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blocksifybuzz · 11 months

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Introduction to AI Platforms

AI Platforms are powerful tools that allow businesses to automate complex tasks, provide real-time insights, and improve customer experiences. With their ability to process massive amounts of data, AI platforms can help organizations make more informed decisions, enhance productivity, and reduce costs.

These platforms incorporate advanced algorithms such as machine learning, natural language processing (NLP), and computer vision to analyze data through neural networks and predictive models. They offer a broad range of capabilities such as chatbots, image recognition, sentiment analysis, and recommendation engines.

Choosing the right AI platform is imperative for businesses that wish to stay ahead of the competition. Each platform has its strengths and weaknesses which must be assessed when deciding on a vendor. Moreover, an AI platform’s ability to integrate with existing systems is critical in effectively streamlining operations.

The history of AI platforms dates back to the 1950s, with the development of early artificial intelligence research. However, over time these technologies have evolved considerably – thanks to advancements in computing power and big data analytics. While still in their infancy stages just a few years ago – today’s AI platforms have matured into complex and feature-rich solutions designed specifically for business use cases.

Ready to have your mind blown and your workload lightened? Check out the best AI platforms for businesses and say goodbye to manual tasks:

Popular Commercial AI Platforms

To explore popular the top AI platforms and make informed decisions, you need to know the benefits each platform offers. With IBM Watson, Google Cloud AI Platform, Microsoft Azure AI Platform, and Amazon SageMaker in focus, this section shows the unique advantages each platform provides for various industries and cognitive services.

IBM Watson

The Innovative AI Platform by IBM:

Transform your business with the dynamic cognitive computing technology of IBM Watson. Enhance decision-making, automate operations, and accelerate the growth of your organization with this powerful tool.

Additional unique details about the platform:

IBM Watson’s Artificial intelligence streamlines workflows and personalizes experiences while enhancing predictive capabilities. The open-source ecosystem allows developers and businesses alike to integrate their innovative applications seamlessly.

Suggested implementation strategies:

1) Leverage Watson’s data visualization tools to clearly understand complex data sets and analyze them. 2) Utilize Watson’s Natural Language processing capabilities for sentiment analysis, identifying keywords, or contextual understanding.

By incorporating IBM Watson’s versatile machine learning functions into your operations, you can gain valuable insights into customer behavior patterns, track industry trends, improve decision-making abilities, and eventually boost revenue. Google’s AI platform is so powerful, it knows what you’re searching for before you do.

Google Cloud AI Platform

The AI platform provided by Google Cloud is an exceptional tool for businesses that major in delivering machine learning services. It provides a broad array of functionalities tailored to meet the diverse demands of clients all over the world.

The following table summarizes the features and capabilities offered by the Google Cloud AI Platform:FeaturesCapabilitiesData Management & Pre-processing

– Large-scale data processing

– Data Integration and Analysis tools

– Deep Learning Frameworks

– Data versioning tools

Model Training

– Scalable training

– AutoML tools

– Advanced tuning configurations

– Distributed Training on CPU/GPU/TPU

Prediction

– High-performance responses within seconds

– Accurate predictions resulting from models trained using large-scale datasets.

Monitoring

– Real-time model supervision and adjustment

– Comprehensive monitoring, management, and optimization of models across various stages including deployment.

One unique aspect of the Google Cloud AI platform is its prominent role in enabling any developer, regardless of their prior experience with machine learning, to build sophisticated models. This ease of use accelerates experimentation and fosters innovation.

Finally, it is worth noting that according to a study conducted by International Business Machines Corporation (IBM), brands that adopted AI for customer support purposes experienced 40% cost savings while improving customer satisfaction rates by 90%.

#ai #machine learning #artificial intelligence #business

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lucas-henry · 1 year

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Forget ChatGPT | Other Mind Blowing AI Tools

Image: Freepik

In the ever-evolving world of technology, artificial intelligence (AI) has become an integral part of our lives. From virtual assistants to chatbots, AI is transforming the way we interact with digital solutions. While ChatGPT is a well-known language model that has garnered a lot of attention, there are many other mind-blowing AI tools that digital agencies can explore to enhance their digital solutions.

Digital agencies are in the business of providing comprehensive digital solutions to their clients. This includes everything from website development to social media management. By leveraging AI, digital agencies can improve the efficiency and effectiveness of their services, ultimately providing better results for their clients.

Let's explore some other AI tools that digital agencies can use beyond ChatGPT.

IBM Watson

IBM Watson is an AI-powered system that can analyze unstructured data, such as text, images, and videos, to provide insights and recommendations. This tool can be used to automate many tasks, such as customer service and content creation, and can help digital agencies improve their efficiency.

For example, digital agencies can use IBM Watson to analyze social media posts and determine which ones are generating the most engagement. This can help them identify trends and create content that is more likely to resonate with their audience.

Google Cloud Vision

Image: Unsplash

Google Cloud Vision is an AI-powered image analysis tool that can detect objects, faces, and text in images and videos. This tool can be used to automate many tasks, such as tagging and categorizing images, and can help digital agencies improve their image and video analysis capabilities.

For example, digital agencies can use Google Cloud Vision to analyze images and videos on social media to determine which ones are generating the most engagement. This can help them identify trends and create content that is more likely to resonate with their audience.

