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AI Workflow Engineer specialization training

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Overview

The IBM AI Enterprise Workflow Specialization is a comprehensive training program designed to equip data science practitioners with the skills necessary for building, deploying, and managing AI solutions in large enterprises. This specialization offers a structured approach to mastering the AI workflow process.

Course Structure

The specialization consists of six courses that build upon each other:

  1. AI Workflow: Business Priorities and Data Ingestion
  2. AI Workflow: Data Analysis and Hypothesis Testing
  3. AI Workflow: Feature Engineering and Bias Detection
  4. AI Workflow: Machine Learning, Visual Recognition and NLP
  5. AI Workflow: Enterprise Model Deployment
  6. AI Workflow: AI in Production

Skills and Knowledge

Participants will gain expertise in:

  • MLOps (Machine Learning Operations)
  • Apache Spark
  • Feature Engineering
  • Statistical Analysis and Inference
  • Data Analysis and Hypothesis Testing
  • Applied Machine Learning
  • Predictive Modeling
  • DevOps
  • Deployment of machine learning models using IBM Watson tools on IBM Cloud

Target Audience

This specialization is tailored for experienced data science practitioners seeking to enhance their skills in enterprise AI deployment. It is not suitable for aspiring data scientists without real-world experience.

Course Content and Delivery

Each course includes a mix of videos, readings, assignments, and labs. For instance, the Feature Engineering and Bias Detection course comprises 6 videos, 14 readings, 5 assignments, and 1 ungraded lab, focusing on best practices in feature engineering, class imbalance, dimensionality reduction, and data bias.

Tools and Technologies

The courses utilize:

  • Open-source tools (e.g., Jupyter notebooks, Python libraries)
  • Enterprise-class tools on IBM Cloud (e.g., IBM Watson Studio) Participants should have a basic working knowledge of design thinking and Watson Studio before starting the specialization.

Certification

Upon completion, participants will be prepared to take the official IBM certification examination for the IBM AI Enterprise Workflow V1 Data Science Specialist, administered by Pearson VUE.

Practical Application

The specialization emphasizes practical application with an enterprise focus. Exercises are designed to simulate real-world scenarios, emphasizing the deployment and testing of machine learning models in an enterprise environment. While most exercises can be completed using open-source tools on a personal computer, the specialization is optimized for an enterprise setting that facilitates sharing and collaboration.

Leadership Team

For leadership teams seeking to enhance their understanding and implementation of AI within their organizations, several specialized training programs are highly relevant:

IBM AI Enterprise Workflow Specialization

While primarily designed for data science practitioners, this Coursera-offered specialization can be valuable for leaders who need to understand the technical aspects of AI implementation. It covers:

  • MLOps
  • Feature Engineering
  • Machine Learning
  • Model Deployment
  • AI in Production This program helps leaders grasp the workflow and technical requirements of AI projects, which is crucial for strategic decision-making and oversight.

AI+ Executive™ Certification

Tailored specifically for business leaders and executives, this program focuses on:

  • AI Strategy Development
  • Strategic Decision-Making with AI
  • AI Project Management
  • Ethical AI Implementation This certification is designed to help leaders develop AI strategies, make informed decisions, and drive innovation within their organizations. It does not require technical expertise and emphasizes the business and strategic aspects of AI.

AI Product Management Specialization

Offered by GenAI Works, this program is suitable for professionals across various functions, including product managers, executives, and analysts. It covers:

  • Applying the data science process
  • Industry best practices
  • Designing human-centered AI products
  • Ethical and privacy considerations This specialization is valuable for leaders who need to understand how AI can be applied in different areas of the business and how to lead cross-functional teams on machine learning projects. No programming skills are required. Each of these programs offers unique benefits, but the AI+ Executive™ Certification and the AI Product Management Specialization are more directly aligned with the needs of leadership teams looking to strategize and implement AI within their organizations. These programs focus on the strategic and managerial aspects of AI implementation, making them particularly suitable for executive-level decision-makers.

History

The IBM AI Enterprise Workflow Specialization, designed to train and certify AI Workflow Engineers, has a structured development and implementation history that reflects the evolving needs of enterprises to integrate AI solutions seamlessly into their operations.

