Treinamentos

Managing Machine Learning Projects with Google Cloud

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Modalidade:

Online, Presencial

Na Matza Education, cada treinamento foi desenvolvido para oferecer conhecimento prático e relevante, conectando teoria e aplicação em cenários reais. Nosso objetivo é preparar profissionais para os desafios do mercado, fortalecendo habilidades técnicas e estratégicas em diferentes áreas da tecnologia e gestão.

Ao participar de um de nossos programas, você terá acesso a conteúdos atualizados, instrutores experientes e uma metodologia voltada para resultados. Independentemente do formato — presencial ou online — buscamos criar uma experiência de aprendizado dinâmica, acessível e de alto impacto.

Mais do que um curso, cada treinamento é uma oportunidade de evolução profissional e pessoal, ajudando você a conquistar certificações, ampliar suas competências e se destacar em um mercado cada vez mais competitivo.

Importante: você deve confirmar o e-mail recebido após a inscrição para validar sua participação.

Enterprise, corporate, or SMB business professionals in non-technical roles.

Roles include but are not limited to: business analysts, IT managers, project managers, product managers.

For senior VPs and above, Data Driven Transformation with Google Cloud is more suitable.

  • Gain a thorough understanding of how ML can be used to improve business processes and create new value.
  • Explore common machine learning use cases implemented by businesses.
  • Identify the requirements to carry out an ML project from assessing feasibility, to data preparation, to model training, to evaluation, to deployment.
  • Define data characteristics and biases that affect quality of ML models.
  • Recognize key considerations for managing ML projects including data strategy, governance, and project teams.
  • Pitch a custom ML use case that can meaningfully impact your business.
  • No prior technical knowledge is required.
  • Savvy about your own business and objectives.
  • Recommended: completing the Business Transformation with Google Cloud course.

2 days – 16 hours

  • The course includes presentations, demonstrations, and immersive activities.
  • Module 1: Introduction

    • Overview: what is machine learning?
    • Key terms: Artificial intelligence, machine learning, and deep learning.
    • Real-world examples of machine learning.
    • Overview: five phases in a machine learning project.
    • Phase 1: Assess the ML use case for specificity and difficulty.
    • Brainstorm a minimum of three custom ML use cases.
  • Module 2: What is Machine Learning?

    • Common ML problem types.
    • Standard algorithms.
    • Data characteristics.
    • Predictive insights and decisions.
    • More real-life ML use cases.
    • Why ML now.
  • Module 3: Employing ML

    • Features and labels.
    • Building labeled data sets.
    • Training an ML model.
    • Evaluating an ML model.
    • General best practices.
    • Human bias and ML fairness.
    • Part 1: custom ML use case proposal.
  • Module 4: Discovering ML Use Cases

    • Replacing rules with machine learning.
    • Automating business processes with machine learning.
    • Understanding unstructured data with machine learning.
    • Personalizing applications with machine learning.
    • Creative use cases with machine learning.
  • Module 5: How to Be Successful at ML

    • Key considerations.
    • Formulating a data strategy.
    • Developing governance around uses of machine learning.
    • Building successful machine learning teams.
    • Creating a culture of innovation.
  • Module 6: Summary

    • Summary, presentations, feedback form.