DeepCell, a pioneering company in the field of micro-fluidic microscope based cell sorting, is pushing the boundaries of cell analysis and sorting through the power of AI.
With a recent successful Series B funding round of $70 million, DeepCell is rapidly advancing its technology to revolutionize the field of cell biology.
Collaborating with a team of experienced machine learning consultants, DeepCell underwent a transformative phase in its development, enhancing its MLOPs capabilities and introducing a cutting-edge self-supervised pre-training system.
This case study explores the impact of these advancements on DeepCell's processes and outcomes.
As Sanas.ai gained traction and more clients, the AI team quickly realized the need to scale their operations. They needed to enhance their technology, improve their development processes, and optimize the entire machine learning workflow to handle a higher volume of requests and data.
To address these challenges, the AI team sought external guidance and expertise, and that's when they engaged our team of 3 experienced consultants.
One of the major achievements of our collaboration between DeepCell was the development of a new self-supervised pre-training system.
This system, built upon advanced AI techniques such as self-supervised representation learning, adversarial learning, recalibration, aleatoric, and epistemic uncertainty, and neural network architecture design, brought about a transformative reduction in errors by more than 50%.
The introduction of the self-supervised pre-training system not only improved the quality of embeddings but also significantly reduced the requirement for labeled data by over 90%.
This was a groundbreaking accomplishment, as it alleviated the burden of manual labeling, accelerated data processing, and enabled DeepCell to analyze and sort cells at an unprecedented scale.
The utilization of the self-supervised pre-training system enabled DeepCell to perform unsupervised clustering of samples.
This breakthrough allowed for biologically interpretable outcomes, empowering researchers to gain deeper insights into cellular behavior and characteristics. The ability to discern patterns and categorize cells without explicit labels opened up new avenues of exploration in the field of cell biology.
The collaboration with Eventum’s ML talent extended beyond the technical aspects, as they also provided valuable strategic input.
Through detailed R&D roadmapping, the machine learning engineers helped DeepCell chart a course for future development, identifying key areas of focus, potential challenges, and opportunities for growth.
This strategic guidance played a crucial role in aligning DeepCell's efforts with its long-term vision and business goals.
Recognizing the significance of talent development, we also provided mentorship to ML scientists and engineers at DeepCell.
This mentorship empowered the team with valuable insights, best practices, and industry expertise, while fostering a culture of continuous learning and innovation within the organization.
Additionally, our team also contributed to the technical interviewing process for leadership candidates. This ensured that DeepCell was able to attract top-tier talent to its ranks, strengthening the company's capabilities and positioning it for sustained success in the highly competitive AI and biotechnology landscape.
Our collaboration with DeepCell and the team of experienced machine learning specialists has significantly propelled the company's mission to revolutionize cell sorting using AI technology.
The refreshed MLOPs capabilities and the implementation of the self-supervised pre-training system have enabled DeepCell to achieve remarkable results, reducing errors, improving embedding quality, and unleashing the potential of unsupervised clustering.
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Learn how Eventum helped DeepCell reduce manual oversight by 90%, provided R&D roadmapping & mentorship of ML team