Origins and Purpose

Developed by IBM, this specialization aims to prepare existing data science practitioners to build, deploy, and manage AI solutions within large enterprises. It focuses on:

  • Connecting business priorities to technical implementations
  • Integrating machine learning with specialized AI use cases (e.g., visual recognition and NLP)
  • Utilizing Python and IBM Cloud technologies

Course Structure

The specialization consists of six interconnected courses:

  1. AI Workflow: Business Priorities and Data Ingestion
  2. AI Workflow: Data Analysis and Hypothesis Testing
  3. AI Workflow: Feature Engineering and Bias Detection
  4. AI Workflow: Machine Learning, Visual Recognition and NLP
  5. AI Workflow: Enterprise Model Deployment
  6. AI Workflow: AI in Production Each course builds upon the previous one, forming a comprehensive workflow that guides learners through the use of enterprise-class tools on IBM Cloud and open-source tools.

Skills and Tools

The specialization enhances skills in:

  • MLOps
  • Apache Spark
  • Feature Engineering
  • Statistical Analysis
  • Predictive Modeling
  • DevOps Learners gain hands-on experience with IBM Watson tooling and other AI tools, ensuring they can effectively create, deploy, and test machine learning models.

Certification

Upon completion, learners are prepared to take the official IBM certification examination for the IBM AI Enterprise Workflow V1 Data Science Specialist, administered by Pearson VUE.

Prerequisites and Recommendations

  • Basic working knowledge of design thinking and Watson Studio
  • Real-world expertise in building machine learning models
  • Not intended for aspiring data scientists, but for practicing data scientists looking to deepen their skills This structured approach highlights the importance of both technical proficiency and business acumen in AI workflow engineering, reflecting the complex needs of modern enterprises in implementing AI solutions.

Products & Solutions

AI Workflow Engineer specialization training programs offer comprehensive solutions to develop skills in building and deploying AI in enterprise environments. Here are some notable programs: IBM AI Enterprise Workflow Specialization

  • Six-course program on Coursera
  • Covers business priorities, data ingestion, analysis, hypothesis testing, feature engineering, bias detection, machine learning, AI use cases, and enterprise model deployment
  • Prepares for IBM AI Enterprise Workflow V1 Data Science Specialist certification
  • Develops skills in MLOps, Apache Spark, feature engineering, statistical analysis, predictive modeling, and DevOps AI Workflow: Enterprise Model Deployment
  • Part of IBM's specialization
  • Focuses on deploying models in large enterprises
  • Covers Apache Spark for data manipulation, model training, and deployment
  • Teaches best practices for model deployment technologies AI Workflow Integrators by CotranslatorAI
  • Three mastercourses for language professionals
  • Covers AI use cases, prompt engineering, and best practices in translation environments
  • Includes live and on-demand events, course materials, and discussion forums AI Product Management Specialization
  • Three-course series on the data science process, industry best practices, and designing human-centered AI products
  • Suitable for product managers and engineering team leaders These programs provide a solid foundation for professionals seeking to enhance their skills in AI workflow engineering, particularly within enterprise environments.

Core Technology

The IBM AI Enterprise Workflow Specialization emphasizes several core technologies and skills essential for AI Workflow Engineers: 1. Cloud and Development Platforms

  • IBM Cloud and Watson Studio
  • Integration with open-source tools like Jupyter notebooks 2. Data Processing and Analysis
  • Apache Spark for large-scale data processing
  • Python and its libraries (e.g., scikit-learn) for data preparation and analysis 3. Machine Learning and AI
  • Machine Learning Operations (MLOps)
  • Feature engineering and bias detection
  • Visual recognition and Natural Language Processing (NLP) 4. Statistical Analysis
  • Data analysis and hypothesis testing
  • Predictive modeling techniques 5. Enterprise Deployment
  • DevOps practices for AI
  • Model deployment using IBM Watson tooling 6. Methodologies
  • Design thinking principles
  • Hands-on projects mirroring real-world scenarios This comprehensive approach equips AI Workflow Engineers with the technical depth and practical experience needed to excel in enterprise environments. The program balances theoretical knowledge with applied skills, ensuring graduates can effectively build, deploy, and maintain AI solutions at scale.

