can generate new text, images, audio, and other outputs using natural language prompts, without the need for coding or specialized models. How Does AI Benefit Big Data? AI, combined with big data, is transforming businesses across various sectors. Key benefits include: Big Data and Machine Learning: A Synergistic Relationship Big data and machine learning are not competing concepts; when combined, they deliver remarkable results. Emerging big data techniques offer powerful ways to manage and analyze data, while machine learning models extract valuable insights from it. Successfully handling the various “Vs” of big data enhances the accuracy and power of machine learning models, leading to better business outcomes. The volume of data is expected to grow exponentially, with predictions of over 660 zettabytes of data worldwide by 2030. As data continues to amass, machine learning will become increasingly reliant on big data, and companies that fail to leverage this combination will struggle to keep up. Examples of AI and Big Data in Action Many organizations are already harnessing the power of machine learning-enhanced big data analytics: Conclusion The integration of AI and big data is crucial for organizations seeking to drive digital transformation and gain a competitive edge. As companies continue to combine these technologies, they will unlock new opportunities for personalization, efficiency, and innovation, ensuring they remain at the forefront of their industries. 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 31, 2024in Data, Enterprise, Generative AI, Google, Salesforce, Technology
guide to RAG
30Aug

Tectonic Guide to Rag Part 2

The first notable change in the field of language models is the significant expansion of context window sizes and a reduction in token costs. For instance, Anthropic’s largest model, Claude, has a context window exceeding 200,000 tokens, while recent reports indicate that Gemini’s context window can reach up to 10 million tokens. Under such circumstances, Retrieval-Augmented Generation (RAG) may no longer be necessary for many tasks, as all required data can be accommodated within the expanded context window. Several financial and analytical projects have already demonstrated that tasks can be solved without needing a vector database as intermediate storage. This trend of reducing token costs and increasing context window sizes is likely to continue, potentially decreasing the need for external mechanisms in LLMs, although they are still relevant for the time being. If the context window remains insufficient, methods for summarization and context compression have been introduced. LangChain, for example, offers a class called ConversationSummaryMemory to address this challenge. pythonCopy codellm = OpenAI(temperature=0) conversation_with_summary = ConversationChain( llm=llm, memory=ConversationSummaryMemory(llm=OpenAI()), verbose=True ) conversation_with_summary.predict(input=”Hi, what’s up?”) Knowledge Graphs As the volume of data continues to grow, navigating through it efficiently becomes increasingly critical. In certain cases, understanding the structure and attributes of data is essential for effective use. For example, if the data source is a company’s wiki, an LLM might not recognize a phone number unless the structure or metadata indicates that it’s the company’s contact information. Humans can infer meaning from conventions, such as the subdirectory “Company Information,” but standard RAG may miss such connections. This challenge can be addressed by Knowledge Graphs, also known as Knowledge Maps, which provide both raw data and metadata that illustrates how different entities are interconnected. This method is referred to as Graph Retrieval-Augmented Generation (GraphRAG). Graphs are excellent for representing and managing structured, interconnected information. Unlike vector databases, they excel at capturing complex relationships and attributes among diverse data types. Creating a Knowledge Graph The process of creating a knowledge graph typically involves collecting and structuring data, which requires expertise in both the subject matter and graph modeling. However, LLMs can automate a significant portion of this process by analyzing textual data, identifying entities, and recognizing their relationships, which can then be represented in a graph structure. In many cases, an ensemble of vector databases and knowledge graphs can improve accuracy, as discussed previously. For example, search functionality might combine keyword search through a regular database (e.g., Elasticsearch) and graph-based queries. LangChain can also assist in extracting structured data from entities, as demonstrated in this code example: pythonCopy codedocuments = parse_and_load_data_from_wiki_including_metadata() graph_store = NebulaGraphStore( space_name=”Company Wiki”, tags=[“entity”] ) storage_context = StorageContext.from_defaults(graph_store=graph_store) index = KnowledgeGraphIndex.from_documents( documents, max_triplets_per_chunk=2, space_name=space_name, tags=[“entity”] ) query_engine = index.as_query_engine() response = query_engine.query(“Tell me more about our Company”) Here, searching is conducted based on attributes and related entities, instead of similar vectors. If set up correctly, metadata from the company’s wiki, such as its phone number, would be accessible through the graph. Access Control One challenge with this system is that data access may not be uniform. For instance, in a wiki, access could depend on roles and permissions. Similar issues exist in vector databases, leading to the need for access management mechanisms such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Relationship-Based Access Control (ReBAC). These access control methods function by evaluating paths between users and resources within graphs, such as in systems like Active Directory. To ensure the integrity of data during the ingestion phase, metadata related to permissions must be preserved in both the knowledge graph and vector database. Some commercial vector databases already have this functionality built in. Ingestion and Parsing Data needs to be ingested into both graphs and vector databases, but for graphs, formatting is especially critical since it reflects the data’s structure and serves as metadata. One particular challenge is handling complex formats like PDFs, which can contain diverse elements like tables, images, and text. Extracting structured data from such formats can be difficult, and while frameworks like LLama Parse exist, they are not always foolproof. In some cases, Optical Character Recognition (OCR) may be more effective than parsing. Enhancing Answer Quality Several new approaches are emerging to improve the quality of LLM-generated answers: While these advancements in knowledge graphs, access control, and retrieval mechanisms are promising, challenges remain, particularly around data formatting and parsing. However, these methods continue to evolve, enhancing LLM capabilities and efficiency. 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 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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August 30, 2024in Data