Amazon Polly

Amazon Polly is an AI-powered text-to-speech service that can convert text into lifelike speech. This tool can be used to automate many tasks, such as creating voice overs for videos, and can help digital agencies improve their content creation capabilities.

For example, digital agencies can use Amazon Polly to create voice overs for explainer videos, which can help them better communicate complex ideas to their audience.

TensorFlow

TensorFlow, an open-source AI library created by Google, is an excellent resource for building and training machine learning models. With its automation capabilities, this tool can streamline several tasks, including data analysis and predictive modeling, making it an invaluable asset for web development agencies looking to enhance their data analysis capabilities.

Image: Freepik

For example, digital agencies can use TensorFlow to build predictive models that can help them identify which social media posts are likely to generate the most engagement. This can help them create more effective social media campaigns.

Hootsuite Insights

Hootsuite Insights is an AI-powered social media analytics tool that can help digital agencies monitor and analyze social media conversations. This tool can be used to automate many tasks, such as sentiment analysis and trend identification, and can help digital agencies improve their social media management capabilities.

Image: Hootsuite

For example, digital agencies can use Hootsuite Insights to monitor social media conversations about their clients' brands and products. This can help them identify areas where their clients can improve their marketing efforts and create more effective social media campaigns.

Conclusion

In conclusion, while ChatGPT is an impressive AI tool, digital agencies have many other options to explore. By leveraging AI tools like IBM Watson, Google Cloud Vision, Amazon Polly, TensorFlow, and Hootsuite Insights, digital agencies can improve their digital solutions and provide better results for their clients. As AI continues to evolve, we can expect to see even more innovative and exciting applications of this technology in the digital agency industry.

#marketing #digitalmarketing #blog #new post #chatgpt #content creation #content marketing #blogger #social media #news #google #lateststories

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timeproperties · 1 year

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The Ultimate Guide to Finding the Best Real Estate Company in Dubai

Are you thinking of investing in the sizzling hot market that is Dubai real estate? Then congratulations on taking this important step towards financial success! Moving to a new country, however, can be daunting and intimidating - not least when it comes to finding the perfect real estate company. Well, you’ve come to the right place because, in this ultimate guide, we’re going to dive deep into everything you need to know about selecting the best company for your property needs in Dubai.

When seeking the best real estate companies in Dubai, there are several factors to consider:

Reputation

First and foremost, reputation is key. It is important to find the Best Real Estate Company in Dubai that has an established track record within the industry with a good standing among its clients. You should research the company's past projects and testimonials from current and former clients, as well as check their online reviews on websites such as Google, Yelp!, or Trustpilot. Additionally, make sure that you verify that the company has all necessary licenses and permits required by your local regulations when investing in property located in Dubai.

Capabilities

Second, you must expertly assess their market analysis capabilities. A good real estate firm should have expertise in the current economic trends for different areas of investment such as construction materials costs or financing options for developers; these will help inform your decision-making process when selecting an ideal property to invest in. Furthermore, look for companies who understand both local and international markets which will allow them to provide you with advice about what kind of properties may be more lucrative in foreign countries like Singapore or Malaysia than those available locally within Dubai itself.

Self-evident

Thirdly - and this may be self-evident - select firms with experienced advisors who listen carefully to your needs and focus on providing quality customer service at each stage of your investment journey. They should be able to offer assistance related not only to local legal matters but also taxation issues pertaining specifically to foreign nationals via tailor-made solutions that meet individual specifications through sound financial planning strategies rooted in data science technology trends set by global leaders like IBM Watson Analytics AI platforms & Amazon Web Services’s Data Lakes Architecture infrastructure responsible for optimizing business decisions across multiple industries today & tomorrow! Plus if they can showcase their portfolio of successful client acquisition images featuring prestigious developments then even better!

Final thoughts

We have examined the various moving parts when looking for the best real estate company in Dubai. From the laws and regulations governing real estate deals to the features that each digital platform offers, this ultimate guide has it all. With so much information available, it can be difficult to make a decision— but with Time Properties, you can rest assured knowing you are in good hands. As one of the leading real estate companies in Dubai, they offer everything you need to find your dream rental properties quickly and efficiently. Whether you’re looking for luxury apartments for rent or a villa, Time Properties is here to ensure absolute satisfaction. With their knowledgeable team and industry-leading services, Time Properties is undoubtedly the right choice for all of your real estate needs in Dubai!

#property finder dubai #commercial property for rent #dubai real estate #residential #office space for rent

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dhanjeerider · 1 year

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🔥 2 lack free offer GitHub student developer pack benifits

GitHub student developer pack Full information

GitHub is a web-based platform that provides developers with a collaborative environment to work on projects. GitHub is not only a code hosting platform, but it is also a place where developers can learn, collaborate, and build a community. One of the most significant offerings from GitHub is the GitHub Student Developer Pack. The GitHub Student Developer Pack is a bundle of free resources for students to learn, build, and grow as developers. In this article, we will discuss the benefits of the GitHub Student Developer Pack.

The GitHub Student Developer Pack is a bundle of software and services that are available to students for free. To be eligible for the pack, you need to be a student currently enrolled in a degree-granting institution. The GitHub Student Developer Pack includes access to over 100 products, services, and training resources, which can help you learn to code, build new projects, and launch your career as a developer.

The following are some of the benefits that come with the GitHub Student Developer Pack:

GitHub Pro Account

With the GitHub Student Developer Pack, you can get a free GitHub Pro account for two years. The GitHub Pro account includes unlimited private repositories, access to advanced tools, and personal support.