Industry Peers

AI workflow engineering is a rapidly evolving field with various training programs and industry insights available. Here are some notable options for professionals looking to specialize in this area: 1. IBM AI Enterprise Workflow Specialization

  • Offered on Coursera
  • Six-course program covering end-to-end AI implementation in enterprises
  • Prepares for IBM AI Enterprise Workflow V1 Data Science Specialist certification
  • Focuses on IBM Cloud tools and open-source technologies 2. CertNexus Certified Artificial Intelligence Practitioner (CAIP)
  • Vendor-neutral certification program
  • Covers AI/ML concepts, problem-solving, workflow tasks, and model building
  • Suitable for data science professionals and AI engineers 3. Industry Best Practices and Tools
  • Siemens' Xcelerator software package for AI-driven workflow management
  • Halliburton's DS365.ai cloud solution for the oil & gas industry
  • Integration of AI in engineering workflows using CAE validation and Product Lifecycle Management Key Considerations for AI Workflow Engineers:
  • Understand the importance of setting up repeatable processes
  • Learn to capture and structure historical data effectively
  • Familiarize yourself with industry-specific tools and solutions
  • Stay updated on emerging trends and best practices in AI workflow management By combining formal training programs with an understanding of industry-specific tools and best practices, AI workflow engineers can enhance their skills and contribute effectively to their organizations. Continuous learning and adaptation to new technologies are crucial in this rapidly evolving field.

More Companies

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Investcorp

Investcorp is a global investment manager specializing in alternative investments, founded in 1982 by Nemir A. Kirdar. The firm has grown from its roots in the Gulf to become a major player in the global investment landscape. ### Business Lines Investcorp operates across several key business lines: - Private Equity: Focuses on mid-market investments, particularly in North America, targeting services-oriented companies with strong growth potential. - Real Estate: Invests in global real estate opportunities. - Absolute Return Strategies: Offers investors exposure to a broader array of investment opportunities. - Credit Management: Launched in 2017 as part of its global growth strategy. - Infrastructure: Entered through a joint venture with Aberdeen Standard Investments. - Strategic Capital: Invests in mid-sized alternative investment managers. ### Growth and Global Presence Investcorp has experienced significant growth, with Assets Under Management (AUM) increasing from $10 billion to over $50 billion in the past six years, reaching $53 billion as of June 30, 2024. The firm has expanded its global presence with offices in key locations such as New York City, London, Riyadh, Abu Dhabi, Doha, Singapore, and Mumbai. ### Investment Approach Investcorp is known for its disciplined investment approach, acting as a strategic partner to its portfolio companies. The firm focuses on organic growth, mergers and acquisitions, enhancing team and organization, and improving efficiency and infrastructure. ### ESG and Corporate Responsibility Investcorp places a strong emphasis on Environmental, Social, and Governance (ESG) factors, integrating ESG considerations into its due diligence processes and ongoing investment support. ### Leadership As of the latest information, Mohammed Alardhi serves as the Executive Chairman, leading the firm's global growth strategy and overseeing its continued expansion and commitment to sustainable value creation. Investcorp has established itself as a trusted global alternative asset manager, known for its superior performance, diverse asset classes, and commitment to ESG and corporate responsibility.

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Liquid Death

Liquid Death, founded by Mike Cessario in 2019, is a disruptive canned water company that has rapidly gained popularity in the beverage industry. The brand's unique approach combines edgy marketing, sustainability initiatives, and a focus on younger consumers. ### Founding and Concept Mike Cessario, a former Netflix creative director with roots in the punk and heavy metal scene, conceptualized Liquid Death in 2009. Inspired by musicians drinking water from energy drink cans at music festivals, Cessario aimed to create a water brand as appealing as energy drinks or beer, targeting a younger, rebellious demographic. ### Branding and Marketing Liquid Death's distinctive branding features skull imagery, heavy metal aesthetics, and provocative slogans like "Murder Your Thirst." The company's marketing strategy leverages humor, viral social media content, and collaborations with influencers across platforms such as TikTok, YouTube, and Instagram. ### Products and Expansion The company offers water in 16.9 and 19.2 US fl oz aluminum cans, sourced from Virginia and Idaho. The product line has expanded to include sparkling water, flavored carbonated beverages, and iced teas. Liquid Death is now available in over 100,000 stores worldwide and is a top-selling water brand on Amazon. ### Sustainability and Social Impact Committed to sustainability, Liquid Death uses aluminum cans instead of plastic bottles and partners with organizations like 5 Gyres and Thirst Project. The company's "Sell Your Soul" program engages customers in funding plastic cleanup efforts. ### Financial Growth and Valuation Liquid Death has experienced rapid growth since its launch. Revenue increased from $45 million in 2021 to $130 million in 2022, with projections of $260 million by the end of 2023. In March 2024, the company was valued at $1.4 billion after raising $67 million in funding. An initial public offering (IPO) is scheduled for spring 2024. ### Target Audience and Cultural Impact The brand primarily targets environmentally conscious Gen Z and Millennial consumers who frequent concerts, malls, and sporting events. Liquid Death has successfully partnered with Live Nation and other music festivals to reduce plastic waste and promote water consumption at live events. In summary, Liquid Death has disrupted the beverage industry through its unique branding, engaging marketing strategies, and commitment to sustainability, establishing itself as a significant player in the market and a favorite among younger, environmentally aware consumers.

