Setting up and managing course social learning and networking in Moodle involves integrating various tools and features that allow students to interact, collaborate, and share knowledge. Below is a step-by-step guide to help you create a socially engaging and networked learning environment in your Moodle course:

1. Set up the Moodle Course

• Create a New Course: Start by creating a course in Moodle. Go to your Moodle homepage, select “Site administration” > “Courses” > “Add a new course”, and fill out the necessary course information such as course name, summary, and format.
• Choose the Format: Select a course format that supports collaboration. Social format or Weekly format can be good options for this.
  • Social format: Displays only one forum but allows for social interaction and can be great for courses focusing on social learning.
  • Weekly format: Divides the course into weekly blocks, useful for managing course progression with social learning tools attached.

2. Enable Social Learning Tools

Moodle offers several tools to encourage social learning. You can integrate these tools into your course to enhance networking and collaboration.

a) Forums

• Use forums for discussion: Moodle forums are excellent for fostering social learning and networking. You can set up various types of forums such as Standard forum for general use, Q&A forums, or Single simple discussion forums.
  • Create discussion threads: Encourage learners to share ideas, ask questions, or respond to prompts.
  • Use group forums: If you divide the students into groups, you can create forums where students from the same group can collaborate.
• Moderate forums: Enable forum moderation to ensure that discussions stay on topic and are respectful.

b) Messaging and Chat

• Direct messaging: Students can send private messages to each other. Encourage students to communicate through messaging for questions and feedback.
• Chat rooms: Set up synchronous chat rooms for real-time discussions. You can create a scheduled chat session for students to join, allowing for group conversations or Q&A sessions.

c) Blogs

• Student Blogs: Moodle allows students to create their own blogs where they can post reflections, projects, or learning milestones. This gives students a platform for self-expression and interaction.
• Peer feedback: Students can comment on each other’s blog posts to encourage feedback and engagement.

d) Wikis

• Collaborative Content Creation: Set up a wiki where students can collaborate on creating documents, notes, or content for the course. This tool is useful for team projects and research.

e) Glossaries

• Create a Collaborative Glossary: Allow students to contribute to a shared glossary of terms related to the course. They can define terms and concepts, creating a useful reference that everyone in the course can benefit from.

f) Peer and Self-Assessment

• Enable peer assessments: Allow students to assess their peers’ work, projects, or contributions. This can be done through the Assignment activity or Workshop activity.
• Reflection activities: Encourage self-reflection where students can track their learning progress and share insights with their peers.

3. Organize Group Work

• Group Activities: Divide your class into groups for collaboration and learning. Group work can be done using forums, wikis, assignments, or file-sharing tasks.
• Group Discussions: Use group forums or group chat to allow students within the same group to collaborate, share ideas, and discuss their projects.
• Group Wikis: Let each group create and manage their own wiki. They can collaborate on research, write group reports, and provide peer feedback on each other’s content.

4. Create and Manage Community Building Tools

• Course Announcements: Use the News Forum for important announcements and updates. You can use this to foster communication between the instructor and students.
• Social Networking Plugins: Consider installing third-party plugins for more advanced networking features such as Mahara for ePortfolios or integration with social media platforms.

5. Gamification and Badges

• Use Gamification: To keep learners engaged and motivated, you can implement gamification tools such as badges, points, and leaderboards.
• Award Badges: Assign badges for milestones, achievements, or contributions to discussions, wikis, or blogs. This can help encourage student participation and engagement.
• Activity Completion: Use the Activity Completion feature to set requirements for completing tasks or activities. This ensures that students stay on track while fostering interaction.

6. Encourage Active Participation

• Interactive Activities: Make use of Moodle’s interactive features such as quizzes, polls, and surveys to stimulate learning and social interaction.
• Invite Guest Speakers: Invite guest speakers or experts to join your course through video conferencing (using BigBlueButton or Zoom integration) to provide an opportunity for students to interact and network with professionals in the field.
• Interactive Media: Encourage students to share videos, podcasts, or multimedia content on topics related to the course.

7. Monitor and Assess Social Learning

• Monitor Student Engagement: Use Moodle’s reports and logs to monitor student engagement in discussions, forums, and other social learning tools. This helps you identify students who may need additional support.
• Track Collaboration and Contributions: Through activity completion, forum participation, or peer assessments, you can keep track of how students are collaborating with one another.

8. Customize Your Moodle for Social Learning

• Personalized Dashboards: Allow students to personalize their Moodle dashboard to see updates and notifications related to their social learning activities.
• Profiles and Badges: Encourage students to complete their profiles, add photos, and list their achievements. This helps build an online learning community and facilitates networking among learners.
• External Social Media Integration: If desired, integrate Moodle with external social media tools (e.g., Twitter, Facebook) for extended networking opportunities.

9. Foster a Positive Online Learning Culture

• Set Ground Rules: Define clear guidelines for online communication, discussions, and collaboration to ensure a positive and respectful environment.
• Encourage Collaboration: Promote collaboration over competition. Give students opportunities to work together, share ideas, and learn from one another.
• Provide Regular Feedback: Keep the communication channels open by offering constructive feedback on their interactions, work, and contributions.

By using these strategies, Moodle can become an effective platform for social learning and networking, encouraging active engagement, collaboration, and peer-to-peer support throughout the course.

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Moodle (Modular Object-Oriented Dynamic Learning Environment) is a free, open-source Learning Management System (LMS) designed to help educators create and manage online courses. It provides a flexible platform for delivering educational content, assessments, and collaborative learning activities.