Domain Name

The GitHub Student Developer Pack also includes a free domain name from Namecheap. You can use this domain name to launch your own website or project.

Design Tools

The GitHub Student Developer Pack provides access to design tools like Canva, which is an online graphic design tool that you can use to create logos, social media graphics, and other designs.

Site Hosting

The GitHub Student Developer Pack also includes free site hosting from GitHub Pages. You can use this hosting to host your website or project.

Programming Apps

The GitHub Student Developer Pack provides access to a range of programming apps and tools. These include tools like Unity, Unreal Engine, PyCharm, Atom, and many more.

Developer Tools

The GitHub Student Developer Pack also provides access to a range of developer tools like AWS, DigitalOcean, and Heroku. These tools can help you deploy and manage your projects.

Training Resources

The GitHub Student Developer Pack also includes access to a range of training resources. These include online courses from Udacity, DataCamp, and others. These courses can help you learn new skills and advance your career as a developer.

Career Resources

The GitHub Student Developer Pack also includes access to career resources like a LinkedIn Learning subscription and a free trial of Hired. These resources can help you prepare for your career as a developer and find a job.

Cloud Services

The GitHub Student Developer Pack includes access to cloud services such as Microsoft Azure, Google Cloud, and IBM Cloud. With these services, you can deploy and scale your applications, run experiments, and gain experience with cloud technologies.

Analytics Tools

The pack also includes access to analytics tools like LogDNA and Datadog. These tools can help you monitor your applications and gain insights into user behavior, performance, and security.

Mobile Development Tools

The pack also includes tools for mobile development, such as the Xamarin platform for building native iOS and Android applications, and the Ionic framework for building cross-platform mobile apps.

Data Science Tools

The GitHub Student Developer Pack includes access to data science tools like IBM Watson Studio, DataRobot, and Kaggle. These tools can help you explore data, build models, and gain insights from your data.

Productivity Tools

The pack also includes productivity tools like Trello, Asana, and Notion. These tools can help you stay organized, manage your projects, and collaborate with your team.

Cybersecurity Tools

The pack includes cybersecurity tools like the HackEDU Secure Development Training platform, which can help you learn about secure coding practices and develop more secure applications.

Video Conferencing Tools

The pack includes video conferencing tools like Zoom and Crowdcast. These tools can help you communicate and collaborate with your team, attend webinars, and host your own online events.

Code Education Resources

The pack also includes code education resources such as free access to Frontend Masters, a platform for learning web development, and free access to Packt Publishing eBooks, which cover a wide range of topics related to programming and technology.

In summary, the GitHub Student Developer Pack is an excellent resource for students who are interested in learning to code, building new projects, and launching their careers as developers. With access to over 100 products, services, and training resources, students can take their skills to the next level and start building the future

Access GitHub student developer pack

In conclusion

the GitHub Student Developer Pack is an amazing resource for students who are interested in pursuing a career in technology. With access to a wide range of tools, services, and educational resources, students can gain hands-on experience, learn new skills, and build their own projects. The pack is a great way to get started on the path to becoming a professional developer, and it provides many opportunities for growth and career development"

from Dk Technozone https://ift.tt/IsMX3pV

#Blogging #website #seo #Tips and Tricks #android tils

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aitechnologies · 1 year

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Latest Artificial Intelligence Technologies

AI has taken a tempest in each industry and significantly affects each area of society. The term Artificial intelligence terms were first begun in 1956 at a meeting. The discussion of the gathering prompted interdisciplinary data tech natural language generationnology. The presence of the web helped development with progressing decisively. Artificial intelligence technology was an independent technology quite a while back(30 years), however presently the applications are boundless in each circle of life. Artificial intelligence is known by the AL abbreviation and is the most common way of reproducing human intelligence in machines.

Let us see a few more Artificial Intelligence Technologies…

1. Natural language generation

Machines process and convey another way than the human cerebrum. Normal language age is an in vogue innovation that converts organized information into the native language. The machines are modified with algorithms to change over the data into a desirable format for the client. Natural language is a subset of man-made artificial intelligence that assists content designers with computerizing content and conveying the desirable format . The content developers can utilize the automated content to advance on different social media entertainment stages, and different media stages to reach the targeted audience. Human intercession will essentially lessen as information will be changed over into desired formats. The information can be envisioned as charts, graphs, and so forth.

2. Speech recognition

Speech recognition is one more significant subset of artificial intelligence that changes over human speech into a helpful and justifiable format by PCs. Speech recognition is an extension among human and PC connections. The innovation perceives and changes over human speech in a few languages. Siri on the iPhone is a commendable outline of speech recognition.

3. Virtual agents

Virtual agents have become significant apparatuses for instructional designers. A virtual agent is a computer application that collaborates with humans. Web and mobile applications give chatbots as their client support specialists to communicate with humans to answer their questions. Google Assistant assists with arranging meetings, and Alexia from Amazon assists with making your shopping simple & easy. A virtual assistant additionally behaves like a language partner, which picks prompts from your choice and preference. The IBM Watson comprehends the average customer service questions which are asked in more ways than one. Virtual agents go about as software-as-a-service too.

