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Salesforce Hyperforce Summer 24 Release Notes
26Apr

Salesforce Hyperforce Summer 24 Release Notes

Hyperforce is the next-generation Salesforce infrastructure architecture built for the public cloud. Salesforce Hyperforce Summer 24 Release Notes. It provides Salesforce applications with compliance, security, privacy, agility and scalability and gives customers more choice over data residency. Salesforce Hyperforce Summer 24 Release Notes Hyperforce is Salesforce’s renewed infrastructure architecture, based on the consumption of public cloud services. It has been designed to offer customers a more powerful and easily scalable platform. In this new scenario, Salesforce does not manage physical resources. What is the difference between Hyperforce and Lightning? The Lightning Platform is the core infrastructure in Salesforce whereas, Hyperforce is a new infrastructure model provided by the CRM platform. 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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April 26, 2024in Hyperforce, Salesforce, Salesforce Sales Cloud
Harnessing Zero Copy Integration
21Apr

Harnessing Zero Copy Integration

Streamlining Data Management: Harnessing Zero Copy Integration for Efficient Customer Insights Zero copy integration offers a revolutionary approach to accessing and utilizing customer data without the need for burdensome data transfers. By seamlessly linking disparate databases, this innovative technique eliminates the complexities associated with moving, copying, or reformatting data. Let’s explore how zero copy integration can optimize data management processes and enhance the efficiency of customer data platforms (CDPs) alongside traditional data warehouses. Understanding Zero Copy Integration: Zero copy integration, also known as zero ETL (extract-transform-load), facilitates the sharing of data across multiple databases without physically relocating it. This method enables real-time access to data, significantly reducing costs and mitigating the risks of errors inherent in data movement processes. Unlike traditional data replication methods, zero copy integration ensures that data remains in its original location, eliminating the need for redundant storage and synchronization efforts. Good technology standards can actually bring order to the chaos and allow products from different companies to work together, fostering healthy competition and enhancing productivity. That is precisely what Zero-Copy Integration does. Zero-Copy Integration is a framework that eliminates the need for data copies and integration, making it easier to work with applications, analytics, artificial intelligence, and machine learning. Canada’s Digital Governance Council recently announced that Zero-Copy Integration has been published for open access by the public, setting a new standard for data collaboration. Moreover, Zero-Copy Integration prioritizes data-centricity and active metadata over complex code and solution modularity over monolithic design. It also facilitates data governance through data products and federated stewardship, eliminating the need for centralized teams. In addition to boosting the development and deployment of new technologies, Zero-Copy Integration also addresses the growing number of data privacy regulations. By putting data at the center of the process, Zero-Copy Integration ensures greater collaboration and control, positively affecting industries like healthcare, research, banking, and public services. Benefits of Zero-Data Integration Proponents of zero-copy integration and dataware say the framework will lower data storage costs, improve performance of IT teams, improve privacy and security of data, and drive innovation in systems for public health, social research, open banking and sustainability through innovations in: Comparing Traditional vs. Zero Copy Integration: Aspect Traditional Zero Copy Replication Requires copying data to target Data remains in original location Updates Data accuracy dependent on synchronization Real-time access to data Cost Involves data movement expenses No additional data movement costs Regulatory Compliance Complex governance due to data duplication Simplified compliance with source data only Errors Data movement introduces potential errors Minimized risk of errors with no data movement Maintenance Increases complexity with copying and synchronization Streamlined management with no data relocation Implementing Zero Copy Integration: The implementation of zero copy integration varies depending on the platform and direction of data access. Let’s explore how it works in scenarios involving Salesforce Data Cloud and Snowflake data warehouse: Case Study: Buyers Edge’s Zero Copy Success Story: Buyers Edge, a procurement optimization company, leveraged zero copy integration to unify customer profiles in a CDP while accessing purchase data from their data warehouse. By seamlessly integrating Salesforce Data Cloud with their warehouse, Buyers Edge enhanced predictive modeling capabilities, resulting in tailored sales and marketing strategies. Zero copy integration revolutionizes data management practices by enabling seamless data access and eliminating the need for data duplication or relocation. As businesses navigate the evolving data landscape, leveraging zero copy integration can streamline operations, enhance insights, and empower organizations to unlock the full potential of their data assets. MILESTONES / TIMELINE Second half 2023 – Internationalization February 21, 2023 – Information Session (watch above ☝ or via YouTube) February 15, 2023 – ‘Zero-copy’ applications published by DCA February 08, 2023 – Published by Digital Governance Council (Press release) December 15, 2022 – Approved by Standards Council of Canada June-Nov 2022 – Public consultation period 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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April 21, 2024in Salesforce, Salesforce Data Cloud, Salesforce Einstein, Snowflake
Einstein Prediction Builder
19Apr

