Transform your software engineers into AI developers with comprehensive, hands-on training in machine learning, deep learning, and production AI systems.
Traditional software engineering skills aren't enough for building AI systems. Development teams without AI training face:
Developers struggle to transition from deterministic programming to probabilistic AI systems without structured training.
Models that work in notebooks fail in production due to lack of MLOps knowledge and engineering best practices.
Teams waste months reinventing solutions to common ML engineering problems that have established patterns and tools.
Organizations pay premium rates for scarce AI talent when they could upskill their existing high-performing engineers.
We meet your developers where they are and take them where they need to be.
For software engineers new to AI who want to build their first machine learning models.
Duration: 5 days intensive or 10 weeks part-time
Prerequisites: Python programming experience
For developers with basic ML knowledge who want to build production-ready AI systems.
Duration: 8 days intensive or 16 weeks part-time
Prerequisites: Basic ML knowledge or Track 1 completion
For ML engineers who need to operationalize AI systems at scale with DevOps best practices.
Duration: 6 days intensive or 12 weeks part-time
Prerequisites: ML engineering experience or Track 2 completion
Get detailed curriculum for all three tracks, including learning objectives, hands-on projects, and certification options.
Learning by doing is at the core of our training. Every track includes multiple hands-on projects.
Build an end-to-end ML system that predicts equipment failures using sensor data.
Build a computer vision and NLP system for automated document classification and information extraction.
Build a complete MLOps pipeline with CI/CD, monitoring, and automated retraining.
Most training time is spent coding, debugging, and building. We provide cloud-based development environments pre-configured with all necessary tools and datasets.
Developers work in pairs and receive code reviews from instructors, mimicking real-world development practices and accelerating learning through peer collaboration.
We use real-world datasets with all their messy complexities, not cleaned academic datasets. Developers learn to handle missing data, class imbalance, and other production realities.
All participants receive lifetime access to course materials, code repositories, video recordings, and our private Slack community for continued learning and support.
We conduct pre-training assessments to understand each developer's background and recommend the appropriate track. We can also run multiple tracks simultaneously for mixed teams, ensuring everyone gets the right level of challenge.
Absolutely. While we have standard curricula, we customize projects and examples to match your technology stack, cloud platform, and specific use cases. We can also focus on particular ML domains (NLP, computer vision, etc.) relevant to your business.
Yes, developers who complete the training and capstone project receive a Boaweb AI certification. We also provide preparation guidance for industry certifications like TensorFlow Developer Certificate or AWS Machine Learning Specialty.
We conduct technical assessments before and after training, measuring improvements in ML knowledge, coding proficiency, and problem-solving ability. We also track post-training metrics like successful AI project completion rates and time-to-productivity on ML tasks.
All participants join our alumni community with monthly office hours, access to our technical experts for questions, advanced workshops on emerging topics, and priority support for 6 months after training completion.
Transform your development team into AI engineering experts. Schedule a consultation to assess your team's current skills and design a customized training program.