4. Decision management

Modern organizations are executing decision management systems for data transformation and understanding into prescient models. Enterprise-level applications execute decision management systems to get modern data to perform business data analysis to support authoritative independent decision-making. Decision management helps in settling on fast decisions, evasion of dangers, and in the automation of the process. The decision management system is generally carried out in the monetary area, the medical services area, trading, insurance sector, web based business, and so on.

5. Biometrics

Deep learning is one more part of artificial intelligence that capabilities in view of artificial neural networks. This method helps PCs and machines to advance as a visual demonstration simply of the manner in which humans do. The expression "deep" is begat in light of the fact that it has stowed away layers in neural networks. Ordinarily, a neural network has 2-3 secret layers and can have a limit of 150 secret layers. Deep learning is viable on enormous information to prepare a model and a realistic handling unit. The algorithms work in an order to automate predictive analytics. Deep learning has spread its wings in numerous domains like aviation and military to distinguish objects from satellites, helps in further developing specialist security by recognizing risk occurrences when a labourer draws near to a machine, assists with identifying malignant growth cells, and so forth.

6. Machine learning

Machine learning is a division of artificial intelligence which enables machines to check out data collections without being actually programmed. Machine learning strategy assists businesses to pursue informed decisions with data analytics performed utilizing algorithms and statistical models. Endeavours are putting vigorously in machine learning to receive the rewards of its application in different domains. Medical services and the clinical calling need machine learning methods to examine patient information for the prediction of diseases and viable treatment. The banking and monetary area needs machine learning for customer data analysis to recognize and propose venture choices to clients and for risk and fraud prevention. Retailers use machine learning for predicting changing client preferences, consumer conduct, by breaking down customer data.

7. Robotic process automation

Robotic process automation is a use of artificial intelligence that designs a robot (programming application) to decipher, convey and analyze information. This discipline of artificial intelligence assists with automating to some degree or completely manual operations that are repetitive and rule-based.

8. Peer-to-peer network

The peer-to-peer network assists with associating between various systems and computers for data sharing without the data transmitting via server. Peer-to-peer networks can take care of the most intricate issues. This technology is utilized in digital forms of money(cryptocurrencies). The implementation is financially savvy as individual workstations are connected and servers are not installed.

9. Deep learning platforms

10. AL optimized hardware

Artificial intelligence software has a popularity in the business world. As the consideration for the software expanded, a requirement for the equipment that upholds the software likewise emerged. A regular chip can't uphold artificial intelligence models. Another age of artificial intelligence chips is developed for neural networks, deep learning, and PC vision. The AL hardware incorporates central processors to deal with versatile responsibilities, unique reason worked in silicon for neural networks, neuromorphic chips, and so on. Organizations like Nvidia, Qualcomm. AMD is creating chips that can perform complex artificial intelligence estimations. Medical services and automobile might be the industries that will profit from these chips.

Conclusion

To close, Artificial Intelligence addresses computational models of intelligence. Intelligence can be depicted as designs, models, and functional capabilities that can be programmed for critical thinking, inductions, language processing, and so on. The advantages of utilizing artificial intelligence are now procured in numerous areas. Organizations taking on artificial intelligence ought to run pre-release preliminaries to dispense with inclinations and blunders. The design, models, ought to be robust. In the wake of delivering artificial systems, enterprises ought to screen constantly in various situations. Organizations ought to make and keep up with principles and recruit specialists from different disciplines for better decision-making. The goal and future objectives of artificial intelligence are to automate all complex human activities and take out mistakes and inclinations.

#artificial intelligence companies #artificial intelligence services #emerging technology companies #cutting edge technology companies

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lynxdragon1 · 2 years

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9 Easy Facts About Video Production Made Simple: A Step-by-Step Guide - Skeleton Explained

The Role of a High Quality Video Production Service For Business - Arturoherrera

The 2-Minute Rule for Video Production - Collin College

The video production process. Illustration by Vladanland The video production procedure consists of 3 main steps:, which is the preparation stage for mapping out your strategy and script for the video, is the phase in which the video is shot, and finally, which includes editing the video, adding music and other results.

Pre-Production The first phase while doing so is pre-production. Basically, pre-production is where you will map out the prepare for your video. You'll figure out what you're going to produce, who you'll be producing for, what resources you'll require to get the video made and for how long the production period will be.

The Main Principles Of What is Video Production?

What are your goals? through Prior to you even start planning, you need to define the goals behind this video. Why are you making it? What do סרטון אנימציה שיווקי want from it? Who's the audience, and what will they acquire from it? Like any other kind of content, a video requires an objective from the extremely beginning to assist the job and measure whether it's a success.

e., identify goals that are: Specific Quantifiable Attainable Appropriate Time-bound Who is this video for? An effective video understands who it's speaking with. You might already have a company understanding of who your audience is, what they like, and how they believe. If that's the case then articulate it here.

3 Easy Facts About Video & Digital Media Production - Minnesota State University Described

Surpass just learning their basic age, gender and location. What are their most typical problems, questions and interests? What do they get in touch with? Who are their impacts? Conduct interviews, request feedback, trawl your social networks pages to find out who's getting in touch with your brand and inquire concerns.

Corporate Video Production and Event Video Services - IBM Watson Media

What is your core message? Now that you know who you're making this video for it's time to assess that information, integrate it with your objectives and develop your core message. Think of what your audience needs to wish to do after watching your video and work backwards from there.

