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Artificial Intelligence in - Engineering

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Objectives

The course emphasizes the end-to-end development and deployment of AI systems, covering ML, deep learning, LLMs, and prompt engineering. It focuses on building scalable, integrated AI pipelines with attention to responsible use, explainability, and operational efficiency in real-world scenarios. The program additionally addresses optimization and monitoring strategies that ensure AI solutions remain robust, accurate, and aligned with organizational objectives.



Course Outcome

Build robust machine learning models from foundational principles through advanced techniques.
Harness deep learning and transformer architectures for generative AI applications. Design and optimize prompt engineering strategies for large language models.
Utilize industry-standard tools including scikit-learn, TensorFlow, Hugging Face, and OpenAI APIs effectively.
Design and optimize prompt engineering strategies for large language models.
Deploy scalable AI models with modern DevOps and MLOps best practices.
Navigate the dual-use nature of generative AI for both attack and defense strategies.
Deliver comprehensive, production-ready AI pipelines integrating real-world data and applications.

Why Mazenet?


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    Expert Faculty

    Our Faculty comprises of 300+ SMEs with many years of experience. All our trainers possess a minimum of 8+ years of experience.

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    Proven Track Record

    We have served over 200+ global corporate clients, consistently maintaining a 99% success rate in meeting training objectives for 300+ technologies with quick turnaround time.

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    Blended Learning

    We provide course content over any platform that our clients prefer. You can choose an exclusive platform or a combination of ILT, VILT, and DLP.

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    Learning Paths

    The learning paths are very defined with clear benchmarks. Quantitative assessments at regular intervals measure the success of the learning program.

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    Case Study

    We have amassed over 10,000 case studies to support training delivery. Candidates will be trained to work on any real-time business vertical immediately after the training.

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    24*7 Global Availability

    We are equipped to conduct training on any day, date or time. We have delivered training pan India, Singapore, North America, Hong Kong, Egypt and Australia.

Delivery Highlights

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    Customized Training Modules

    Training programs are highly flexible with module customizations to suit the requirements of the business units.

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    Certification

    The training can be supplemented with appropriate certifications that are recognized across the industry.

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    Multi-language Support

    Course content can be delivered in English, Spanish, Japanese, Korean or any other language upon request.

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    Personalized Training Reports

    Candidates are assessed individually at regular intervals and are provided unique learning suggestions to suit their learning calibre.

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    Industry-Oriented Training

    Industry-oriented training, completing which, candidates can be immediately deployed for billable projects.

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    Diverse Training Platforms

    Choose from Instructor-Led Training, Virtual Instructor-Led Training, Digital Learning Platform and Blended Training platforms

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Course Preview

  • Study supervised, unsupervised, and reinforcement learning techniques.
  • Master data preprocessing, feature engineering, and model evaluation.
  • Learn to avoid overfitting and underfitting using best practices like cross-validation.

  • Explore neural networks including CNNs and RNNs.
  • Understand transformer architectures and attention mechanisms.
  • Work with large language models such as GPT, BERT, and LLaMA using Hugging Face.
  • Fine-tune pre-trained generative models for tasks like summarization and image generation.

  • Learn prompt design strategies including zero-shot, few-shot, and chain-of-thought techniques.
  • Understand instruction tuning and prompt optimization.
  • Explore the impact of temperature and token settings on model outputs.
  • Practice with OpenAI API and LangChain.

  • Deploy AI models using frameworks like FastAPI and Flask.
  • Containerize applications with Docker.
  • Scale models on cloud platforms such as AWS Sagemaker and Azure ML.
  • Implement logging, monitoring, and CI/CD for ML workflows.

  • Examine fairness, transparency, and explainability in AI models.
  • Identify and mitigate bias in data and algorithms.
  • Address data privacy and governance requirements including GDPR.
  • Adopt responsible practices for using generative AI and large language models.

  • Build and deploy ML models for structured data.
  • Fine-tune sentiment analysis models with Hugging Face.
  • Develop AI assistants using prompt engineering and OpenAI API.
  • Integrate AI features into full-stack application. ḍ