How Does Moodle Work?

Moodle operates as a web-based platform where instructors and students interact in a virtual learning environment. Here’s how it works:

1. Course Creation & Management

• Instructors can create courses, organize materials (documents, videos, quizzes), and set up learning paths.
• Courses can be structured by weeks, topics, or competencies.
• Supports blended learning (mix of online and in-person classes).

2. User Roles & Access Control

• Administrators manage the site, plugins, and user permissions.
• Teachers/Trainers create and deliver course content.
• Students access materials, submit assignments, and participate in discussions.
• Guests may have limited access (depending on settings).

3. Learning Tools & Activities

• Assignments: Students submit work online (files or text).
• Quizzes: Automated grading with various question types (MCQ, essays, etc.).
• Forums: Discussion boards for collaboration.
• Wikis & Databases: Collaborative content creation.
• Live Classes: Integration with Zoom, BigBlueButton, etc.
• Gamification: Badges, certificates, and progress tracking.

4. Assessment & Grading

• Automated grading for quizzes.
• Manual feedback on assignments.
• Gradebook to track student performance.

5. Plugins & Customization

• Moodle supports 3rd-party plugins for additional features (e.g., plagiarism detection, attendance tracking).
• Custom themes and branding options.

6. Accessibility & Mobile Learning

• Works on all devices (responsive design).
• Moodle Mobile App allows offline learning.

Where is Moodle Used?

• Schools, Universities, & Corporate Training
• Online Certification Programs
• Non-profits & Government Agencies

Advantages of Moodle

✔ Free & Open-Source (no licensing costs)
✔ Highly customizable with plugins
✔ Supports SCORM & xAPI (e-learning standards)
✔ Strong community support

How to Get Started?

1. Download from Moodle.org
2. Install on a web server (self-hosted) or use MoodleCloud (hosted version).
3. Set up courses and enroll users.

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IBM Watson Studio is a comprehensive data science and AI development platform that enables users to build, train, and deploy machine learning models and AI applications. It offers a suite of tools for data preparation, model development, and collaboration, making it ideal for data scientists, analysts, and developers. Watson Studio supports a wide range of machine learning and deep learning algorithms, and it integrates with IBM Cloud services for scalable computing. Use cases include data cleaning and transformation, building and training models for tasks like classification and regression, developing AI-powered applications such as chatbots, automating machine learning with AutoAI, and deploying models for real-time predictions. Its collaborative features make it well-suited for team-based projects across industries like healthcare, finance, and retail.

What is IBM Watson Studio?

IBM Watson Studio is an integrated development environment designed to facilitate data science, machine learning, and AI model development. It offers a collaborative platform for data scientists, analysts, and business professionals to work together on data preparation, model building, and deployment. IBM Watson Studio integrates various tools and technologies, including open-source frameworks, Jupyter Notebooks, SPSS Modeler, and a range of Watson APIs, making it a comprehensive solution for the AI lifecycle.

As a cloud-based service, IBM Watson Studio streamlines the process of exploring data, training machine learning models, and deploying them into production environments. It provides an environment where users can easily scale their AI workflows, access powerful computational resources, and integrate with other IBM Cloud services.

Top 10 Use Cases of IBM Watson Studio

1. Predictive Maintenance
   • Analyze sensor data to predict equipment failures and schedule maintenance before downtime occurs.
2. Fraud Detection
   • Leverage machine learning models to identify patterns of fraudulent activity in financial transactions or insurance claims.
3. Customer Segmentation
   • Use clustering and classification techniques to group customers based on their behavior and preferences.
4. Supply Chain Optimization
   • Optimize inventory levels, forecast demand, and improve logistics by analyzing historical and real-time data.
5. Healthcare Insights
   • Build models to predict patient outcomes, identify at-risk individuals, and improve treatment recommendations.
6. Natural Language Processing (NLP)
   • Create applications that extract insights from unstructured text data, such as customer feedback or legal documents.
7. Churn Prediction
   • Identify customers at risk of leaving and implement targeted retention strategies.
8. Image Recognition and Analysis
   • Train deep learning models to classify images, detect objects, and analyze visual data for various industries.
9. Energy Consumption Forecasting
   • Analyze historical energy usage data to predict future consumption and optimize energy distribution.
10. Marketing Campaign Optimization
    • Leverage data to segment audiences, predict campaign performance, and allocate resources more effectively.

Features of IBM Watson Studio

• Collaboration Tools: Enables teams to work together on datasets, models, and notebooks in a unified environment.
• Flexible Deployment: Supports multiple deployment options, including cloud, on-premises, and hybrid setups.
• Integration with Watson APIs: Connects easily to Watson services for NLP, speech-to-text, image recognition, and more.
• Open-Source Compatibility: Integrates with popular open-source frameworks and libraries like TensorFlow, PyTorch, and scikit-learn.
• AutoAI: Automates key steps of the AI workflow, from data preparation to model selection and hyperparameter tuning.
• Data Preparation and Refinement: Offers tools for cleaning, transforming, and enriching datasets.
• Scalable Infrastructure: Provides access to IBM’s powerful cloud resources for large-scale training and deployment.