#פתרונות שיווק #הפקת סרטון #פרסום ביוטיוב #הפקת סרטון תדמית מחיר

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apapae · 7 days

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2024: Predictive Analytics Transforming Lead Management

Lead generation is the initial step in identifying and marketing to qualified, interested prospects. However, it changes as tactics and technology evolve. Understanding lead generation trends may be useful in this circumstance.

Businesses generate and collect an increasing amount of data as technology advances. Companies use predictive analytics technologies to gain a competitive advantage and make data-driven decisions.

For More Information: https://sales-demand.com/2024-predictive-analytics-transforming-lead-management/

Effective lead generation also requires the integration of sales and marketing, as companies need to make sure that their marketing and sales initiatives are coordinated.

Important aspects that organizations need to take into account for their lead generation operations include analytics and measurement, as well as interactive content for engagement.

Predictive Analytics in Lead Management

Predictive analytics is similar to having a smart assistant for your firm. It combines data, trends, and algorithms to forecast what will happen in the future. In layman's words, it's the magic of predicting which leads are more likely to become satisfied customers.

1# Growing Significance

Imagine having the ability to predict which potential customers are most interested in your products or services. That is exactly what it accomplishes with B2B lead management. It has grown in importance since it improves the overall efficiency and effectiveness of the process.

2# Key Factors Driving Adoption

Businesses are embracing predictive analytics for several reasons. For starters, it saves time and resources by focusing on the leads with the highest conversion rates. Second, it increases sales by personalizing messages and offerings to each lead's specific needs. It's like having a personal guide through the maze of potential consumers, making the trip easier and more fruitful.

Benefits of Integrating Predictive Analytics

Integrating predictive analytics into your business is like adding a superhero cape to your marketing strategy. Here’s why:

1# Improved Lead Targeting and Segmentation

Consider predictive analytics to be your trusted partner, assisting you in precisely targeting your marketing campaigns. It goes through massive amounts of data to locate the ideal audience for your products or services. This means no more wasting time on leads who aren't a good fit; it's like having a GPS for your marketing target.

Visit Us: www.sales-demand.com

2# Enhanced Personalization

Predictive analytics takes personalization to a whole new level. It’s like having a conversation with each lead in their own language. By understanding their preferences and behaviors, it crafts messages that feel tailor-made. It’s the difference between receiving a generic ad and one that feels like it was made just for you – making your brand more like a friend than a stranger.

3# Boosting Campaign ROI

Imagine having a secret map that guides your campaigns to success. Predictive analytics provides that map by giving you insights backed by data. It’s not just a shot in the dark; it’s a calculated move. This precision ensures that every penny spent on your campaign contributes meaningfully to your business growth. It’s like having a financial advisor for your marketing budget, making sure it works harder and smarter.

Predictive Analytics Tools and Technologies

In the dynamic realm of business, predictive analytics tools have become indispensable for foresight and strategy. Here’s a simple breakdown:

Overview of Popular Tools: Predictive analytics has champions like IBM Watson, Google Analytics, and Salesforce Einstein. These tools harness the power of data to forecast trends and patterns.

Our Services: https://sales-demand.com/lead-generation-solutions/

Comparative Analysis: Each tool brings unique strengths. IBM Watson excels in machine learning, Google Analytics in web analytics, and Salesforce Einstein integrates seamlessly with CRM. Understanding these distinctions ensures you pick the tool aligned with your specific needs.

Choosing the Right Tools: Businesses should tailor their choice based on requirements. Consider factors like data volume, industry specificity, and budget. For instance, if customer relationship management is a priority, Salesforce Einstein might be the ideal fit.

Navigating Predictive Analytics: Overcoming Challenges

It brings promises but also hurdles. Let’s simplify:

Common Challenges: Implementing predictive analytics may face obstacles like data complexities and resistance to change. Recognizing these hurdles is the first step.

Strategies for Success: Overcoming challenges involves strategic planning. Engage stakeholders, provide training, and ensure alignment with business goals to pave the way for seamless integration.

Data Quality and Ethics: The heart of predictive analytics is quality data. Ensuring accuracy and ethical usage is paramount. A commitment to ethical practices builds trust and reliability in predictive insights.

Predictive Analytics Trends

Delving into the future of predictive analytics offers a glimpse into exciting possibilities:

Emerging Trends: It evolves. Stay attuned to new algorithms, machine learning advancements, and data visualization tools, ensuring your lead management stays ahead.

Innovations in B2B Marketing: Witness a transformation in B2B marketing as predictive analytics continues to innovate. From personalized customer experiences to refined targeting, the possibilities are vast.

Beyond 2024: Peer into the crystal ball for anticipated developments. Expect further integration with AI, heightened automation, and even more sophisticated predictive models.

Future Trends and Innovations

As we set sail into the future, anticipate these transformative trends:

Emerging Trends in Lead Management: Stay ahead by embracing new trends in predictive analytics. From augmented reality integrations to predictive lead scoring advancements, the landscape is ever-evolving.

Our Services: https://sales-demand.com/database-solutions/

Innovations Shaping B2B Marketing: Witness ground breaking innovations shaping the future of B2B marketing. Personalized AI-driven strategies, immersive customer experiences, and automated decision-making are set to redefine the industry.

Anticipated Developments Beyond 2024: Look beyond the horizon and envision developments post-2024. Expect increased synergy between predictive analytics and artificial intelligence, resulting in unparalleled insights and efficiency.

