Case Study

Advanced Cloud-based Machine Learning Experiment Orchestration

The Challenge

Many machine learning engineers face significant challenges when running experiments locally on their computers. Depending on the hardware used, these tasks can be time-consuming and prone to causing hardware issues. The challenge was to create a platform that enables these experiments to be conducted in the cloud, utilizing the latest hardware technology.

Goals & Team Structure

The primary goal of Aichor was to develop a seamless and efficient platform for machine learning experiment orchestration on the cloud. The project aimed to:

1. Provide a robust cloud-based environment for running machine learning experiments.
2. Minimize the time and hardware constraints faced by engineers and data scientists.
3. Ensure scalability and flexibility to handle varying workloads and complex models.

The team structure consisted of:

• Project Manager: Oversees project development and ensures milestones are met.
• Machine Learning Engineers: Design and implement the machine learning models.
• Cloud Engineers: Set up and maintain the cloud infrastructure.
• UX/UI Designers: Create an intuitive and user-friendly interface.
• QA Testers: Ensure the platform is free of bugs and performs optimally.

Design Process

The design process followed a structured approach:

1. Requirement Gathering: Collaborated with stakeholders to understand their needs and define project requirements.
2. Conceptual Design: Developed initial sketches and wireframes to visualize the platform's layout and user flow.
3. Prototyping: Created interactive prototypes to test and refine the user experience.
4. Implementation: Translated designs into a functional platform using modern development frameworks and cloud technologies.
5. Testing & Iteration: Conducted rigorous testing to identify issues and iterated on the design based on user feedback.

Research

To ensure Aichor met the needs of its users, extensive research was conducted:

1. User Interviews: Engaged with machine learning engineers and data scientists to gather insights into their workflows and pain points.
2. Competitive Analysis: Studied existing cloud-based ML platforms to identify strengths and gaps.
3. Technical Feasibility: Assessed the latest cloud technologies and hardware capabilities to ensure the platform could deliver optimal performance.

High Fidelity Design

The high-fidelity design phase involved creating detailed and polished versions of the platform's interface:

1. Visual Design: Developed a cohesive visual language that was both aesthetically pleasing and functional.
2. Interaction Design: Focused on creating smooth and responsive interactions to enhance user experience.
3. Accessibility: Ensured the platform was accessible to users with various needs, incorporating best practices in inclusive design.

Behance

Usability Testing Insights

Usability testing provided critical insights into how users interacted with Aichor:

1. Ease of Use: Users appreciated the intuitive interface, which simplified the process of launching and managing experiments.
2. Performance: The platform significantly reduced experiment run times compared to local setups, leading to high user satisfaction.
3. Flexibility: Users valued the ability to scale resources up or down based on their needs, making the platform versatile for different types of projects.

Final Thoughts

Aichor has successfully addressed the challenges faced by machine learning engineers by providing a powerful and user-friendly cloud-based platform for running experiments. The collaborative effort of a dedicated team and a user-centered design approach resulted in a solution that not only meets but exceeds user expectations. With Aichor , engineers can now focus more on innovation and less on the limitations of their local hardware, driving forward the field of machine learning research.