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Data is the fuel that powers AI

Learn critical data engineering skills including data understanding, data preparation, data analysis, and code quality. Use Python and appropriate libraries to conduct exploratory data analysis and database tools such as SQL to store data, combine data, manipulate data, and query data. In addition, gain critical skills in how to prepare data for model ingestion and machine learning, and understand why AI lives and breathes on data!

• Data analysis with Python and relevant libraries (e.g., NumPy, Pandas)

• Database languages including the usage of SQL for structured data

• Inferential statistics needed for data profiling and analysis

• Data engineering and big data processing and how that’s different than routine data processing

• Introduction to cloud computing for data analysis and preparation

• Professional data science tools and setup including Git, Jupyter Notebooks, and more

Artificial intelligence enables transformative business outcomes

Learn about machine learning, including different types of algorithms and their respective applications. Gain hands-on experience with linear and logistical regression, as well as clustering to process data and build AI solutions to solve business problems. Dive into advanced topics such as neural networks and deep learning. Immerse yourself in the emerging field of GenAI by learning about foundation and large language models (LLMs) trained on vast amounts of data. See how organizations are harnessing GenAI for groundbreaking AI transformations across industries.

Examples of AI and GenAI skills you will learn:

• Supervised and unsupervised machine learning (ML) techniques, as well as ML models and methods such as neural networks and deep learning

• Critical AI techniques such as dimensionality reduction and transfer learning

• The mechanics of AI infrastructure, cloud and data platforms, foundation models, and LLMs to enable the creation of GenAI applications

• Key differences between model training, fine-tuning, retrieval-augmented generation (RAG), and prompt engineering

• Applications and high-value use cases of GenAI for text, image, code, video, and audio generation

• A framework for Trustworthy Generative AI to mitigate risks and ensure GenAI solutions are secure and well-governed

Learn how AI is used across all major industries

Deloitte services nearly 90% of the Global Fortune 500, and we have delivered thousands of AI engagements across every industry to help our clients solve their most pressing data and AI challenges. Participants in the Deloitte AI Academy will gain first-hand exposure to use cases specific to each industry and how AI solutions can be implemented. Learn the headwinds and tailwinds driving AI adoption, and hear from our account teams and professionals who are on the frontlines of deploying AI solutions daily.

Examples of industry use cases you will experience:

• Consumer

• Energy, resources & industrials

• Financial services

• Government & public services

• Life sciences & health care

• Technology, media & telecommunications

Learn how AI is driving organizational business impact

AI is driving change across every organization in unimaginable ways. From marketing to cybersecurity to human resources, experience how AI can be infused into everything across an organization. The Deloitte AI Academy helps participants bridge the technical skills with business applications across domains.

Examples of domain-specific use cases you will experience:

• Audit and assurance

• Autonomous mobility

• Customer service and marketing

• Cybersecurity

• Enterprise resources planning

• Fraud, waste, and abuse

• GenAI in insurance

• Health care

• Supply chain & manufacturing

• Tax

• Unlimited Reality

• And many more

Trust at the center

We put trust at the center of everything that we do. Deloitte uses Trustworthy AI^TM, our proprietary multidimensional AI framework to help organizations develop ethical safeguards and implement AI solutions that do as intended. While GenAI is new in many ways, it accelerates and amplifies risks that have always been a factor in AI development and deployment. The concepts and tools that enable Trustworthy AI still apply, even as many of the emerging risks and problematic scenarios are more nuanced. As more companies adopt AI, leaders grapple with ethical decisions about its design and use. Help clients bridge the ethics gap by gaining real-world experience in using the framework and considering AI solutions across a comprehensive set of factors. Learn what it takes to build safeguards needed to manage AI risk and articulate trust in AI and GenAI solutions.

The Trustworthy AI/GenAI framework includes six primary facets:

1. Fair and impartial use checks to identify biases within algorithms/data, designing controls for unexpected outcomes.

2. Transparent and explainable AI practices so participants understand how their data is being used.

3. Responsibility and accountability policies to clearly determine who is responsible for the output of AI system decisions.

4. Robust reliability monitoring so AI systems can learn from humans/other systems and produce consistent outputs.

5. Privacy safeguards for data to ensure that consumer privacy is respected and data isn’t leveraged beyond intended use.

6. Safe and secure cybersecurity protocols to protect AI systems from potential risks that may cause physical and digital harm.

Find growth in areas such as team building and project management to manage AI services

Learn leading practices for working in a professional services environment, such as:

• Building and fostering professional relationships with clients, project teams, and internal leaders.

• Developing presentation skills in a manner consistent with a technical leadership audience (e.g., executive presence and polish).

• Self-marketing one’s brand as an AI practitioner and (how to) pursue a career in AI.

• Managing the lifecycle of an AI project, from ideation through execution, using iterative methodologies.