How IBM Watson Studio Works and Architecture

IBM Watson Studio is designed as a modular platform, allowing users to select the components that best fit their workflow. Its core architecture includes:

1. Data Access and Preparation: Connect to various data sources, including databases, cloud storage, and on-premises systems. Use built-in tools to clean, normalize, and transform data.
2. Development Environments: Work with Jupyter Notebooks, RStudio, SPSS Modeler, or the AutoAI graphical interface.
3. Machine Learning and Deep Learning: Build, train, and evaluate models using integrated machine learning libraries and frameworks.
4. Model Management and Deployment: Store models in a centralized repository, track version history, and deploy models as APIs or batch jobs.
5. Integration with IBM Cloud Services: Leverage additional Watson services, data storage solutions, and security features to enhance workflows.

By combining these components, IBM Watson Studio supports the entire AI lifecycle, from data exploration to production deployment.

How to Install IBM Watson Studio

IBM Watson Studio is a cloud-based platform, and it does not require installation in the traditional sense. However, if you’re looking to use it programmatically (e.g., through APIs, SDKs, or from a Python environment), you can interact with Watson Studio using the IBM Cloud SDK or directly through APIs.

1. Create an IBM Cloud Account

Before proceeding, ensure you have an IBM Cloud account. You can create one for free on the IBM Cloud website.

2. Install IBM Cloud CLI

To manage your IBM Cloud services from the command line, install the IBM Cloud CLI (Command Line Interface):

• Go to IBM Cloud CLI Installation and follow the instructions for your operating system.
• Once installed, open your terminal or command prompt and log in to IBM Cloud using: ibmcloud login

3. Install IBM Watson SDK for Python (Optional)

If you want to interact with IBM Watson services programmatically in Python, you can install the Watson SDK for Python. For example, to interact with Watson Studio, you will likely need the ibm-watson Python package for accessing various Watson services.

To install the IBM Watson SDK, use pip:

pip install ibm-watson

You may also want the ibm-cloud-sdk-core package for authentication and more advanced SDK features:

pip install ibm-cloud-sdk-core

4. Interact with IBM Watson Studio via APIs (Using Python SDK)

You can now interact with Watson Studio using the IBM Watson APIs. Below is an example code to interact with Watson Studio services programmatically.

First, set up your credentials (such as your API key and service URL). Then, use the Watson SDK to interact with Watson Studio.

Example (Python code):

from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
from ibm_watson import VisualRecognitionV3

# Set up your IBM Watson credentials
api_key = 'YOUR_API_KEY'
url = 'YOUR_SERVICE_URL'

# Set up the authenticator and service
authenticator = IAMAuthenticator(api_key)
visual_recognition = VisualRecognitionV3(version='2018-03-19', authenticator=authenticator)
visual_recognition.set_service_url(url)

# Example of analyzing an image
with open('example_image.jpg', 'rb') as image_file:
    result = visual_recognition.classify(images_file=image_file).get_result()

print(result)

Replace 'YOUR_API_KEY' and 'YOUR_SERVICE_URL' with the actual credentials from your IBM Cloud Watson service.

5. Access Watson Studio

To access Watson Studio via the API, you typically work with different Watson services (such as Watson Machine Learning, Watson Visual Recognition, and Watson Natural Language Understanding). You will use the corresponding Python SDKs to integrate these services with your Watson Studio workflows.

Basic Tutorials of IBM Watson Studio: Getting Started

To get started with IBM Watson Studio, here are some initial steps:

1. Create a Project:
   • Open Watson Studio, go to the Projects page, and click “Create Project.”
   • Choose “Standard” or “Enterprise” and provide a name for your project.
2. Add Data Assets:
   • Upload a CSV file or connect to a data source.
   • Use the Data Refinery tool to clean and transform your data.
3. Launch a Notebook:
   • Open the Notebooks tab and create a new notebook.
   • Choose a runtime environment, such as Python or R.
4. Build a Simple Model:
   • Use the AutoAI feature to automate model building.
   • Explore different algorithms, compare their performance, and select the best one.
5. Deploy Your Model:
   • After training, save your model to the Model Repository.
   • Deploy it as a REST API and test it with sample inputs.

The post What is IBM Watson Studio and Its Use Cases? appeared first on Artificial Intelligence.

What is DevOps?

DevOps is a software development methodology that combines software development (Dev) and IT operations (Ops) to improve collaboration, efficiency, and quality in the software development lifecycle.

Why We need DevOps?

We need DevOps because it allows organizations to deliver software faster and more reliably. By combining development and operations teams, organizations can automate processes, streamline workflows, and increase the speed of software delivery.

What is the Advantage of DevOps?

The advantages of DevOps include:

1. Increased collaboration and communication: DevOps promotes collaboration and communication between development teams, operations teams, and other stakeholders. This results in better alignment, shared goals, and faster resolution of issues.
2. Continuous delivery and faster time to market: DevOps enables teams to automate various stages of the software delivery process, such as building, testing, and deploying. This automation leads to faster, more frequent and reliable releases, ultimately reducing time to market.
3. Improved quality and stability: Continuous integration, automated testing, and deployment pipelines ensure that software changes are thoroughly tested and validated. This leads to higher quality releases with fewer bugs and reduces the risk of system failures.
4. Increased scalability and efficiency: DevOps practices emphasize scalability and efficiency by leveraging automation, infrastructure-as-code, and containerization. This allows for easier provisioning and scaling of resources to meet changing demands, ultimately optimizing resource utilization.
5. Enhanced customer satisfaction: DevOps focuses on delivering value to customers by rapidly responding to their needs and incorporating their feedback into new releases. This customer-centric approach improves satisfaction and loyalty.
6. Better risk management: DevOps practices encourage monitoring, logging, and continuous feedback loops, which enable teams to identify and resolve issues quickly. This proactive approach minimizes the impact of failures and reduces downtime.
7. Empowered and more motivated teams: DevOps eliminates silos and encourages cross-functional collaboration, empowering team members to contribute to different stages of the software lifecycle. This autonomy and shared responsibility result in higher engagement, motivation, and job satisfaction.