Einstein Prediction Builder

Einstein Prediction Builder, a sophisticated yet user-friendly tool from Salesforce Einstein, empowers users to generate predictions effortlessly, without requiring machine learning expertise or coding skills. This capability enables businesses to augment their operations with foresight-driven insights. As of the Spring ’20 release, all Enterprise Edition and above orgs can build one free prediction with Einstein Prediction Builder. Consider the potential business outcomes unlocked by leveraging Einstein Prediction Builder. Let’s delve into a hypothetical scenario: Meet Mr. Claus, the owner of ‘North Claus,’ a business that began as a modest family venture but gradually expanded its footprint. As ‘North Claus’ burgeoned across 10 countries, Mr. Claus recognized the need for Business Intelligence (BI) to navigate market dynamics effectively. BI entails gathering insights to forecast and comprehend market shifts—an imperative echoed by Jack Ma’s famous adage, “Adopt and change before any major trends and changes.” Intrigued by the prospect of BI, especially amidst the disruptive backdrop of Covid-19, Mr. Claus embarked on a journey to implement it in his company. The Formation of Business Intelligence: In today’s digital landscape, businesses amass vast amounts of data from diverse sources such as sales, customer interactions, and website traffic. This data serves as the bedrock for deriving actionable insights, enabling organizations to formulate forward-looking strategies. However, developing robust BI capabilities poses several challenges: Mr. Claus grappled with these challenges as he endeavored to develop BI independently. Recognizing the complexity involved, he turned to Salesforce, particularly intrigued by Einstein Prediction Builder. Einstein Prediction Builder Trailhead Understanding Einstein Prediction Builder: Einstein Prediction Builder, available in various Salesforce editions, leverages checkbox and formula fields to generate predictions. Before utilizing Prediction Builder, certain prerequisites must be met: Creating Einstein Predictions: To initiate the creation of Einstein Predictions, users navigate to Setup and access the Einstein Prediction Builder. The guided Setup simplifies the process, guiding users through relevant data inputs at each step. Once configured, predictions can be enabled, disabled, or cloned as needed. Key Features and Applications: Einstein Predictions integrate seamlessly with Salesforce Lightning, providing predictive insights directly on record pages. These predictions offer invaluable guidance on various aspects, such as sales opportunities and payment delays. Additionally, Prediction Builder facilitates packaging of predictions for seamless deployment across orgs and supports integration with external platforms like Tableau. Prediction Builder equips businesses with the intelligence needed to anticipate market trends, optimize workflows, and enhance customer interactions. As Mr. Claus discovered, embracing predictive analytics can revolutionize decision-making and drive sustainable growth. Like1 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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April 19, 2024in Einstein Insights, Salesforce, Salesforce Einstein
Who Calls AI Ethical
18Apr

Who Calls AI Ethical

Background – Who Calls AI Ethical On March 13, 2024, the European Union (EU) enacted the EU AI Act, a move that some argue has hindered its position in the global AI race. This legislation aims to ‘unify’ the development and implementation of AI within the EU, but it is seen as more restrictive than progressive. Rather than fostering innovation, the act focuses on governance, which may not be sufficient for maintaining a competitive edge. The EU AI Act embodies the EU’s stance on Ethical AI, a concept that has been met with skepticism. Critics argue that Ethical AI is often misinterpreted and, at worst, a monetizable construct. In contrast, Responsible AI, which emphasizes ensuring products perform as intended without causing harm, is seen as a more practical approach. This involves methodologies such as red-teaming and penetration testing to stress-test products. This critique of Ethical AI forms the basis of this insight,and Eric Sandosham article here. The EU AI Act To understand the implications of the EU AI Act, it is essential to summarize its key components and address the broader issues with the concept of Ethical AI. The EU defines AI as “a machine-based system designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment. It infers from the input it receives to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.” Based on this definition, the EU AI Act can be summarized into several key points: Fear of AI The EU AI Act appears to be driven by concerns about AI being weaponized or becoming uncontrollable. Questions arise about whether the act aims to prevent job disruptions or protect against potential risks. However, AI is essentially automating and enhancing tasks that humans already perform, such as social scoring, predictive policing, and background checks. AI’s implementation is more consistent, reliable, and faster than human efforts. Existing regulations already cover vehicular safety, healthcare safety, and infrastructure safety, raising the question of why AI-specific regulations are necessary. AI solutions automate decision-making, but the parameters and outcomes are still human-designed. The fear of AI becoming uncontrollable lacks evidence, and the path to artificial general intelligence (AGI) remains distant. Ethical AI as a Red Herring In AI research and development, the terms Ethical AI and Responsible AI are often used interchangeably, but they are distinct. Ethics involve systematized rules of right and wrong, often with legal implications. Morality is informed by cultural and religious beliefs, while responsibility is about accountability and obligation. These constructs are continuously evolving, and so must the ethics and rights related to technology and AI. Promoting AI development and broad adoption can naturally improve governance through market forces, transparency, and competition. Profit-driven organizations are incentivized to enhance AI’s positive utility. The focus should be on defining responsible use of AI, especially for non-profit and government agencies. Towards Responsible AI Responsible AI emphasizes accountability and obligation. It involves defining safeguards against misuse rather than prohibiting use cases out of fear. This aligns with responsible product development, where existing legal frameworks ensure products work as intended and minimize misuse risks. AI can improve processes such as recruitment by reducing errors compared to human solutions. AI’s role is to make distinctions based on data attributes, striving for accuracy. The concern is erroneous discrimination, which can be mitigated through rigorous testing for bias as part of product quality assurance. Conclusion The EU AI Act is unlikely to become a global standard. It may slow AI research, development, and implementation within the EU, hindering AI adoption in the region and causing long-term harm. Humanity has an obligation to push the boundaries of AI innovation. As a species facing eventual extinction from various potential threats, AI could represent a means of survival and advancement beyond our biological limitations. 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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April 18, 2024in Salesforce
How AI is Raising the Stakes in Phishing Attacks
13Apr