Conclusion

This transformative journey underscores the essential role of predictive analytics in the ever-evolving B2B lead management arena. Businesses eagerly welcome these developments, seeing them as the key to being competitive in the future. Accept the power of predictive analytics and set yourself up for long-term success.

Boost your business with Sales Demand’s help in managing leads. Get in touch to discover how we can bring you more potential customers. To learn more, contact us now.

#b2b marketing #content syndication

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sunaleisocial · 9 days

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3 Questions: Enhancing last-mile logistics with machine learning

New Post has been published on https://sunalei.org/news/3-questions-enhancing-last-mile-logistics-with-machine-learning/

3 Questions: Enhancing last-mile logistics with machine learning

Across the country, hundreds of thousands of drivers deliver packages and parcels to customers and companies each day, with many click-to-door times averaging only a few days. Coordinating a supply chain feat of this magnitude in a predictable and timely way is a longstanding problem of operations research, where researchers have been working to optimize the last leg of delivery routes. This is because the last phase of the process is often the costliest due to inefficiencies like long distances between stops due to increased ecommerce demand, weather delays, traffic, lack of parking availability, customer delivery preferences, or partially full trucks — inefficiencies that became more exaggerated and evident during the pandemic.

With newer technology and more individualized and nuanced data, researchers are able to develop models with better routing options but at the same time need to balance the computational cost of running them. Matthias Winkenbach, MIT principal research scientist, director of research for the MIT Center for Transportation and Logistics (CTL) and a researcher with the MIT-IBM Watson AI Lab, discusses how artificial intelligence could provide better and more computationally efficient solutions to a combinatorial optimization problem like this one.

Q: What is the vehicle routing problem, and how do traditional operations research (OR) methods address it?

A: The vehicle routing problem is faced by pretty much every logistics and delivery company like USPS, Amazon, UPS, FedEx, DHL every single day. Simply speaking, it’s finding an efficient route that connects a set of customers that need to be either delivered to, or something needs to be picked up from them. It’s deciding which customers each of those vehicles — that you see out there on the road — should visit on a given day and in which sequence. Usually, the objective there is to find routes that lead to the shortest, or the fastest, or the cheapest route. But very often they are also driven by constraints that are specific to a customer. For instance, if you have a customer who has a delivery time window specified, or a customer on the 15th floor in the high-rise building versus the ground floor. This makes these customers more difficult to integrate into an efficient delivery route.

To solve the vehicle routing problem, we obviously we can’t do our modeling without proper demand information and, ideally, customer-related characteristics. For instance, we need to know the size or weight of the packages ordered by a given customer, or how many units of a certain product need to be shipped to a certain location. All of this determines the time that you would need to service that particular stop. For realistic problems, you also want to know where the driver can park the vehicle safely. Traditionally, a route planner had to come up with good estimates for these parameters, so very often you find models and planning tools that are making blanket assumptions because there weren’t stop-specific data available.

Machine learning can be very interesting for this because nowadays most of the drivers have smartphones or GPS trackers, so there is a ton of information as to how long it takes to deliver a package. You can now, at scale, in a somewhat automated way, extract that information and calibrate every single stop to be modeled in a realistic way.

Using a traditional OR approach means you write up an optimization model, where you start by defining the objective function. In most cases that’s some sort of cost function. Then there are a bunch of other equations that define the inner workings of a routing problem. For instance, you must tell the model that, if the vehicle visits a customer, it also needs to leave the customer again. In academic terms, that’s usually called flow conservation. Similarly, you need to make sure that every customer is visited exactly once on a given route. These and many other real-world constraints together define what constitutes a viable route. It may seem obvious to us, but this needs to be encoded explicitly.

Once an optimization problem is formulated, there are algorithms out there that help us find the best possible solution; we refer to them as solvers. Over time they find solutions that comply with all the constraints. Then, it tries to find routes that are better and better, so cheaper and cheaper ones until you either say, “OK, this is good enough for me,” or until it can mathematically prove that it found the optimal solution. The average delivery vehicle in a U.S. city makes about 120 stops. It can take a while to solve that explicitly, so that’s usually not what companies do, because it’s just too computationally expensive. Therefore, they use so-called heuristics, which are algorithms that are very efficient in finding reasonably good solutions but typically cannot quantify how far away these solutions are from the theoretical optimum.

Q: You’re currently applying machine learning to the vehicle routing problem. How are you employing it to leverage and possibly outperform traditional OR methods?

A: That’s what we’re currently working on with folks from the MIT-IBM Watson AI Lab. Here, the general idea is that you train a model on a large set of existing routing solutions that you either observed in a company’s real-world operations or that you generated using one of these efficient heuristics. In most machine-learning models, you no longer have an explicit objective function. Instead, you need to make the model understand what kind of problem it’s actually looking at and what a good solution to the problem looks like. For instance, similar to training a large language model on words in a given language, you need to train a route learning model on the concept of the various delivery stops and their demand characteristics. Like understanding the inherent grammar of natural language, your model needs to understand how to connect these delivery stops in a way that results in a good solution — in our case, a cheap or fast solution. If you then throw a completely new set of customer demands at it, it will still be able to connect the dots quite literally in a way that you would also do if you were trying to find a good route to connect these customers.