What is the feature of DevOps?

The key features of DevOps include:

• Continuous Integration (CI): DevOps emphasizes the practice of integrating code changes frequently and automatically, allowing teams to detect issues early.
• Continuous Delivery (CD): DevOps promotes the idea of delivering software updates frequently and reliably, ensuring that new features and bug fixes reach users quickly.
• Infrastructure as Code (IaC): DevOps encourages the use of code to manage and provision infrastructure, making it easier to replicate environments and reduce manual efforts.
• Automated Testing: DevOps advocates for the automation of testing processes, enabling teams to identify and fix issues quickly and efficiently.

Top 10 Use Cases of DevOps

1. Continuous Deployment: DevOps enables organizations to continuously deploy code changes to production, reducing the time between development and deployment.
2. Infrastructure Automation: DevOps allows for the automation of infrastructure provisioning and management, making it easier to scale and manage resources.
3. Continuous Monitoring: DevOps promotes continuous monitoring of applications and infrastructure, allowing teams to identify and address issues in real-time.
4. Microservices Architecture: DevOps is well-suited for organizations adopting a microservices architecture, as it enables teams to deploy and manage individual services independently.
5. Agile Development: DevOps aligns well with agile development methodologies, as it encourages collaboration, frequent feedback, and continuous improvement.
6. Release Management: DevOps helps organizations streamline the release management process, allowing for faster and more reliable software releases.
7. Cloud Migration: DevOps can facilitate the migration of applications and infrastructure to the cloud, enabling organizations to take advantage of cloud services and scalability.
8. Security and Compliance: DevOps incorporates security and compliance practices into the development process, ensuring that applications meet the necessary standards.
9. Containerization: DevOps supports the use of containerization technologies like Docker, making it easier to deploy and manage applications across different environments.
10. DevSecOps: DevOps can be extended to include security practices, resulting in DevSecOps, which integrates security into every stage of the software development lifecycle.

How to Implement DevOps?

To implement DevOps, organizations can follow these steps:

1. Assess the current state: Understand the existing development and operations processes, identify pain points, and define objectives for the DevOps implementation.
2. Build a DevOps team: Assemble a cross-functional team with members from development, operations, and other relevant departments to drive the implementation.
3. Define processes and tools: Establish the processes and tools needed for collaboration, automation, and monitoring. This may include version control systems, continuous integration and delivery tools, and infrastructure management platforms.
4. Automate processes: Identify opportunities for automation and implement tools and technologies that can automate tasks such as code deployment, testing, and infrastructure provisioning.
5. Foster collaboration: Encourage communication and collaboration between development and operations teams to break down silos and promote shared responsibility.
6. Measure and improve: Define metrics and key performance indicators (KPIs) to track the success of the DevOps implementation. Continuously monitor and analyze data to identify areas for improvement.

How to Get Certified in DevOps?

There are a number of certifications available for DevOps professionals. Some of the most popular certifications include:

• DevOps Engineer – Certified Professional (DevOps CERT): This certification is offered by the DevOps Institute and is designed for professionals with experience in DevOps. The exam covers a wide range of topics, including continuous integration and continuous delivery (CI/CD), infrastructure as code (IaC), and cloud computing.

• Certified DevOps Engineer – Associate (CDevOps-A): This certification is offered by the Linux Foundation and is designed for professionals with a basic understanding of DevOps. The exam covers topics such as automation, communication, and collaboration.

• Certified DevOps Engineer – Expert (CDevOps-E): This certification is also offered by the Linux Foundation and is designed for professionals with experience in DevOps. The exam covers a more advanced level of topics, such as security and compliance.

• Certified Kubernetes Administrator (CKA): This certification is offered by the Cloud Native Computing Foundation and is designed for professionals who want to manage Kubernetes clusters. The exam covers topics such as cluster administration, networking, and security.

• Certified Kubernetes Application Developer (CKAD): This certification is also offered by the Cloud Native Computing Foundation and is designed for professionals who want to develop applications for Kubernetes. The exam covers topics such as application development, deployment, and monitoring.

The best certification for you will depend on your experience level and career goals. If you are new to DevOps, then the CDevOps-A certification is a good place to start. If you have more experience, then the CDevOps-E certification or the CKA certification may be a better fit.

To become certified, you will need to take and pass an exam. The exams are typically offered online and are proctored. The cost of the exams varies depending on the certification.

To get certified in DevOps, Visit most popular website for providing DevOps certification Courses .

– DevOpsSchool.com
– scmGalaxy.com
– BestDevOps.com

How to Learn DevOps?

There are a number of ways to learn DevOps. Some of the most popular ways include:

• Taking a course: There are a number of online and in-person courses available for DevOps professionals. Example- DevOpsSchool.com , scmGalaxy.com , BestDevOps.com
• Reading books: There are a number of books available on DevOps.
• Attending conferences: There are a number of conferences held each year that focus on DevOps.
• Joining a community: There are a number of online communities for DevOps professionals.
• Getting hands-on experience: The best way to learn DevOps is by getting hands-on experience. This can be done by volunteering for a DevOps project or by working for a company that uses DevOps practices.