How AI is Raising the Stakes in Phishing Attacks

Cybercriminals are increasingly using advanced AI, including tools like ChatGPT, to execute highly convincing phishing campaigns that mimic legitimate communications with uncanny accuracy. As AI-powered phishing becomes more sophisticated, cybersecurity practitioners must adopt AI and machine learning defenses to stay ahead. What are AI-Powered Phishing Attacks? Phishing, a long-standing cybersecurity issue, has evolved from crude scams into refined attacks that can mimic trusted entities like Amazon, postal services, or colleagues. Leveraging social engineering, these scams trick people into clicking malicious links, downloading harmful files, or sharing sensitive information. However, AI is elevating this threat by making phishing attacks more convincing, timely, and challenging to detect. General Phishing Attacks Traditionally, phishing emails were often easy to spot due to grammatical errors or poor formatting. AI, however, eliminates these mistakes, creating messages that appear professionally written. Additionally, AI language models can gather real-time data from news and corporate sites, embedding relevant details that create urgency and heighten the attack’s credibility. AI chatbots can also generate business email compromise attacks or whaling campaigns at a massive scale, boosting both the volume and sophistication of these threats. Spear Phishing Spear phishing involves targeting specific individuals with highly customized messages based on data gathered from social media or data breaches. AI has supercharged this tactic, enabling attackers to craft convincing, personalized emails almost instantly. During a cybersecurity study, AI-generated phishing emails outperformed human-crafted ones in terms of convincing recipients to click on malicious links. With the help of large language models (LLMs), attackers can create hyper-personalized emails and even deepfake phone calls and videos. Vishing and Deepfakes Vishing, or voice phishing, is another tactic on the rise. Traditionally, attackers would impersonate someone like a company executive or trusted colleague over the phone. With AI, they can now create deepfake audio to mimic a specific person’s voice, making it even harder for victims to discern authenticity. For example, an employee may receive a voice message that sounds exactly like their CFO, urgently requesting a bank transfer. How to Defend Against AI-Driven Phishing Attacks As AI-driven phishing becomes more prevalent, organizations should adopt the following defense strategies: How AI Improves Phishing Defense AI can also bolster phishing defenses by analyzing threat patterns, personalizing training, and monitoring for suspicious activity. GenAI, for instance, can tailor training to individual users’ weaknesses, offer timely phishing simulations, and assess each person’s learning needs to enhance cybersecurity awareness. AI can also predict potential phishing trends based on data such as attack frequency across industries, geographical locations, and types of targets. These insights allow security teams to anticipate attacks and proactively adapt defenses. Preparing for AI-Enhanced Phishing Threats Businesses should evaluate their risk level and implement corresponding safeguards: AI, and particularly LLMs, are transforming phishing attacks, making them more dangerous and harder to detect. As digital footprints grow and personalized data becomes more accessible, phishing attacks will continue to evolve, including falsified voice and video messages that can trick even the most vigilant employees. By proactively integrating AI defenses, organizations can better protect against these advanced phishing threats. 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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April 13, 2024in Data, Technology
What is Salesforce Einstein 1
09Apr

What is Salesforce Einstein 1?