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Roadzen

Roadzen Inc. (NASDAQ: RDZN) is a global insurtech company revolutionizing the auto insurance industry through advanced artificial intelligence (AI), telematics, and computer vision technologies. Founded in 2015 and headquartered in Burlingame, California, Roadzen has established a global presence with offices in the U.S., India, the U.K., and France. The company's mission is to enhance various aspects of the auto insurance lifecycle, including product development, claims processing, road safety improvement, damage assessment, underwriting, and personalized pricing based on individual driving behaviors. Roadzen's suite of innovative products includes: - Via - xClaim - StrandD - Global Distribution Network - Drivebuddy AI - Good Driving These solutions leverage cutting-edge technologies to provide more efficient, effective, and personalized insurance services. The company serves a diverse client base of 160 enterprise customers and 3,200 small and medium businesses, including leading insurers, carmakers, fleets, dealerships, brokers, car sales platforms, and ridesharing platforms. For the fiscal year ended March 31, 2024, Roadzen achieved record revenues of $46.7 million, representing a 245% increase over the prior year. However, the company reported a net loss of $134.73 million for the same period, reflecting its investment in growth and technology development. Roadzen has gained recognition as one of CNBC's World's Top InsurTech Companies for 2024 in the Underwriting & Risk Analysis category. Publications such as Forbes, Fortune, and Financial Express have also acknowledged the company's innovative work in AI at the intersection of insurance and mobility. Led by CEO and founder Rohan Malhotra, Roadzen employs approximately 380 professionals across its global offices. The company's stock has experienced significant volatility since its listing, with a 52-week high of $7.17 and a low of $0.71, underperforming both the US Software industry and the broader US market over the past year.

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Bitcoin

Bitcoin (BTC) is the world's first decentralized cryptocurrency, created in 2008 by an anonymous individual or group known as Satoshi Nakamoto. Launched in 2009, Bitcoin operates on a peer-to-peer network using blockchain technology, revolutionizing the concept of digital currency. ### Key Components 1. **Blockchain**: A decentralized, public ledger recording all Bitcoin transactions chronologically. 2. **Mining**: The process of verifying transactions and adding new blocks to the blockchain through solving complex mathematical problems. 3. **Private and Public Keys**: Essential for secure transactions, allowing users to send and receive bitcoins. ### How Bitcoin Works - **Transactions**: Users can send and receive bitcoins using wallet addresses. - **Verification**: Miners verify transactions and add them to the blockchain. - **Mining Rewards**: Miners receive newly minted bitcoins and transaction fees for their efforts. ### Economic and Philosophical Aspects - **Decentralization**: Bitcoin operates without central authority, aligning with free-market ideologies. - **Investment and Usage**: Viewed as both a currency and an investment, with notable price volatility. ### Units and Denominations Bitcoin is divisible to eight decimal places, with smaller units including millibitcoin (mBTC) and satoshi (sat). ### Environmental and Social Impact - **Energy Consumption**: Bitcoin mining requires significant electricity, raising environmental concerns. - **Benefits**: Offers cost-efficient transactions, privacy, and potential for greater financial inclusion. In summary, Bitcoin represents a groundbreaking digital currency system, offering a decentralized alternative to traditional financial structures while presenting unique challenges and opportunities in the global economy.