When to use Flow

When and Why Should You Use a Flow in Salesforce? Flow is Salesforce’s premier tool for creating configurable automation and guided user experiences. If you need to build a process that doesn’t require the complexity of Apex code, Flow should be your go-to solution. It’s versatile, user-friendly, and equipped to handle a wide range of business automation needs. Legacy tools like Process Builder and Workflow Rules are being phased out, with support ending in December 2025. While you may choose to edit existing automations in these tools temporarily, migrating to Flow should be a top priority for future-proofing your Salesforce org. Capabilities of FlowFlows allow you to: When Should You Avoid Using a Flow?Although Flow is powerful, it’s not the right choice in every scenario. Here are situations where it may not be suitable: Creating a Flow in Salesforce Pro Tips for Flow Building Flow vs. Apex: Which to Choose?Flows are simpler, faster to deploy, and accessible to admins without coding expertise. Apex, on the other hand, is suited for complex use cases requiring advanced logic or integrations. Here’s when Apex should be used instead: Why Flows Are the FutureSalesforce has positioned Flow as the central automation tool by deprecating Workflow Rules and Process Builder. With every release, Flow’s capabilities expand, making it easier to replace tasks traditionally requiring Apex. For instance: Final ThoughtsSalesforce admins should prioritize building and migrating automation to Flow. It’s a scalable and admin-friendly tool that ensures your org stays up-to-date with Salesforce’s evolving ecosystem. Whether you’re automating basic processes or tackling complex workflows, Flow provides the flexibility to meet your needs. 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 28, 2024in Data, Salesforce Flow
Winter 25 Release Developer Enhancements
25Aug

Winter 25 Release Developer Enhancements

Developer Enhancements Salesforce Flow Enhancements Flow Orchestrator Enhancements How to Access and Use Key Salesforce Features View the Automation Lightning App: Manage Steps in an Orchestration Stage: Customize the Flow Orchestration Work Guide Component: Customize SMS One-Time Password Delivery for Experience Cloud Sites: Control Authenticated Callouts Permissions: Improve Data Transmission Speed and Security with TLS 1.3: Enable LWC Stacked Modals: Additional Enhancements: Salesforce Mobile App Enhancements Resolve Sync Issues with Offline Drafts: Access Hyperlinks in Enhanced Reports: Messaging in the Salesforce Mobile App: Seller-Focused Sales Mobile Experience: Stay Informed About Upcoming Releases: Check Out Resources: 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 25, 2024in Salesforce Shield
Apex Sharing Reasons
20Aug