The bold new future of enterprise AI requires a new type of platform. What is Salesforce Einstein 1? One platform that can handle terabytes of disconnected data, gives you the freedom to choose your AI models, and connects directly into the flow of work, all while maintaining customer trust. The Einstein 1 Platform unifies your data, AI, CRM, development, and security into a single, comprehensive platform. It empowers IT, admins, and developers with an extensible AI platform, facilitating fast development of generative apps and automation. What is Salesforce Einstein 1 Einstein 1 has a mixture of artificial intelligence tools on the platform, and it kind of mirrors the way the core Salesforce platform is built, standardized and custom. We have out of the box AI features such as sales email generation in Sales Cloud, and service replies in Service Cloud. What is Einstein One? Einstein One seamlessly integrates our robust data platform with Data Cloud, offering a comprehensive AI and data-driven solution within our CRM service. As the leading comprehensive AI for CRM, Salesforce Einstein transforms the Customer Success Platform by incorporating a suite of integrated AI technologies, making it more intelligent and accessible to innovators worldwide. Introducing the Einstein One Platform The future of enterprise AI demands a groundbreaking platform capable of managing terabytes of diverse data, providing the flexibility to select AI models, and seamlessly integrating into daily workflows while maintaining customer trust. The Einstein One Platform consolidates data, AI, CRM, development, and security into a singular, comprehensive platform. It empowers IT, administrators, and developers with an extensible AI platform, facilitating rapid development of generative apps and automation. Salesforce has made significant investments in enhancing the Einstein One platform by introducing two prompt engineering features – a testing center and prompt engineering suggestions, demonstrating the company’s commitment to advancing AI capabilities. Data-driven and AI-driven customer experiences are vital in constructing Data Cloud. Salesforce’s predictive AI, Einstein, has been operational since 2016, conducting over a trillion predictions per week. The company continuously evolves its capabilities, introducing generative AI products like Einstein GPT, discussed at Dreamforce [2023]. ‘Customer 360′ now represents Salesforce customers’ ability to transform into customer-centric entities, delivering integrated and cohesive experiences across sales, marketing, service, and commerce. Leveraging Data Cloud, structured and unstructured engagement data comes alive, offering a personalized perspective of customer interactions. Einstein is accessible through various Salesforce products, including Sales Cloud, Service Cloud, Marketing Cloud, Salesforce Platform, Analytics Cloud, and Community Cloud. What is the Einstein One platform? Salesforce’s Einstein One is a transformative platform that securely integrates data, connects multiple Salesforce products, and empowers customer-centric businesses to create AI-driven apps, revolutionizing CRM interactions. What applications are connected with the Einstein One platform? Einstein Trust Layer: Ensuring the security of generative AI applications within Salesforce, this application acts as a safeguard for data, preventing unauthorized access. Data Cloud: Unifying structured and unstructured data, Data Cloud completes the customer 360 profile, integrating seamlessly with the Einstein One platform and making data actionable. Einstein Copilot: A built-in conversational assistant, Einstein Copilot enables users to ask questions in Natural Language and receive intelligent answers, actions, and more. Copilot Studio: An essential component of Einstein One, Copilot Studio manages Einstein One and Einstein Copilot, allowing businesses to control user access and use generative AI in their workspace. Prompt Builder, Skill Builder, Model Builder: Tools within Copilot Studio, these components empower businesses to create trusted AI prompts, add skills to user access, and choose AI models. What is Data Cloud for Einstein One? Data Cloud, combined with the Einstein Trust Layer, provides secure access to a unified data repository, forming a reliable resource for complete customer profiles and supporting AI assistants. Data Cloud facilitates valuable visualizations through reports, dashboards, and tools like Tableau, offering insights to enhance understanding of customers and business operations. What will Einstein One do for businesses? Einstein One aims to: Increase productivity Reduce operational costs Enhance the delivery of exceptional customer experiences The debut of Einstein One marks a significant stride for Salesforce and businesses, offering a secure path to boost productivity through AI technologies, ensuring continuous access to cutting-edge technologies for businesses. Where can generative AI impact businesses today? Generative AI has the potential to impact businesses by enhancing repetitive processes, making them faster. Exploring text-based processes, such as repetitive emails or form filling, is a great starting point for businesses looking to enhance capabilities. What can generative AI do for sales, service, field service teams? Generative AI can positively impact teams by automating processes, offering intelligent insights, and improving overall efficiency. Specific use cases for generative AI in sales, service, and field service teams can be explored through Salesforce’s 101 video series. What does Einstein One Tableau do? Einstein One Data Cloud, formerly known as Genie, serves as a metadata management tool connecting data from various sources. It makes data usable for AI and analysis, integrating with Einstein One, Tableau, and Salesforce’s CRM applications. Is Salesforce Einstein the same as Tableau? Formerly known as Einstein Analytics, Tableau CRM is Salesforce’s premium analytic platform, offering a powerful way to explore data, especially for current Salesforce users. Salesforce has renamed Einstein Analytics to Tableau CRM, reflecting its commitment to empowering users to make better decisions faster. In essence, the Einstein One platform combines Salesforce Data Cloud, Einstein Copilot, and the Einstein Trust Layer, providing a comprehensive solution for secure and connected data, facilitating generative AI on a comprehensive platform. Salesforce’s Einstein One Platform & the Data-Driven Revolution This innovation has the potential to revolutionize the way businesses interact with their customers. In this insight, we delve deeper into this transformative development, offering valuable insights, considerations for implementation, and the indispensable expertise of Tectonic as a data-oriented technology partner. Salesforce Data Cloud and Einstein AI are now seamlessly integrated into the Einstein One Platform. This integration empowers companies to effortlessly connect their diverse data sources, enabling the creation of AI-driven applications with minimal coding effort. Moreover, it forges a cohesive view of your data across

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April 9, 2024in Artificial Intelligence, Data, Einstein 1 Platform, Einstein Insights, Enterprise, Generative AI, Google, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein, Salesforce Sales Cloud, Technology
LLM Knowledge Test
07Apr