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Introduction

Integrated Development Environments (IDEs) are software applications that help programmers write and develop code. IDEs provide a complete environment for coding, debugging, and testing software.

Features of IDEs

IDEs have many features that make programming easier and more efficient. Some of these features include:

Code Editor

IDEs have a code editor that allows programmers to write and edit code. The code editor has features like syntax highlighting, auto-completion, and code formatting that make coding faster and more accurate.

Debugger

IDEs have a debugger that helps programmers find and fix errors in their code. The debugger allows programmers to step through their code line by line and see what is happening at each step.

Compiler

IDEs have a compiler that translates the code written by the programmer into machine code that the computer can understand. The compiler also checks the code for errors and warnings.

Integrated Tools

IDEs have integrated tools that help programmers with tasks like version control, testing, and deployment. These tools make it easier for programmers to manage their code and collaborate with others.

Benefits of IDEs

IDEs have many benefits for programmers. Some of these benefits include:

Increased Productivity

IDEs make programming faster and more efficient by providing features like auto-completion and code formatting.

Better Code Quality

IDEs help programmers find and fix errors in their code, which leads to better code quality.

Collaboration

IDEs have integrated tools that make it easier for programmers to collaborate with others on their code.

Conclusion

Integrated Development Environments (IDEs) are software applications that help programmers write and develop code. IDEs have many features that make programming easier and more efficient, and they have many benefits for programmers. If you are a programmer, using an IDE can help you write better code and be more productive.

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Using FICO Xpress Insight, Python Developers Can Help Business Leaders Make More Informed, Data-Driven Decisions

Highlights:

• The addition of native Python support to FICO® Xpress Insight enables Python’s 8.2 million users to empower business professionals with easy-to-use applications that can execute sophisticated analytic models.
• With FICO® Xpress Insight business users and analysts can work with any advanced analytic model in business terms to perform simulations, compare scenarios and visualize outcomes to make better informed decisions.
• Python developers can now build and operationalize Python based models all within a single framework, reducing time to deployment by orders of magnitude.

FICO, a global analytics leader, today announced it has added native Python support to FICO® Xpress Insight. Xpress Insight enables data scientists to quickly build and deploy any advanced analytic or optimization model as a powerful business application. 

Across industries, data scientists create powerful models to solve complex business problems. Yet, according to most industry analysts, more than half of data science projects are never fully deployed. FICO Xpress Insight helps translate between the data scientist and the line of business user by taking highly complex analytic or optimization models and turning them into simple point and click applications that help them make real business decisions.

“It’s a huge waste of time and resources when the models don’t reach the intended business users,” said Bill Waid, vice president and general manager of FICO® Decision Management Suite. “The real value of highly complex analytic models comes when they’re in the hands of the business users and become an integral part of their decision making process.”

The newest update to Xpress Insight enables the 8.2 million developers that use the popular coding language Python to rapidly build business applications that can execute sophisticated analytic models. Getting analytic applications into the hands of business users allows business leaders to make more informed decisions, perform simulations, compare scenarios and visualize outcomes.

“Python is one of the most productive tools for our digital and data science initiatives. Having Python supported in Xpress Insight helps us smoothly build end-to-end applications from data preparation, to modeling, to visualization,” said Sitao Zhang, data scientist, Supply Chain Digital and Data Science, Johnson and Johnson. “This new feature has not only made our applications more coherent during programming and development, but it also helps us deliver a more user-friendly experience to the business professionals using the applications to drive results.”

FICO® Xpress Insight is part of the FICO® Platform, a decisioning foundation critical for enterprises’ digital transformation. The platform is designed to eliminate data siloes and enable interoperability between enterprise applications. By connecting data-derived insights from disparate business units, enterprises can respond quickly to customers’ immediate needs and anticipate their future demands, resulting in deeper, more engaging customer experiences.

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BOSTON–(BUSINESS WIRE)–AtScale, the leading provider of intelligent data virtualization, today announced a partnership with DataRobot, the leading enterprise AI platform, to deliver a turnkey approach to predictive and descriptive data analysis. The partnership builds a bridge between data science and business intelligence to incorporate and share the same KPIs used for predictive and prescriptive business decisions.

“We are thrilled to partner with DataRobot to accelerate our go-to-market within the data science community. DataRobot’s vision for maximizing business value via AI is unparalleled, and the combination of our capabilities is another step toward democratization of predictive and prescriptive analytics,” said Christopher Lynch, executive chairman and CEO, AtScale.

Organizations globally are moving AI predictions into day-to-day business operations. The combination of DataRobot and AtScale provides a consumable enterprise metrics hub with consistent, governed KPIs and a simplified interface for automated feature creation utilizing DataRobot’s Feature Importance and Impact functionality. AtScale uniquely makes DataRobot predictions consumable to business intelligence and reporting workstreams, providing both technical and non-technical stakeholders access to predictions using the tool of their choice.

“Our global market presence and diverse customer base continue to highlight the need for greater unity between the data science and business intelligence communities,” said Jeremy Achin, co-founder and CEO, DataRobot. “AtScale’s intelligent data virtualization adds a new business constituency in the enterprise who can incorporate AI predictions for operational decision making.”

About AtScale
AtScale powers the analysis used by the Global 2000 to make million dollar business decisions. The company’s Intelligent Data Virtualization platform provides organizations the opportunity to enhance or create a self service data culture by leveraging its business-friendly semantic layer, intelligent data engineering, easy-to-use web-based design interface, multi-cloud support, and strong data source and client integration.

