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Top Machine Learning Companies in Vietnam


3 Companies

profile

NeurondAI

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Neurond is an artificial intelligence company that provides businesses with customized AI solutions. Founded in 2020 by AI experts with 20 years of experience in this field, Neurond is committed to helping businesses harness the power of AI to transform their operations and drive growth. What sets Neurond AI apart is its deep understanding of client’s businesses and the ability to deliver solutions that meet their specific needs. We will work alongside you to turn your business ideas into reality. Neurond AI provides world-class Artificial Intelligence and Data Science solutions to companies around the globe. Our services include AI, Business Intelligence, Data Engineering, Data Science & Analytics Solutions, and MLOps.

  • dollar

    $25-49/hr

  • user

    10 to 49

  • calender

    2020

  • location

    Vietnam

profile

GEM - Global Enterprise Mobility Corporation

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Global Enterprise Mobility (GEM) is a leading provider of excellent IT services in Vietnam. Founded in 2014, GEM has created diverse service offerings, which include application development & maintenance and emerging tech development - namely AI and Big Data solutions. GEM has been the proud IT partner of businesses of all scales and industries from all over the world - Japan, Singapore, New Zealand, South Korea, the US, and the EU. Our clients is empowered by GEM's CMMI-appraised delivery process and extensive technical strategy consultancy service. As a leading IT service provider, GEM’s service stands out with three key benefits for businesses - Competent IT personnel: Tech-savvy professionals experienced in handling projects of all scales and various industries - Extensive tech-stack: Read More

  • dollar

    <$25/hr

  • user

    250 to 499

  • calender

    2014

  • location

    Vietnam

profile

GEM Corporation

No Reviews

Global Enterprise Mobility (GEM) is a premier provider of cutting-edge IT services with a view to spearheading innovation in Vietnam's tech landscape. Founded in 2014, GEM has become a distinguished and trusted tech partner of global clients thanks to our diverse array of services tailored to meet their evolving needs of our clients. GEM offers key services: - Application Modernization - Technology Transformation - Cloud Transformation and Managed Services - Data Platform Development and Data Migration - System Integration - BI & Analytics - AI Solutions Why Choose GEM? 1. Innovative Solutions: GEM possesses an extensive tech stack and best industry practices to deliver innovative solutions that exceed expectations. 2. Client-Centric Approach: Our unwaveRead More

  • dollar

    <$25/hr

  • user

    250 to 499

  • calender

    2014

  • location

    Vietnam

Frequently Asked Questions

Machine Learning is a subset of Artificial Intelligence that focuses on creating algorithms and statistical models that enable computer systems to improve their performance on a specific task through experience, without being explicitly programmed. It's a key component of AI that allows systems to learn from data and make predictions or decisions.

Machine Learning can provide more adaptive and intelligent solutions compared to traditional software. It can analyze large amounts of data to identify patterns and make predictions, automate complex tasks, personalize user experiences, and continuously improve its performance over time. This can lead to increased efficiency, better decision-making, and new insights for your business.

The main types of Machine Learning are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled data to train models, unsupervised learning finds patterns in unlabeled data, and reinforcement learning trains models through a reward-based system. Each type is suited for different kinds of problems and datasets.

Machine Learning has diverse applications across industries. In healthcare, it's used for disease prediction and medical image analysis. In finance, it's applied for fraud detection and algorithmic trading. In retail, it enables personalized recommendations and demand forecasting. Other applications include autonomous vehicles, natural language processing, and predictive maintenance in manufacturing.

Some challenges include the need for large amounts of quality data, potential bias in algorithms, the "black box" nature of some models making decisions difficult to interpret, high computational requirements, and the need for skilled professionals. Additionally, there may be privacy concerns when dealing with sensitive data.

Machine Learning is a broader field that includes various algorithms and statistical methods. Deep Learning is a subset of Machine Learning that uses artificial neural networks with multiple layers (deep neural networks) to model and process complex patterns in data. Deep Learning is particularly effective for tasks like image and speech recognition but often requires more data and computational resources than other Machine Learning methods.

Implementing Machine Learning solutions typically requires a mix of skills including programming (often in languages like Python or R), statistics and mathematics, data analysis, and domain-specific knowledge. Familiarity with Machine Learning algorithms, data preprocessing techniques, and tools like TensorFlow or scikit-learn is also important. Additionally, skills in data visualization and communication are valuable for presenting results to non-technical stakeholders.

Companies new to Machine Learning can start by identifying specific business problems that could benefit from data-driven solutions. They can then explore available datasets, consider hiring or training staff in Machine Learning, or partnering with specialized consultants or service providers. Starting with small, well-defined projects can help build experience and demonstrate value before scaling to larger initiatives.

Ethical considerations in Machine Learning include ensuring data privacy and security, addressing potential biases in training data or algorithms, maintaining transparency in decision-making processes, and considering the societal impact of automated systems. It's important to have clear governance structures and to regularly audit Machine Learning systems for fairness and accuracy.

The cost of Machine Learning solutions can vary widely depending on the complexity of the problem, the amount of data involved, and the required infrastructure. While initial costs may be higher due to the need for specialized skills and potentially more powerful hardware, Machine Learning solutions can often provide long-term cost savings through increased efficiency and automation. However, ongoing costs for data management, model updating, and maintenance should also be considered.

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