LLM Knowledge Test

Large Language Models. How much do you know about them? Take the LLM Knowledge Test to find out. Question 1Do you need to have a vector store for all your text-based LLM use cases? A. Yes B. No Correct Answer: B ExplanationA vector store is used to store the vector representation of a word or sentence. These vector representations capture the semantic meaning of the words or sentences and are used in various NLP tasks. However, not all text-based LLM use cases require a vector store. Some tasks, such as summarization, sentiment analysis, and translation, do not need context augmentation. Here is why: Question 2Which technique helps mitigate bias in prompt-based learning? A. Fine-tuning B. Data augmentation C. Prompt calibration D. Gradient clipping Correct Answer: C ExplanationPrompt calibration involves adjusting prompts to minimize bias in the generated outputs. Fine-tuning modifies the model itself, while data augmentation expands the training data. Gradient clipping prevents exploding gradients during training. Question 3Which of the following is NOT a technique specifically used for aligning Large Language Models (LLMs) with human values and preferences? A. RLHF B. Direct Preference Optimization C. Data Augmentation Correct Answer: C ExplanationData Augmentation is a general machine learning technique that involves expanding the training data with variations or modifications of existing data. While it can indirectly impact LLM alignment by influencing the model’s learning patterns, it’s not specifically designed for human value alignment. Incorrect Options: A) Reinforcement Learning from Human Feedback (RLHF) is a technique where human feedback is used to refine the LLM’s reward function, guiding it towards generating outputs that align with human preferences. B) Direct Preference Optimization (DPO) is another technique that directly compares different LLM outputs based on human preferences to guide the learning process. Question 4In Reinforcement Learning from Human Feedback (RLHF), what describes “reward hacking”? A. Optimizes for desired behavior B. Exploits reward function Correct Answer: B ExplanationReward hacking refers to a situation in RLHF where the agent discovers unintended loopholes or biases in the reward function to achieve high rewards without actually following the desired behavior. The agent essentially “games the system” to maximize its reward metric. Why Option A is Incorrect:While optimizing for the desired behavior is the intended outcome of RLHF, it doesn’t represent reward hacking. Option A describes a successful training process. In reward hacking, the agent deviates from the desired behavior and finds an unintended way to maximize the reward. Question 5Fine-tuning GenAI model for a task (e.g., Creative writing), which factor significantly impacts the model’s ability to adapt to the target task? A. Size of fine-tuning dataset B. Pre-trained model architecture Correct Answer: B ExplanationThe architecture of the pre-trained model acts as the foundation for fine-tuning. A complex and versatile architecture like those used in large models (e.g., GPT-3) allows for greater adaptation to diverse tasks. The size of the fine-tuning dataset plays a role, but it’s secondary. A well-architected pre-trained model can learn from a relatively small dataset and generalize effectively to the target task. Why A is Incorrect:While the size of the fine-tuning dataset can enhance performance, it’s not the most crucial factor. Even a massive dataset cannot compensate for limitations in the pre-trained model’s architecture. A well-designed pre-trained model can extract relevant patterns from a smaller dataset and outperform a less sophisticated model with a larger dataset. Question 6What does the self-attention mechanism in transformer architecture allow the model to do? A. Weigh word importance B. Predict next word C. Automatic summarization Correct Answer: A ExplanationThe self-attention mechanism in transformers acts as a spotlight, illuminating the relative importance of words within a sentence. In essence, self-attention allows transformers to dynamically adjust the focus based on the current word being processed. Words with higher similarity scores contribute more significantly, leading to a richer understanding of word importance and sentence structure. This empowers transformers for various NLP tasks that heavily rely on context-aware analysis. Incorrect Options: Question 7What is one advantage of using subword algorithms like BPE or WordPiece in Large Language Models (LLMs)? A. Limit vocabulary size B. Reduce amount of training data C. Make computationally efficient Correct Answer: A ExplanationLLMs deal with massive amounts of text, leading to a very large vocabulary if you consider every single word. Subword algorithms like Byte Pair Encoding (BPE) and WordPiece break down words into smaller meaningful units (subwords) which are then used as the vocabulary. This significantly reduces the vocabulary size while still capturing the meaning of most words, making the model more efficient to train and use. Incorrect Answer Explanations: Question 8Compared to Softmax, how does Adaptive Softmax speed up large language models? A. Sparse word reps B. Zipf’s law exploit C. Pre-trained embedding Correct Answer: B ExplanationStandard Softmax struggles with vast vocabularies, requiring expensive calculations for every word. Imagine a large language model predicting the next word in a sentence. Softmax multiplies massive matrices for each word in the vocabulary, leading to billions of operations! Adaptive Softmax leverages Zipf’s law (common words are frequent, rare words are infrequent) to group words by frequency. Frequent words get precise calculations in smaller groups, while rare words are grouped together for more efficient computations. This significantly reduces the cost of training large language models. Incorrect Answer Explanations: Question 9Which configuration parameter for inference can be adjusted to either increase or decrease randomness within the model output layer? A. Max new tokens B. Top-k sampling C. Temperature Correct Answer: C ExplanationDuring text generation, large language models (LLMs) rely on a softmax layer to assign probabilities to potential next words. Temperature acts as a key parameter influencing the randomness of these probability distributions. Why other options are incorrect: Question 10What transformer model uses masking & bi-directional context for masked token prediction? A. Autoencoder B. Autoregressive C. Sequence-to-sequence Correct Answer: A ExplanationAutoencoder models are pre-trained using masked language modeling. They use randomly masked tokens in the input sequence, and the pretraining objective is to predict the masked tokens to reconstruct the original sentence. Question 11What technique allows you to scale model

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April 7, 2024in Generative AI
Build a Culture of Data
04Apr

Build a Culture of Data

What is a Data Culture? A Data Culture is the collective behaviors and beliefs of people who value, practice, and encourage the use of data to improve decision-making. As a result, data is woven into the operations, mindset, and identity of an organization. Why is a data culture important?  It enables more informed decision-making. With a data culture in place, decisions at all levels of the organization are based on data-driven insights rather than intuition or guesswork. This leads to more effective strategies and better outcomes. What is the difference in data culture and data strategy? Gartner defines data strategy as “a highly dynamic process employed to support the acquisition, organization, analysis, and delivery of data in support of business objectives.” In contrast, the culture around data comes together with data talent, data literacy, and data tools. Build a Culture of Data Building a data culture is crucial for companies to unlock valuable insights and make smarter, more strategic decisions. Here’s what leaders need to know to foster a data-driven environment: By following these steps and prioritizing the development of a data culture, leaders can empower their organizations to make informed decisions, drive growth, and stay ahead of the competition in today’s data-driven world. Data Maturity Understanding data maturity is crucial for organizations as it provides a framework for assessing their current state of data management and analytics capabilities. It serves as a tool to guide decision-making and prioritize initiatives aimed at advancing the organization’s data capabilities. By evaluating data maturity, organizations can identify gaps, set goals, and determine the necessary steps to progress along their data journey. Data maturity assessment typically involves evaluating various aspects of data management, including data governance, data quality, data infrastructure, analytics capabilities, and organizational culture around data. Based on the assessment, organizations can identify areas of strength and weakness and develop a roadmap for improvement. Furthermore, understanding data maturity enables organizations to track their progress over time. By periodically reassessing data maturity, organizations can measure how much they have advanced and identify areas that still require attention. This iterative process allows organizations to continuously improve their data capabilities and adapt to evolving business needs and technological advancements. In summary, understanding data maturity allows organizations to: Like1 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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April 4, 2024in Einstein 1 Platform, Salesforce
Legacy Salesforce URL Redirection Retires
31Mar