Apex Sharing Reasons

Creating Apex Sharing Reasons Apex sharing reasons are essential for implementing Apex-managed sharing on custom objects. They define the purpose behind the sharing of records and streamline the process of managing sharing entries programmatically. By using Apex sharing reasons, you can: Why Use Apex Sharing Reasons? When Apex sharing reasons are configured, they appear in the Reason column of the sharing settings for a custom object record. This visibility helps users and administrators understand the intent behind each sharing entry. Key Considerations Steps to Create an Apex Sharing Reason Additional Information For more details about Apex-managed sharing, consult the Apex Developer Guide. This resource provides comprehensive insights into managing record sharing programmatically in Salesforce. By implementing Apex sharing reasons thoughtfully, you can enhance both the functionality and clarity of your record-sharing processes. 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 20, 2024in Data, Salesforce
Salesforce Data Snowflake and You
19Aug

Salesforce Data Snowflake and You

Unlock the Full Potential of Your Salesforce Data with Snowflake At Tectonic, we’ve dedicated years to helping businesses maximize their Salesforce investment, driving growth and enhancing customer experiences. Now, we’re expanding those capabilities by integrating with Snowflake.Imagine the power of merging Salesforce data with other sources, gaining deeper insights, and making smarter decisions—without the hassle of complex infrastructure. Snowflake brings this to life with a flexible, scalable solution for unifying your data ecosystem.In this insight, we’ll cover why Snowflake is essential for Salesforce users, how seamlessly it integrates, and why Tectonic is the ideal partner to help you leverage its full potential. Why Snowflake Matters for Salesforce Users Salesforce excels at managing customer relationships, but businesses today need data from multiple sources—e-commerce, marketing platforms, ERP systems, and more. That’s where Snowflake shines. With Snowflake, you can unify these data sources, enrich your Salesforce data, and turn it into actionable insights. Say goodbye to silos and blind spots. Snowflake is easy to set up, scales effortlessly, and integrates seamlessly with Salesforce, making it ideal for enhancing CRM data across various business functions.The Power of Snowflake for Salesforce Users Enterprise-Grade Security & GovernanceSnowflake ensures that your data is secure and compliant. With top-tier security and data governance tools, your customer data remains protected and meets regulatory requirements across platforms, seamlessly integrating with Salesforce. Cross-Cloud Data SharingSnowflake’s Snowgrid feature makes it easy for Salesforce users to share and collaborate on data across clouds. Teams across marketing, sales, and operations can access the same up-to-date information, leading to better collaboration and faster, more informed decisions. Real-Time Data ActivationCombine Snowflake’s data platform with Salesforce Data Cloud to activate insights in real-time, enabling enriched customer experiences through dynamic insights from web interactions, purchase history, and service touchpoints. Tectonic + Snowflake: Elevating Your Salesforce Experience Snowflake offers powerful data capabilities, but effective integration is key to realizing its full potential—and that’s where Tectonic excels. Our expertise in Salesforce, now combined with Snowflake, ensures that businesses can maximize their data strategies. How Tectonic Helps: Strategic Integration Planning: We assess your current data ecosystem and design a seamless integration between Salesforce and Snowflake to unify data without disrupting operations. Custom Data Solutions: From real-time dashboards to data enrichment workflows, we create solutions tailored to your business needs. Ongoing Support and Optimization: Tectonic provides continuous support, adapting your Snowflake integration to meet evolving data needs and business strategies. Real-World Applications Retail: Integrate in-store and e-commerce sales data with Salesforce for real-time customer insights. Healthcare: Unify patient data from wearables, EMRs, and support interactions for a holistic customer care experience. Financial Services: Enhance Salesforce data with third-party risk assessments, enabling quicker, more accurate underwriting. Looking Ahead: The Tectonic Advantage Snowflake opens up new possibilities for Salesforce-powered businesses. Effective integration, however, requires strategic planning and hands-on expertise. Tectonic has a long-standing track record of helping clients get the most out of Salesforce, and now, Snowflake adds an extra dimension to our toolkit. Whether you want to better manage data, unlock insights, or enhance AI initiatives, Tectonic’s combined Salesforce and Snowflake expertise ensures you’ll harness the best of both worlds. Stay tuned as we dive deeper into Snowflake’s features, such as Interoperable Storage, Elastic Compute, and Cortex AI with Arctic, and explore how Tectonic is helping businesses unlock the future of data and AI. Ready to talk about how Snowflake and Salesforce can transform your business? Contact Tectonic today! 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 19, 2024in Data, Enterprise, Salesforce, Salesforce Data Cloud, Snowflake
Salesforce Thematic Personalization
16Aug

