AI/ML Development
Engineering with Artificial Intelligence
AI development and custom engineering is an exciting and dynamic process that involves designing, developing, and deploying artificial intelligence systems and applications. Here are some key aspects of how we help businesses use data as their competitive advantage:
Research and Design: You would start by understanding the problem you’re trying to solve and researching potential AI solutions. This might involve studying existing algorithms, models, and techniques, and determining which ones are most suitable for the task at hand.
Algorithm Development: You would then develop and fine-tune algorithms and models based on your research. This might involve selecting machine learning algorithms, neural network architectures, and other techniques that are best suited to solving the problem.
Data Collection and Preparation: High-quality data is crucial for training AI models. You would need to gather, preprocess, and clean the necessary data to ensure it’s suitable for training and evaluation.
Model Training: This involves using the prepared data to train your AI models. You would configure parameters, perform training iterations, and monitor the model’s performance to ensure it’s learning effectively.
Model Evaluation and Testing: Once trained, you’ll evaluate the model’s performance using various metrics and test it with new data to ensure it generalizes well to unseen examples.
Optimization: Fine-tuning models for better performance is an iterative process. You’ll work on optimizing models, tweaking hyperparameters, and possibly implementing regularization techniques to avoid overfitting.
Deployment: After obtaining a well-performing model, you’ll work on deploying it to real-world applications. This might involve integrating the model into existing software systems, creating APIs for interaction, and ensuring the model operates efficiently in production.
Monitoring and Maintenance: Once the AI system is live, you’ll monitor its performance in real-world scenarios. If necessary, you’ll fine-tune the model further based on how it behaves in the wild and address any issues that arise.
Collaboration: AI development is often a collaborative effort. You might work with data scientists, software engineers, domain experts, and other stakeholders to ensure the AI solution meets the desired goals.
Our team of full stack engineers are fluent in programming (common languages like Python, Tensorflow, R), mathematics (linear algebra, calculus, statistics), and a deep understanding of machine learning and neural networks. Depending on the projects, you might work with various AI frameworks and libraries like TensorFlow, PyTorch, or scikit-learn.
AI Engineering consultants play important roles in our clients’ teams by designing and implementing data-centric scalable solutions. They are involved in data ingestion and data flow pipelines, conducting data analysis and managing data farms.