Legacy Salesforce URL Redirection Retires

Legacy Salesforce URL Redirection Retires – Salesforce Announcement Security Notification At Salesforce, we understand that the confidentiality, integrity, and availability of your data is vital to your business, and we take the protection of your data very seriously. We’re writing to remind you that redirections for some of your Salesforce orgs’ URLs stop in the Winter ’25 release, starting in August 2024 for sandboxes and in September 2024 for production orgs. We highly encourage you to test for this change and update all references to your previous URLs before those redirections stop. Security Notification Why the Change? To comply with the latest browser and security standards, Salesforce recently required that customers deploy enhanced domains. This feature updated the hostname formats for the domains that serve Salesforce orgs. To reduce the impact of this change, your previous hostnames are redirected by default. However, to meet Salesforce security standards, some of the previous domains will be retired in 2024. The corresponding redirections stop in the Winter ’25 release. Why the Change? Actions Required Review the affected hostnames and how you can disable the redirections for testing. For details, see Prepare for the End of Redirections for Non-Enhanced Domains in Salesforce Help. Additional Resources Salesforce Enhanced Domains FAQSalesforce Help: Prepare for the End of Redirections for Non-Enhanced DomainsSalesforce Help: Log My Domain Hostname RedirectionsSalesforce Help: Enhanced Domains TimelineTo ask questions about enhanced domains, visit our monitored My Domain and Enhanced Domains group in the Trailblazer Community.We appreciate your trust in us as we continue to make your success and security our top priorities. Additional Resources Legacy Salesforce URL Redirection Retires Prepare for the End of Redirections for Non-Enhanced Domains After enhanced domains are deployed, your previous non-enhanced hostnames are redirected. Those redirections stop in Winter ’25, starting in August 2024 for sandboxes.. Review the affected hostnames and how you can disable the redirections to test before Salesforce disables them. REQUIRED EDITIONS Available in: both Salesforce Classic (not available in all orgs) and Lightning Experience Available in: Group, Essentials, Professional, Enterprise, Performance, Unlimited, and Developer Editions After enhanced domains are deployed, Salesforce redirects two categories of non-enhanced hostnames. To see if redirections are in place for a previous My Domain, check the Redirections section of the My Domain Setup page. NOTE My Domain URL redirections help prevent disruption, but they’re not intended as a permanent solution. Not all services work well with redirections, and a redirection adds a step to the process of loading the final web page. When you deploy enhanced domains, we highly recommend that you disable redirections during testing and update all references to your old URLs. Track the Source of Redirections You can search the metadata for your org to determine where the impacted URLs are used in Salesforce, but discovering all the other places that your URLs are used can be more complex. For example, site URLs and content URLs can be used on your website, social media pages, marketing materials, and templates such as email signatures and automated responses. To help you identify these locations outside of Salesforce, the Hostname Redirects log includes the referrer and origin sent with each request that Salesforce redirects. For more information, see Log My Domain Hostname Redirections. Notify Users During Redirections To help your users update outdated links and bookmarks, display a brief message during the redirection that provides the current URL. See Manage My Domain Redirections. Previous *.force.com Site Hostnames When you enable and deploy enhanced domains, the hostname formats change for your Experience Cloud sites and Salesforce Sites. To minimize potential disruption, the *.force.com hostnames that Salesforce hosted for your sites are redirected to your current site hostnames. Those redirections remain in place until Winter ’25 or until you disable these redirections. To disable these redirections for testing purposes, use an option in the Redirections section of the My Domain page and test. For more information, see Disable Redirections for Your Previous Force.com Site URLs. For the replacement format for each of these hostnames, see My Domain URL Format Changes When You Enable Enhanced Domains. To better understand the purpose of each hostname type and whether it applies to you, see My Domain Hostnames. Here are the *.force.com site hostnames for a production org that are temporarily redirected after you enable and deploy enhanced domains. Here are the .*force.com site hostnames for a sandbox that are temporarily redirected after you enable and deploy enhanced domains. Other Non-Enhanced My Domain Hostnames When you enable and deploy enhanced domains, your other previous non-enhanced My Domain hostnames are redirected. Those redirections remain in place until Winter ’25 or until you disable these redirections. For the type and the replacement format for each of these hostnames, see My Domain URL Format Changes When You Enable Enhanced Domains. To better understand the purpose of each hostname type and whether it applies to you, see My Domain Hostnames. NOTE If you deploy another My Domain change after you enable and deploy enhanced domains, these hostnames are no longer redirected. For more information, see Understand Redirections for Previous My Domain Hostnames. To disable the redirections of these non-enhanced My Domain hostnames for testing purposes, you can temporarily disable your previous My Domain. For more information, see Disable or Remove Your Previous My Domain. Here are the non-enhanced hostnames for a production org that are temporarily redirected after you enable and deploy enhanced domains. Here are the non-enhanced hostnames for a sandbox that are temporarily redirected after you enable and deploy enhanced domains. 1 If your installed package is unmanaged, the package name is c. Instanced URLs Without My Domain If your org was created before October 2020, you didn’t get a My Domain by default. In that case, your users accessed Salesforce with these hostnames that contained your instance name but not your My Domain name. These hostnames are redirected if the Instanced URL setting in the Redirections section on the My Domain Setup page is set to Redirect to the same page within the domain

