Artificial Intelligence Implementation Boot Camp


This artificial intelligence course prepares you to strategically contribute to the adoption of machine learning and AI features in your own projects and applications


Artificial Intelligence Implementation Boot Camp
Duration: 1 day
This artificial intelligence course prepares you to strategically contribute to the adoption of machine learning and AI features in your own projects and applications
Learn to separate reality from myth, and filter real-world applications from business media buzz. This class is a fast-paced, intensive literacy class that leaves you quickly equipped with a broad range of management tools to incorporate machine intelligence into your own business strategy. “AI” is a buzzword, but the actual technology behind machine learning and other machine intelligence services is very real. Although there is broad consensus among major management analysts that AI and machine learning are immediate disruptors to most technology services, there is still very little practical adoption when it comes to integrating these features.
This class teaches you how to navigate the machine intelligence landscape and build actual use cases for your own scenarios. You’ll learn what types of teams, roles, platforms, and tools are required for a practical adoption strategy. You’ll learn to profile good candidate projects for AI features and spot business opportunities where AI could be useful. Group exercises allow you to exchange ideas with peers and work together to arrive at your own creative examples. The level of detail covered in this workshop leaves you thoroughly informed about the state of the art in AI and machine learning, and ready to face the future on your own teams.
Part 1: Introduction
1. Working definitions: AI, Machine Learning, Deep Learning, Data Science & Big Data
2. State of AI: summarizing major analysts’ statistics & predictions
3. Summarizing AI misinformation
4. Effects on the job market
5. Today’s AI use cases
o Where it works well
o Where it doesn’t work well
6. What do high profile uses have in common?
7. Addressing legitimate concerns & risks
Part 2: The Big Data Prerequisite
1. Evaluating your big data practice
2. State of tools – understanding intelligent big data stacks
o Visualization and Analytics
o Computing
o Storage
o Distribution and Data Warehousing
3. Strategically restructuring enterprise data architecture for AI
4. Unifying data engineering practices
5. Datasets as learning data
6. Defeating Bias in your Datasets
7. Optimizing Information Analysis
8. Utilizing the IoT to amass a large amount of data
Part 3: Implementing Machine Learning
1. Examine pillars of a practicing AI team
o Business case
o Domain expertise
o Data science
o Algorithms
o Application integration
2. Bettering Machine Learning Model Management
3. State of tools – understanding intelligent machine learning stacks
4. Machine Learning Methods and Algorithms
o Decision Trees
o Support Vector Machines
o Regression
o Naïve Bayes Classification
o Hidden Markov Models
o Random Forest
o Recurrent Neural Networks
o Convolutional Neural Networks
5. Developing Validation Sets
6. Developing Training Sets
7. Accelerating Training
8. Encoding Domain Expertise in Machine Learning
9. Automating Data Science
10. Deep Learning
Part 4: Creating Concrete Value
1. Opportunities for automation
2. Understanding automation vs. job displacement vs. job creation
3. Finding hidden opportunities through improved forecasting
4. Production and operations
5. Adding AI to the Supply Chain
6. Marketing and Sales Applications
o Predict Customer Behavior
o Target Customers Efficiently
o Manage Leads
o AI-powered content creation
7. Enhancing UX and UI
8. Next-Generation Workforce Management
9. Explaining Results
Part 5: Machine intelligence as part of the customer experience
1. IoT and the role of machine learning
2. Projects based on customer & user needs
3. Handling customer inquiries with AI
4. Creating empathy-driven customer facing actions
5. Narrowing down intent
6. AI as part of your channel strategy
Part 6: Machine Intelligence & Cybersecurity
1. How can ML help with security?
o Advance cyber security analytics
o Developing defensive strategies
o Automating repetitive security tasks
o Close zero-day vulnerabilities
2. How are attackers leveraging ML and AI?
3. Building up trust towards automated security decisions and actions
4. Automated application monitoring as a security layer
5. Identifying Vulnerabilities
6. Automating Red Team/Blue Team Testing Scenarios
7. Modeling AI after previous security breaches
8. Automating and streamlining Incident Responses
9. How use deep learning AI to detect and prevent malware and APTs
10. Using natural language processing
11. Fraud detection
12. Reducing compliance testing & cost
Part 7: Filling the Internal Capability Gap
1. Assessing your technological and business processes
2. Building your AI and machine learning toolchain
3. Hiring the right talent
4. Developing talent
5. How to make AI more accessible to people who are not data scientists
6. Launching pilot projects
Part 8: Conclusion and Charting Your Course
1. Review
2. Charting Your Course
o Establishing a timeline
3. Open Discussion

This artificial intelligence course is for anyone that strategically contributing to the adoption of machine learning and AI features into their projects and applications.
Professionals who may benefit include:
• Anyone in an IT Leadership role
• CIOs / CTOs
• Product Owners and Managers
• Developers and Application Team leads
• Project and Program Managers
• DevOps & Automation Engineers
• Software Managers and Team Leads
• IT Operations Staff

In this class you will learn:
• Differentiate fact from fiction on AI and machine learning topics
• Have intelligent conversations about the state of AI and ML technologies
• Navigate tool and technology stacks associated with AI and ML, and communicate with your engineering team members about requirements, needs, talent, and costs
• Design or manage projects and programs that may incorporate aspects of AI and ML
• Understand the different types of machine learning
• Translate technical constraints and business concerns among different groups of stakeholders who may not understand the context of priorities of other parties
• Build and lead teams who bring together the requisite skill sets needed for effective AI and machine learning implementation