For this, we’re using model architectures that most people know from the language processing space. It seems a little bit counterintuitive because what does language processing have to do with routing? But actually, the properties of these models, especially transformer models, are good at finding structure in language — connecting words in a way that they form sentences. For instance, in a language, you have a certain vocabulary, and that’s fixed. It’s a discrete set of possible words that you can use, and the challenge is to combine them in a meaningful way. In routing, it’s similar. In Cambridge there are like 40,000 addresses that you can visit. Usually, it’s a subset of these addresses that need to be visited, and the challenge is: How do we combine this subset — these “words” — in a sequence that makes sense?

That’s kind of the novelty of our approach — leveraging that structure that has proven to be extremely effective in the language space and bringing it into combinatorial optimization. Routing is just a great test bed for us because it’s the most fundamental problem in the logistics industry.

Of course, there are already very good routing algorithms out there that emerged from decades of operations research. What we are trying to do in this project is show that with a completely different, purely machine learning-based methodological approach, we are able to predict routes that are pretty much as good as, or better than, the routes that you would get from running a state-of-the-art route optimization heuristic.

Q: What advantages does a method like yours have over other state-of-the-art OR techniques?

A: Right now, the best methods are still very hungry in terms of computational resources that are required to train these models, but you can front-load some of this effort. Then, the trained model is relatively efficient in producing a new solution as it becomes required.

Another aspect to consider is that the operational environment of a route, especially in cities, is constantly changing. The available road infrastructure, or traffic rules and speed limits might be altered, the ideal parking lot may be occupied by something else, or a construction site might block a road. With a pure OR-based approach, you might actually be in trouble because you would have to basically resolve the entire problem instantly once new information about the problem becomes available. Since the operational environment is dynamically changing, you would have to do this over and over again. While if you have a well-trained model that has seen similar issues before, it could potentially suggest the next-best route to take, almost instantaneously. It’s more of a tool that would help companies to adjust to increasingly unpredictable changes in the environment.

Moreover, optimization algorithms are often manually crafted to solve the specific problem of a given company. The quality of the solutions obtained from such explicit algorithms is bounded by the level of detail and sophistication that went into the design of the algorithm. A learning-based model, on the other hand, continuously learns a routing policy from data. Once you have defined the model structure, a well-designed route learning model will distill potential improvements to your routing policy from the vast amount of routes it is being trained on. Simply put, a learning-based routing tool will continue to find improvements to your routes without you having to invest into explicitly designing these improvements into the algorithm.

Lastly, optimization-based methods are typically limited to optimizing for a very clearly defined objective function, which often seeks to minimize cost or maximize profits. In reality, the objectives that companies and drivers face are much more complex than that, and often they are also somewhat contradictory. For instance, a company wants to find efficient routes, but it also wants to have a low emissions footprint. The driver also wants to be safe and have a convenient way of serving these customers. On top of all of that, companies also care about consistency. A well-designed route learning model can eventually capture these high-dimensional objectives by itself, and that is something that you would never be able to achieve in the same way with a traditional optimization approach.

So, this is the kind of machine learning application that can actually have a tangible real-world impact in industry, on society, and on the environment. The logistics industry has problems that are much more complex than this. For instance, if you want to optimize an entire supply chain — let’s say, the flow of a product from the manufacturer in China through the network of different ports around the world, through the distribution network of a big retailer in North America to your store where you actually buy it — there are so many decisions involved in that, which obviously makes it a much harder task than optimizing a single vehicle route. Our hope is that with this initial work, we can lay the foundation for research and also private sector development efforts to build tools that will eventually enable better end-to-end supply chain optimization.

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jcmarchi · 9 days

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3 Questions: Enhancing last-mile logistics with machine learning

New Post has been published on https://thedigitalinsider.com/3-questions-enhancing-last-mile-logistics-with-machine-learning/

3 Questions: Enhancing last-mile logistics with machine learning

Q: What is the vehicle routing problem, and how do traditional operations research (OR) methods address it?

Q: You’re currently applying machine learning to the vehicle routing problem. How are you employing it to leverage and possibly outperform traditional OR methods?

Q: What advantages does a method like yours have over other state-of-the-art OR techniques?

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kirnakumar155 · 12 days

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SAP Ariba IBM

SAP Ariba and IBM: Transforming Procurement for the Digital Age

Procurement, a core business function, has recently undergone a significant transformation. The rise of cloud-based solutions, intelligent automation, and the need for more data-driven insights are shaping the future of buying and sourcing for businesses of all sizes. Two technology leaders, SAP Ariba and IBM, are at the forefront of this change, delivering innovative solutions that help companies streamline and optimize their procurement processes.

What is SAP Ariba?

SAP Ariba is a market-leading suite of cloud-based procurement solutions spanning the entire source-to-pay (S2P) cycle. It brings buyers and suppliers together on a vast collaborative network, the Ariba Network, facilitating efficient transactions and deeper insights into spend patterns. Key components of SAP Ariba include:

Strategic Sourcing: Tools for e-sourcing, supplier discovery, and contract management.

Procure-to-Pay (P2P): Automating requisitions, purchase orders, invoicing, and payments.

Supplier Management: Centralized supplier information for performance tracking and risk mitigation.

Spend Analysis: Advanced analytics and visualizations to uncover spending patterns and identify savings opportunities.

IBM’s Role in the SAP Ariba Ecosystem

IBM has a robust partnership with SAP, acting as a key consulting and implementation partner for SAP Ariba solutions. IBM brings several strengths to the table:

Deep Industry Expertise: IBM consultants understand how procurement operates in various sectors, offering tailored implementation support.

