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Overcoming barriers to AI and data adoption
AI and data adoption depends as much on people as on tools. Identify barriers, clarify use cases and adapt support to make change understandable. Develop an approach that encourages adoption and discussion of new practices.
- Duration
- 5 days 35 hours
- Code
- IA015FR Code
Presentation
Adopting artificial intelligence (AI) and harnessing big data are major drivers of organisational transformation. However, the path to innovation often presents obstacles. From technical challenges in handling big data to ethical concerns and organisational resistance, many barriers can slow or prevent successful technology integration.
This intensive 5-day course explores methods and tools for overcoming AI and data adoption barriers. Begin by understanding AI's evolution in the context of big data and data science and identifying the main integration obstacles. Then learn to use advanced technologies such as Spark ML for big data processing and analysis and explore large language models (LLMs) and their practical applications.
Throughout the programme, analyse specific technical, ethical and organisational challenges and discover proven strategies to overcome them. Practical application through concrete case studies and interactive discussions develops the skills to initiate and lead AI and data adoption within your organisation.
Objectives
By the end of this AI and big data course, you will be able to:
- understand AI's evolution through big data and identify the main adoption barriers;
- use Spark ML for big data processing and analysis to overcome technical handling challenges;
- apply Spark ML machine learning techniques to develop effective predictive and classification models in big data contexts and address deployment obstacles;
- explore large language models and analyse their applications in varied contexts;
- identify and analyse ethical and organisational barriers to AI adoption;
- use Spark ML to optimise performance and support large-scale AI model deployment despite technical and infrastructure limitations;
- understand LLM challenges and prospects in big data contexts to encourage adoption and identify specific usage barriers.
Program
Module 1: understanding fundamentals and identifying barriers
- Big data and data science fundamentals and their crucial role in AI's development.
- The historical evolution of AI through big data.
- Identifying and categorising technical, ethical and organisational barriers to AI adoption.
Module 2: using Spark ML for processing and analysis
- The role of big data in accelerating AI advances.
- Analysing technical, ethical and organisational obstacles.
- Using Spark ML for data processing and analysis.
Module 3: developing and optimising AI models with Spark ML
- Using Spark ML for machine learning in big data contexts.
- Implementing predictive models and classification algorithms with Spark ML.
Case study:
- Analysing practical cases from the IT industry.
Module 4: deploying AI models and exploring practical cases
- Methodologies for large-scale AI model deployment.
Case study:
- Analysing practical examples of overcoming AI limitations with Spark ML.
Module 5: exploring LLMs and adoption strategies
- Introduction to large language models such as GPT and BERT.
- Analysing LLM applications in text understanding and generation.
Case study:
- Analysing LLM challenges and prospects in big data contexts.
Audience
This course is intended for:
- strategic decision-makers and business leaders who want to understand AI and data adoption challenges, guide strategy and identify ways to overcome organisational and strategic barriers;
- digital transformation and IT leaders responsible for integrating AI and data technologies into IT infrastructure and seeking solutions to big data and AI technical and architectural challenges;
- data science and AI specialists, including data scientists and AI engineers, who want deeper advanced technology skills and solutions to technical and performance barriers in AI development and deployment;
- anyone involved in digital transformation and AI adoption seeking a comprehensive understanding of barriers to contribute effectively to integration within projects and organisations.
Prerequisites
The following prerequisites apply:
- Basic AI and big data knowledge: understanding of AI fundamentals, including learning types and basic algorithms, and big data concepts such as the 3 Vs and distributed processing.
- Programming and data handling experience: although not mandatory, familiarity with a language such as Python or Scala and data manipulation techniques supports understanding and application, particularly for Spark ML.
- Basic mathematical and statistical concepts: understanding probability, descriptive statistics and linear algebra helps with the machine learning concepts covered through Spark ML.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Quiz / multiple-choice questions
- Case study
Course highlights
- In-depth AI and big data expertise: trainers with strong expertise in AI, big data processing and advanced technologies such as Spark ML and LLMs.
- Comprehensive applied training: from Spark ML data processing to LLM exploration, the programme covers essentials for overcoming barriers to AI and data use.
- Spark ML practice and LLM analysis: exercises and case studies focus on applying machine learning techniques and analysing LLM use cases.
- Adoption strategies: proven methods and interactive discussion to identify and overcome technical, ethical and organisational integration barriers.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.