Salesforce Thematic Personalization

Thematic Personalization Made Simple with Salesforce Leverage the power of thematic personalization to tailor your messaging and creative assets for each target audience directly within Salesforce. If you’re a Faraday user, integrating thematic personalization predictions into your CRM is a seamless way to elevate your outreach. With predictions accessible in Salesforce, you can shape your content to better resonate with your leads and contacts. This integration helps you understand what appeals to your audience, ensuring your communications are more relevant and impactful—all while working within the tools you already know. It’s an effortless way to enhance personalization and make the most of your data. Step-by-Step Integration Guide Step 1: Connect Your Data SourcesUse the link below to connect Salesforce to Faraday. Alternatively, you can skip this step and upload your data using CSV files to get started.👉 Connect to Salesforce Step 2: Ingest Data into Event StreamsStream your data into Faraday to enable the platform to interpret its meaning. Follow the link below for guidance on setting up event streams to power this template.👉 Ingest Data Step 3: Organize Your Customer DataGroup your data into cohorts—key building blocks in Faraday. These cohorts enable you to predict customer behavior with precision.👉 Define Cohorts Step 4: Declare Your Prediction ObjectivesOnce your cohorts are ready, instruct Faraday to predict the behaviors you care about. Follow the documentation using the link below.👉 Set Prediction Goals Step 5: Build and Deploy Your Personalization PipelineCreate a content personalization pipeline and deploy it to Salesforce to use predictions for shaping creative and messaging.👉 Deploy Content Personalization Step 6: Finalize Deployment to SalesforceComplete your setup by creating a deployment target within Salesforce or, if preferred, export your results as a CSV file.👉 Deploy to Salesforce Why Integrate Thematic Personalization?This integration empowers you to seamlessly incorporate predictive insights into your CRM workflow, enabling more personalized, effective communications. With minimal effort, you can connect with your audience on a deeper level, enhance engagement, and achieve better results. 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 16, 2024in Data, Salesforce
benefits of salesforce flow automation
15Aug

Benefits of Salesforce Flow Automation

Salesforce Flow Automation offers robust tools to streamline operations, enhance productivity, and improve accuracy. Whether you’re new to Salesforce or refining existing workflows, here are five top tips for maximizing the benefits of Salesforce Flow Automation. 1. Define Clear Objectives Before creating any flows, clearly define your automation goals, whether it’s reducing manual data entry, accelerating approval processes, or ensuring consistent customer follow-ups. Having specific objectives will keep your flow design focused and help you measure the impact of your automation. 2. Leverage Pre-Built Flow Templates Salesforce provides a range of pre-built flow templates tailored to common business needs, saving time and effort. Start with these templates and customize them to suit your unique requirements, allowing you to implement efficient solutions without building from scratch. 3. Optimize Decision Elements Decision elements in Salesforce Flow enable branching logic based on set conditions. Use them to direct the flow according to specific criteria, such as routing different approval paths based on deal value or service type. This targeted approach ensures each scenario is handled effectively. 4. Thoroughly Test Before Deployment Testing is a critical part of the automation process. Before launching a new flow, test it in a sandbox environment to catch any issues. Cover a range of scenarios and edge cases to confirm that the flow works as expected, helping avoid disruptions and ensuring a smooth transition into live use. 5. Monitor and Continuously Improve Automation is an evolving process. After deploying flows, monitor their performance to ensure they’re achieving desired outcomes. Use Salesforce’s reporting tools to track metrics like completion rates and processing times. With this data, you can fine-tune your flows to boost efficiency and adapt to changing business needs. By following these tips, you can unlock the full potential of Salesforce Flow Automation, leading to streamlined processes and better business outcomes. Embrace automation to reduce manual work and keep focus on driving core business growth. 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 15, 2024in Data, Salesforce, Salesforce Implementation Services, Salesforce Optimization

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