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March 31, 2024in Hyperforce, Salesforce, Salesforce Experience Cloud
Marketing Cloud Engagement
18Mar

Salesforce Distributed Marketing Content

Salesforce Distributed Marketing Content empowers you to extend dynamic content to dispersed teams, fostering safe and efficient interaction with that content. Integrate one or more Distributed Marketing Content Blocks seamlessly with standard or custom content areas to facilitate collaborative moments within branded email messages. For instance, enable users to personalize holiday cards with personal notes and images or provide context to market update messages. Leverage Distributed Marketing alongside AMPscript to enable users to craft customized SMS messages. Salesforce Distributed Marketing Content While marketing teams retain control over message structure, ensuring coherence, brand alignment, and compliance, collaborative content augments this framework, granting distributed teams flexibility within set parameters – a concept Salesforce refers to as “flexibility within a framework.” The usage of Distributed Marketing content is flexible and can adapt over time. Since each message is independently configurable, you can initiate with existing assets and introduce collaborative elements as needed. Please note that Distributed Marketing employs JavaScript ES6 for message personalization, requiring the disabling of Prevent Cross-Site Tracking in Safari and third-party cookies in Chrome. Enable Email Personalization with Distributed Marketing Content Blocks Utilize Distributed Marketing Content Blocks within Marketing Cloud to create personalized sections of content for Distributed Marketing users. Enable Custom SMS Messages Incorporate AMPscript into SMS messages to empower users to compose and dispatch custom SMS messages through Distributed Marketing. Personalization Data Extension Distributed Marketing establishes personalization data extensions in Marketing Cloud to store user-entered personalization data for email messages. A personalization data extension is automatically generated when connecting a journey to a campaign or enabling a journey for quick send. Custom SMS messages are not stored here but are accessible in the journey’s event data extension. Legacy Personalization While Legacy Personalization options like Introduction, Conclusion, and Greeting are available, their usage will be supported until End of Support is announced. Like1 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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March 18, 2024in Data, Distributed Marketing, Salesforce
UX Design Trends 2024
08Mar

UX Design Trends 2024

Navigating Design Trends: AI, Discovery, Accessibility, and Collaboration. Salesforce UX Design Trends 2024. As we reflect on the past year and look ahead, design trends are emerging, signaling a pivotal moment in the intersection of creativity, usability, and AI. For developers, admins, architects, and business leaders, understanding these trends is crucial in shaping the future. Here are the four design trends steering this transformative journey: As we move forward, these design trends signify a paradigm shift, emphasizing the significance of AI, streamlined discovery, accessibility, and the growing collaboration between designers and developers. Navigating this transformative landscape requires an adaptable mindset and a commitment to ethical, inclusive design practices from the outset. When you work with Tectonic we take all these considerations to mind as we design or re-design your Salesforce org. Contact Tectonic today. UX Design Trends 2024 Like2 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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March 8, 2024in Salesforce
demand generation web use cases for personalization
03Mar

Demand Generation Web Use Cases for Personalization

Utilize effective personalization techniques adopted by businesses in online campaigns to stimulate demand generation. The term “demand generation” has somewhat faded from the marketing lexicon due to the emphasis on analytics, AI, and metrics for lead conversion. However, where does personalization fit into the broader scope of demand generation? Demand generation web use cases for personalization. Personalization plays a pivotal role in various aspects of demand generation: In lead nurturing, personalization is equally vital: Moreover, personalization is instrumental in lead acquisition efforts by delivering relevant experiences to all of your prospects. To effectively implement personalization, real-time insights into individual behaviors and interactions are essential. A comprehensive personalization solution should unify data from various channels and systems, enabling seamless cross-channel personalization. This includes “stitching” together anonymous and known user profiles, integrating data with complementary systems like CRMs and marketing platforms, and facilitating real-time omni-channel personalization. The key to successful personalization lies in understanding and addressing each individual’s unique needs and preferences. By adopting a customer-centric approach and setting clear objectives aligned with business goals, organizations can leverage personalization to enhance customer experiences, boost conversion rates, and drive measurable business growth. To execute a successful personalization strategy, organizations must: By following these steps and continuously optimizing personalization efforts, organizations can build stronger customer relationships, drive business growth, and maximize marketing ROI. Website personalization serves as the starting point for many companies embarking on their personalization journey. This entails ensuring that returning visitors encounter pages tailored to their previous experiences or recent purchases. It can also involve presenting new customers with product recommendations based on their current browsing session. The return on this initial investment can be substantial, with many companies witnessing a significant increase in conversion rates, sometimes by as much as 50% or more. For instance, a site converting 2% of visitors might see that figure rise to 3%, a dream scenario for digital marketers. Moreover, this boost in conversion rates can have far-reaching effects across marketing programs, leading to a reduction in overall customer acquisition costs. Tectonic now offers Personalization Implementation Solutions. The next stage in personalization maturity involves integrating a customer’s web and email experiences. This seamless connection between two major channels for customer engagement brings organizations closer to achieving an omni-channel personalization experience. Timely and relevant follow-up messages after a customer’s website visit or purchase can deepen relationships and enhance lifetime value without significant additional marketing expenditure. Finally, the ultimate goal is to extend personalization across all channels, ensuring consistent and tailored experiences wherever customers interact with your brand. However, achieving this can be challenging due to fragmented customer data across multiple channels, teams, and systems. An effective personalization solution should consolidate and synthesize this cross-channel information by maintaining unified customer profiles and enabling real-time omni-channel personalization. Testing is a crucial aspect of successful personalization efforts, allowing organizations to optimize campaigns and maximize engagement, conversions, and revenue. A robust personalization solution should facilitate A/B testing, measuring lift over control, evaluating impacts against specific goals, and filtering results by segment. Effective website personalization lays the foundation for broader personalization efforts across channels. By seamlessly integrating web and email experiences and extending personalization to all touchpoints, organizations can deliver tailored experiences that drive engagement, loyalty, and ultimately, business growth. By Tectonic’s Salesforce Marketing Platform Architect Shannan Hearne Like1 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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March 3, 2024in Data, Einstein Insights, Generative AI, Salesforce, Salesforce Einstein, Salesforce Marketing Cloud, Salesforce Optimization, Salesforce Sales Cloud
Sales Cloud Innovation Driven by UX Design Principles
29Feb