Learn more about why AtScale is a leader for enterprises to deliver fast, accurate data-driven business intelligence and machine learning analysis at scale in GigaOm’s Data Virtualization Radar Report. For more information, visit www.atscale.com, and connect with us on Twitter, and LinkedIn.

About DataRobot
DataRobot is the leader in enterprise AI, delivering trusted AI technology and enablement services to global enterprises competing in today’s Intelligence Revolution. DataRobot’s enterprise AI platform democratizes data science with end-to-end automation for building, deploying, and managing machine learning models. This platform maximizes business value by delivering AI at scale and continuously optimizing performance over time. The company’s proven combination of cutting-edge software and world-class AI implementation, training, and support services, empowers any organization – regardless of size, industry, or resources – to drive better business outcomes with AI.

DataRobot has offices across the globe and funding from some of the world’s best investing firms including Alliance Bernstein, Altimeter, B Capital Group, Cisco, Citi Ventures, ClearBridge, DFJ Growth, Geodesic Capital, Glynn Capital, Intel Capital, Meritech, NEA, Salesforce Ventures, Sands Capital, Sapphire Ventures, Silver Lake Waterman, Snowflake Ventures, Tiger Global, T. Rowe Price, and World Innovation Lab. DataRobot was named to the Forbes 2020 Cloud 100 list and the Forbes 2019 and 2020 Most Promising AI Companies lists, and was named a Leader in the IDC MarketScape: Worldwide Advanced Machine Learning Software Platforms Vendor Assessment. For more information visit www.datarobot.com, and join the conversation on the DataRobot Community, More Intelligent Tomorrow podcast, Twitter, and LinkedIn.

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Harbr, a London startup that helps organizations like Moody’s Analytics to create their own custom data exchanges, yesterday announced that it has completed a Series A round of financing, netting $38.5 million for the growing concern.

Gartner predicts that enterprise adoption of data exchanges will increase by 40% over the next two years. According to Harbr, that mirrors the type of growth that it’s seen since coming out of stealth back in May.

Harbr develops a platform that others can use to create their own private data exchanges, which in turn they can use to share data with their own customers and partners, and to foster collaboration. A core element to the process, the company says, is “convert[ing] data into products that are easy to find, use, share, and manage.”

The platform supports data in any format, including structured and unstructured data. It helps customers convert data, models, document and “even code” into “ready-to-use, monetizable products.” Customers retain control over who can access and consume the data products they share on Harbr, and the company also provides ways to help price the data products and charge for consumption.

Harbr’s data exchange supports “spaces” that come pre-loaded with support for common data science tools, such as Anaconda, Zeppelin, Spark, and Hadoop. A range of data endpoints for inputting data from S3, Google Cloud, and Azure Blob store, are also configured out of the box.

One early Harbr user is Moody’s Analytics, a subsidiary of the Moody’s ratings company that provides access to economic research tools. Michael Salk, managing director of content distribution at Moody’s Analytics, says Harbr “allows us to quickly and easily create and customize data products to streamline access and consumption for both customers and our internal users.”

While the Internet has removed limitations for data sharing, Harbr maintains there is immense potential to grow data exchanges. Currently, the cloud providers dominate the space, with last November’s launch of AWS Data Exchange, as well as Azure Data Share (which launched in July 2019), and Snowflake Marketplace (which launched a month earlier). Google so far has failed to impress with its limited data exchange, according to an Eckerson Group white paper on the topic.

Despite the entries of big tech firms, there is still room for smaller players to differentiate and offer value, according to Gary Butler, CEO and co-founder of Harbr.

“Despite significant investments in data-focused technologies and teams over the last decade, most enterprises are still unable to deliver targeted outcomes from data in a timely and scalable manner,” Butler says in a press release. “The secure data sharing and collaboration capabilities of Harbr’s enterprise data exchange platform fast-track those outcomes.”

The Series A investment was co-led by investors Dawn Capital and Tiger Global Management, which are new investors in Harbr. This brings the company’s total funding to $52 million.

According to Dawn Capital partner Evgenia Plotnikova, is Harbr is among “the vanguard” of companies helping to lead the way in data exchanges.

“For data to become truly powerful, we need more automation and collaboration. Today, human efforts are consumed by finding and preparing data, rather than focused on high-value activities that drive real productivity gains,” Plotnikova said in a press release. “Customers we’ve spoken to find Harbr’s enterprise data exchange transformative, and their engagement across Fortune 1000 companies substantiates this.”

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As the world struggles to achieve the UN’s Sustainable Development Goals (SDGs), the need for reliable data to track our progress is more important than ever. Government, civil society, and private sector organizations all play a role in producing, sharing, and using this data, but their information-gathering and -analysis efforts have been able to shed light on only 68 percent of the SDG indicators so far, according to a 2019 UN study.

To help fill the gap, the data science for social good (DSSG) movement has for years been making datasets about important social issues—such as health care infrastructure, school enrollment, air quality, and business registrations—available to trusted organizations or the public. Large tech companies such as Facebook, Google, Amazon, and others have recently begun to embrace the DSSG movement. Spurred on by advances in the field, the Development Data Partnership, the World Economic Forum’s 2030Vision consortium, and Data Collaboratives, they’re offering information about social media users’ mobility during COVID-19, cloud computing infrastructure to help nonprofits analyze large datasets, and other important tools and services.