Cognitive Capabilities: IBM Watson infuses SAP Ariba with AI-powered insights for enhanced decision-making and intelligent supplier matching.

Integration Services: IBM helps organizations seamlessly connect SAP Ariba with their existing ERP and supply chain systems.

Additional Solutions: IBM offers complementary technologies like blockchain and robotic process automation (RPA) to extend the capabilities of SAP Ariba further.

Key Benefits of the SAP Ariba and IBM Collaboration

The synergy between SAP Ariba and IBM offers several compelling advantages for organizations seeking to transform their procurement operations:

Cost Reduction: Automated processes, streamlined workflows, and more excellent spend visibility help cut costs associated with procurement.

Improved Compliance: Rules-based processes, contract management, and supplier risk monitoring enhance compliance with company policies and regulations.

Enhanced Supplier Collaboration: Facilitates effective supplier communication, improves contract negotiations, and fosters innovation.

Data-Driven Insights: Powerful analytics tools translate complex spend data into actionable insights, driving better strategic decisions.

Agility and Scalability: Cloud-based solutions and flexible deployment options enable companies to respond quickly to changing needs and scale up or down as required.

The Road Ahead

The future of procurement looks bright thanks to the innovations being driven by SAP Ariba and IBM. Integrating technologies like AI, blockchain, and IoT is bound to make procurement even more intelligent, efficient, and transparent. Suppose your organization wishes to remain competitive and gain a strategic edge in the years ahead. In that case, it’s imperative to consider cloud-based procurement solutions and the potential benefits of the SAP Ariba and IBM partnership.

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cmsgpblog · 12 days

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Navigating the IoT Ecosystem: Unveiling the Top IoT Device Management Companies

In the dynamic landscape of the Internet of Things (IoT), where billions of interconnected devices are reshaping industries and lifestyles, effective device management is paramount. IoT device management companies play a pivotal role in ensuring the seamless operation, security, and scalability of IoT deployments. In this article, we delve into the realm of IoT device management, spotlighting leading companies driving innovation in this critical sector.

The Significance of IoT Device Management

As IoT ecosystems continue to expand, encompassing a diverse array of devices spanning industries such as healthcare, manufacturing, transportation, and smart cities, the need for robust device management solutions becomes increasingly apparent. IoT device management encompasses a range of functionalities, including:

Device Provisioning: Onboarding and provisioning devices onto the network securely and efficiently.

Configuration Management: Remote configuration and management of device settings and parameters.

Monitoring and Diagnostics: Real-time monitoring of device health, performance, and status, as well as diagnostics and troubleshooting.

Firmware Updates: Over-the-air (OTA) firmware updates to ensure devices are running the latest software versions with patches and security enhancements.

Security and Compliance: Implementing security measures such as authentication, encryption, and access control to safeguard devices and data, as well as ensuring compliance with industry regulations and standards.

Top IoT Device Management Companies

Microsoft Azure IoT: Microsoft's Azure IoT platform offers comprehensive device management capabilities through Azure IoT Hub. With features such as device provisioning service, device twin management, and over-the-air updates, Azure IoT enables secure and scalable management of IoT devices deployed in various environments.

IBM Watson IoT: IBM Watson IoT provides a robust device management solution that enables organizations to efficiently manage and monitor their IoT devices at scale. With features like device registration, configuration management, and software updates, IBM Watson IoT empowers businesses to drive operational efficiencies and optimize device performance.

AWS IoT Device Management: Amazon Web Services (AWS) offers a suite of IoT device management services under AWS IoT, including AWS IoT Device Management. This service allows organizations to onboard, organize, and remotely manage IoT devices at scale, with capabilities such as bulk registration, fleet indexing, and device shadowing.

Cisco IoT Control Center: Cisco IoT Control Center provides a comprehensive platform for managing cellular-connected IoT devices. With features like device provisioning, connectivity management, and real-time diagnostics, Cisco IoT Control Center enables organizations to streamline device lifecycle management and optimize connectivity costs.

Sierra Wireless Octave: Sierra Wireless Octave is a comprehensive IoT platform that includes device management capabilities for cellular-connected devices. With features like device provisioning, configuration management, and firmware updates, Sierra Wireless Octave simplifies the deployment and management of IoT solutions across diverse industries.

Emerging Trends and Future Outlook

As the IoT landscape continues to evolve, several trends are shaping the future of IoT device management:

Edge Computing: The integration of edge computing capabilities into device management solutions enables localized processing and decision-making, reducing latency and bandwidth requirements.

AI and Analytics: The use of artificial intelligence (AI) and advanced analytics enhances device management capabilities, enabling predictive maintenance, anomaly detection, and optimization of device performance.

Blockchain Integration: Blockchain technology is being explored to enhance the security, integrity, and traceability of device management operations, particularly in industries with stringent regulatory requirements.

Interoperability and Standards: Efforts to standardize device management protocols and ensure interoperability among IoT devices facilitate seamless integration and management across heterogeneous IoT ecosystems.

In conclusion, IoT device management companies play a crucial role in enabling the seamless operation and optimization of IoT deployments across industries. By leveraging innovative technologies and best practices, these companies empower organizations to harness the full potential of IoT, driving efficiency, innovation, and growth in the digital age.

#IoT device management companies

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