Scaling ML Models and Embracing AI to Augment Humanity

Saurabh Agarwal, a machine learning (ML) engineer at MavQ, an India-based AI platform company, has emerged as a prominent figure in the field, constantly keeping up with the latest developments in artificial intelligence (AI). Agarwal’s passion for data and his relentless pursuit of knowledge have propelled him to the forefront of ML modeling and deployment.

Hailing from Jaipur, North India, Agarwal’s journey began during a full-time internship in data engineering and cloud, where he first experienced the power of data. Intrigued, he delved deeper into data analytics, honing his skills in building data pipelines for ML models. With guidance from mentors like Gaurav Kheterpal, an expert in Salesforce, Mulesoft, and multicloud, and support from AI communities, Agarwal solidified his expertise in ML modeling.

Over the course of two years, Agarwal mastered the development and deployment of ML models from scratch. His repertoire expanded to include handling models for converting paper documents into digital formats using deep learning techniques. Concurrently, he pursued an executive course in AI and ML from the prestigious Birla Institute of Technology and Science, Pilani.

Agarwal’s enthusiasm stems from witnessing the tremendous potential of data. He explains, “The sheer joy of seeing what data can do is exciting. Today, the exponential jump in data consumption has made models easy and resource-friendly.” Looking ahead, he foresees a promising future for MLOps (machine learning operations) and model productization, highlighting the importance of optimizing models for scalability, cost-effectiveness, and maintaining a competitive edge.

In India, a career in machine learning can be financially rewarding. Agarwal reveals that the salary package for an average ML engineer ranges from Rs. 15-20 lakhs (US$18,000-24,000) per annum, with higher-level domains commanding Rs. 60-70 lakhs per annum as proficiency increases. He emphasizes the significance of specialization within domains such as MLOps, applied ML, and advanced modeling, asserting that those who possess a clear focus and expertise in specific areas will excel.

Agarwal stresses the need for a strong command of software engineering and a deep understanding of data as fundamental traits for aspiring ML professionals. He advises individuals to appreciate how data affects businesses before delving into ML modeling. Furthermore, he emphasizes the importance of explainability, ethical considerations, and the ability to question the implications of models.

Leading a team of 15 to 20 individuals, Agarwal spends most of his day productizing ML models and benchmarking them against industry leaders. He takes pride in the accuracy, quality, and speed of their models, confidently stating that they are either on par with or surpassing renowned names in the field.

Agarwal firmly believes that AI will amplify the potential of data for businesses in the future. As models and deep learning continue to improve, he emphasizes the need to confront challenges such as the “black box” problem, ensuring transparency and understanding in decision-making processes of complex AI models.

After a fulfilling day at work, Agarwal unwinds with a jog or a game of badminton, drawing parallels between his activities and the natural rhythm of an AI professional’s work. His advice to aspiring ML and AI enthusiasts is simple yet profound: choose a domain wisely and develop an inherent love for data. Agarwal envisions AI as an augmentation of human capabilities, emphasizing the need to adapt and harness its potential.

Saurabh Agarwal’s journey exemplifies the passion, expertise, and unwavering commitment required to succeed in the dynamic realm of AI and ML. As he forges ahead, Agarwal’s dedication to scaling ML models and embracing AI to augment humanity remains unwavering.