Sales Cloud Innovation Driven by UX Design Principles

Driving Sales Cloud Innovation Through UX Design Principles: Sales Cloud Innovation Driven by UX Design Principles Enhancing user experiences and driving innovation within Sales Cloud relies on the fundamental principles of UX design. The core philosophy revolves around understanding users’ needs and ensuring simplicity as the default, allowing for increased trust and success. Here’s how three foundational UX design principles guide the product design team at Salesforce: UX Design in Action: The principles of meeting users where they’re at, maintaining low walls and high ceilings, and favoring simplicity are integral to Sales Cloud’s UX design philosophy. By adhering to these principles, Sales Cloud strives to build confidence among users, fostering a collaborative approach to developing innovative and user-friendly products.  Sales Cloud administrators need to operate with the same thought process. Tectonic is proud to introduce our Sales Cloud Implementation Solutions. Content updated May 2024. Like1 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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February 29, 2024in Salesforce Implementation Services, Salesforce Sales Cloud
Personalization With Customized AI-Driven Journeys
28Feb

Personalization With Customized AI-Driven Journeys

AI-Enabled Triggers for Guiding Customer Journeys – Personalization With Customized AI-Driven Journeys Initiate timely and relevant customer experiences that seamlessly lead individuals through their purchasing journey. Employ AI-powered decision-making to identify the most suitable next steps for customers, offering personalized suggestions based on real-time behavior, historical data, and business-specific datasets such as pricing and inventory. Deliver predefined experiences, such as browsing or cart abandonment journeys, while utilizing real-time interactions to determine the optimal content, channel, or offer for each customer. Efficiently extract insights by harnessing behavioral data and advanced analytics to visualize cross-channel customer journeys for both individuals and segments, identifying and resolving key friction points. Elevate customer acquisition, loyalty, and lifetime value by crafting personalized, omni-channel journeys that align with both customer desires and business objectives. Enable trigger-based customer journeys that facilitate immediate responses to customer actions, whether in the physical realm, such as entering a store and connecting to Wi-Fi, or in the virtual space, like visiting a shopping website. The Role of AI in Elevating the Customer Journey AI significantly contributes to heightened customer satisfaction, ultimately leading to improved retention. Address customer pain points in their preferred language and provide solutions tailored to their needs based on purchasing history and previous interactions with customer service. AI’s Influence Across Customer Journey Stages At each stage of the customer journey, AI transforms experiences by delivering personalized interactions from awareness to post-purchase. This transformation is made possible through automation, predictive analytics, and intelligent virtual agents. Transformative Impact of Generative AI on Customer Journeys Generative AI, exemplified by advanced language models like GPT-4, has the potential to revolutionize customer journeys. These models automate communication and content creation, dynamically adjusting tone and style to match customer preferences. For instance, Grammarly’s tone detector adapts communication based on the recipient’s profile and interaction history. Continuous Iteration and AI in Customer Journey Mapping In the era of digitization, AI-driven personalization surpasses traditional customer journey mapping based on a few personas. Organizations must harness AI and machine learning to create personalized journeys that enhance user experiences. The iterative improvement process involves collecting comprehensive data, utilizing AI for analysis and insights, implementing changes, and evaluating results through key performance indicators. Netflix: An AI Success Story Netflix serves as a prime example of AI success, continuously analyzing user behavior and preferences to refine content recommendation algorithms. This approach enhances personalization, leading to increased customer engagement and satisfaction. Integrating Generative AI into Existing Systems To fully capitalize on generative AI, integration into existing systems and processes is crucial. This may entail developing APIs to connect AI tools with customer relationship management (CRM) systems and content management systems. Testing and Continuous Enhancement Implementing AI-driven personalization necessitates a robust testing and evaluation process. Clearly defined key performance indicators and analytics capabilities are essential for measuring effectiveness and making continuous improvements. Like2 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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February 28, 2024in AI Tools, Data, Generative AI, Salesforce, Salesforce Marketing Cloud, Salesforce Personalization

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