But sharing data resources doesn’t mean they’ll be used effectively, if at all, to advance social impact. High-impact results require recipients of data assistance to inhabit a robust, holistic data ecosystem that includes assets like policies for safely handling data and the skills to analyze it. As tech firms become increasingly involved with using data and data science to help achieve the SDGs, it’s important that they understand the possibilities and limitations of the nonprofits and other civil society organizations they’re working with. Without a firm grasp on the data ecosystems of their partners, all the technical wizardry in the world may be for naught.

Companies must ask questions such as: What incentives or disincentives are in place for nonprofits to experiment with data science in their work? What gaps remain between what nonprofits or data scientists need and the resources funders provide? What skills must be developed? To help find answers, TechChange, an organization dedicated to using technology for social good, partnered with Project17, Facebook’s partnerships-led initiative to accelerate progress on the SDGs. Over the past six months, the team led interviews with top figures in the DSSG community from industry, academia, and the public sector. The 14 experts shared numerous insights into using data and data science to advance social good and the SDGs. Four takeaways emerged from our conversations and research:

1. Support the hidden work that makes effective data science possible.

Data scientists spend nearly half their time making sure their data is reliable, clean, and well organized. But too often, social sector organizations don’t have the resources to invest in essential steps like standardizing or digitizing data, which are critical to ensuring the quality of their data science projects.

Their struggles are understandable. Depending on the institution, preparing the “right data” for the task at hand could range from digitizing paper records to synchronizing knowledge management systems across different teams. This behind-the-scenes work can also involve the difficult task of developing and using standards for interoperability and data sharing. Some organizations are trying to make this easier. Carl Elmstam and Roderick Besseling, digital development experts from the Swedish development agency Sida and the Dutch Foreign Ministry, referenced the International Aid Transparency Initiative (IATI) to create a global standard for publishing data about humanitarian funding and projects. Such standards are important for coordinating complex, highly collaborative projects. For example, effective global research on COVID-19 requires harmonizing case data related to the disease.

The experts we spoke with said that tech companies can help civil society organizations struggling with this initial stage of a big data project by:

• Elevating awareness about the importance of organizing and standardizing data.
• Requiring funded groups to adhere to standards like those put forth by the IATI.
• Offering financial resources for social sector organizations to do this behind-the-scenes work.

2. Help partners build their data culture and digital transformation strategies.

A digital transformation strategy is an action plan to help an organization improve how it leverages digital tools and techniques, including data, to advance its goals more efficiently and effectively. By taking a big-picture view across the often deeply entrenched silos within social sector organizations, these strategies can help put the people, priorities, and incentives in place to cultivate a data culture of people who responsibly and proactively engage with data to solve problems. The global pandemic has underscored the importance of such strategies: Organizations have needed to adapt to working digitally and remotely, while nonprofits facing existential financial challenges have been forced to use data to invest limited resources where they’re most needed.

However, only 23 percent of nonprofits have a long-term vision for technology, according to a recent survey by Salesforce, and experts we spoke with shared similar observations.

“Organizations are trying to deal first with technology in itself,” said Catalina Escobar, a co-founder of the Colombian social change group MAKAIA. “They’re thinking about starting a digital transformation strategy.”

Some organizations are more advanced. The global NGO CARE developed a Responsible Data Maturity Model to help institutions craft a coherent, long-term data strategy. Partnerships such as the Digital Impact Alliance (DIAL) and New York University’s Open Data Policy Lab help coordinate successful digital and data-transformation strategies in the global development sector through frameworks like the principles for digital development.

If tech companies want their data or computing power to achieve their full potential in the DSSG movement, the experts we interviewed recommended:

• Working with partners that already have a digital transformation strategy.
• Helping organizations to design digital transformation strategies that will enable the meaningful use of data.
• Funding or contributing to efforts, such as those of CARE or DIAL, to support digital transformation at scale.

3. Provide skilled data scientists to work directly with nonprofits.

Tech companies often provide data or infrastructure for nonprofits, but too few nonprofits have the in-house experts (such as data scientists or data product managers) to make use of those digital assets. To fill this gap, some organizations, such as Zindi, put together competitions for online communities of data scientists to solve social problems. And initiatives such as Data Science Africa or Google’s AI Impact Challenge provide funding, training, and data science talent to nonprofits.

Although blueprints exist for tech companies to boost nonprofits’ data science chops, the experts we interviewed had mixed feelings about the success and sustainability of such efforts. The problem is that they can push nonprofits toward depending on tech firms for specific projects for limited periods, rather than building long-term independent capabilities.

“Anything that temporarily increases capacity at a nonprofit is not leading to more data-driven work,” said Claudia Juech, founding CEO and a board member of the Cloudera Foundation. “Once these people pull out, it will be very hard for nonprofits to keep it going and make it a part of their work.”

Some people argue we should experiment with more direct methods to strengthen in-house data capacity in the social sector. But Groups like DataKind have found success with a model they’ve developed for embedding data specialists into nonprofits to solve problems together. Their approach involves two important components: comprehensively understanding a nonprofit’s data problems and, secondly, training data scientists to appreciate the complexities, constraints, and incentives of working effectively inside nonprofits. DataKind’s senior director of product, Caitlin Augustin, and Afua Bruce, the organization’s chief program officer, said it is important to recruit the right people for such projects—they need to be highly skilled but humble, and able to communicate well with non-experts.

Nick Hamlin, a data scientist at Global Giving, agrees with the approach, further suggesting that—given many nonprofits’ struggles with limited resources—the data scientists chosen for such projects possess a “willingness to exist in the presence of uncertainty.”

To help civil society organizations’ address skill gaps in data science and to ensure tech firms’ digital contributions are used to their full potential, our experts suggested:

• Training data scientists, at scale and in a coordinated fashion across companies, on the work cultures, structures, and constraints of nonprofits.
• Continuing the funding of proven models to lend data science talent to nonprofits in longer-term engagements.
• For shorter-term engagements, deploying data scientists who are good communicators and trainers so they leave nonprofit teams with the skills to do the work on their own.

4. Support training for nonprofits to make responsible and ethical decisions based on data.

Social sector leaders need to be mindful of the potential biases that can seep into the collection, analysis, and presentation of data, or they risk taking inappropriate actions based on the information.

There are many educational and training resources tech companies can use to train their own teams and nonprofits in the critical thinking skills that ensure data projects are ethically executed. For example, the Johns Hopkins Center for Government Excellence and New York University’s GovLab provide training on interpreting data or understanding the risks when sharing data. TTC Labs, a project initiated and supported by Facebook, helps people across industries explore how to manage trust and transparency as they work with digital products and data. The global development NGO IREX helps leaders build soft skills for data-informed decision-making , while TechChange’s “Gender Data 101” course helps people be mindful of gender biases when working with data for social impact. To further bolster the creation and sharing of resources like these, our interviewees suggested that tech firms:

• Invest in training and educating their own employees on ethics and decision-making related to data.
• Sponsor training for nonprofits to build critical thinking, interpretation, and decision-making skills.
• Partner with non-traditional learning institutions that build critical thinking skills for using data ethically, and support similar efforts within the formal education system.

Avoid Going It Alone

As tech companies release datasets, donate cloud computing resources, or lend data scientists to the social sector, the experts we spoke with emphasized the importance of couching the efforts within existing systems, strategies, and knowledge. Coordination among all participants in the DSSG ecosystem—government, civil society, and private sector—will be critical, and numerous networks are already set up to do just that: 2030Vision, the Global Partnership for Sustainable Development Data, Data Collaboratives, the Data Culture Project, and others. Working through cross-industry cooperatives like these will help avoid redundant and siloed investments—and ensure we move more quickly toward achieving the SDGs as their deadlines loom.

The post How Tech Companies Can Advance Data Science for Social Good appeared first on Artificial Intelligence.

There are a growing number of data collaboration solution providers competing to solve the IT management challenges of enterprise data fragmentation. This is the problem where siloed teams all want to work on large data sets, including data from other teams, and so copies of the data are made and shared, resulting in increased costs of storing and managing multiple copies of the same data in an enterprise.

Enter data collaboration, a relatively new approach that is shaking up big data management and is at the cutting edge of data-driven decision making. Data collaboration allows multiple parties to share access to their data, which gives all parties a more holistic view of the project and enables deeper insights. Not only do teams reap the benefits of having immediate access to a wider array of data, IT departments benefit from reduced data fragmentation and lower management costs while simplifying the deployment architecture.

These platforms also offer significant value-added features, such as rapid integration and development, as well as advanced data visualization. At the same time, they eliminate the need to distribute and control additional copies of data. Data collaboration does away with the necessity of developing APIs in applications or planning complex solutions to make the data fully accessible — all of which can take months before teams can even get their projects started. Instead, users can directly leverage the platform tools to build solutions rapidly and turn around a digital service without the need for skilled software developers to write extensive code. Think of it as what CMS products did for web publishing, or what cell phone cameras have done for content creation. It produces instant results by removing all the heavy lifting.

While the benefits are apparent for enterprises, what about organizations or researchers that want to collaborate with third parties or partners outside of their controlled networks? With data collaboration, the biggest boon to organizations is the added security features for controlling access to the data. Since access is provided through the platform itself, the granularity of access can go down to the data cell level. This allows owners to decide which data are shared to maximize collaboration, while ensuring conformity to regulatory and security requirements. There is no need to worry about what a third party will do with personal data: the owner is always in control and no copies are ever distributed.

In business, data collaboration is the key for achieving the next level of strategic decision making. Imagine you want to expand your department store offerings to increase revenue. You already have your own data on what customers are buying in your store, but what are they buying that you don’t already sell? With data collaboration, you could partner with a payment system provider and find out what customers spend their money on. Critically, not only would you know what your customers are buying from your store versus elsewhere, but you could also see what purchases they are making on the same day. That additional data from a collaborator could tell you if you should open a grocery section and whether it can effectively compete by saving your customers an extra trip. The advantages that data collaboration offer to businesses is obvious.

Magical unicorn?

By now, data collaboration may start to sound like a magical unicorn. How is this practical or even possible? How can you achieve insight when you don’t possess the data? The reality is that with large data sets, you don’t gain insight by looking at the actual data. There’s just too much of it. Instead, you formulate queries around the questions that need to be asked. It’s the query results that will provide the insightful answers. This is already a common practice with data that you do own, so imagine the possibilities of combining data across markets and industries without the cost and expertise barriers of implementing traditional data sharing, integration and security controls.

All this added insight can be achieved without having to know who the individuals are and what each person is purchasing. As a collaborator, you can ensure that personally identifiable information is not exposed, and your customers’ privacy is maintained, because it is the queries that produce insights. These queries can be shared with other collaborators for mutual benefit. As an IT leader, you can reduce your complexity and costs for information systems and data storage. As a program manager, you can accelerate outcomes by rapidly prototyping and productizing solutions. It’s a revolution for big data with the potential to transform how we make use and make sense of data without compromising on security and privacy.

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