Aakash dedicatedly works on each aspect to provide the best solutions using technology. He believes that leveraging advanced machine learning techniques can save millions of dollars.
In computer science, proactively identifying anomalies within data to monitor the health of critical assets is one of the challenging problems to solve. Prestigious Cloud Service Providers are striving to create accurate and precise algorithms that can detect anomalies. Advanced statistical algorithms are used to find abnormalities within the data patterns of a single sensor.
Aakash Chotrani, a talented and experienced software engineer and a member of the technical staff at Oracle, is a part of Oracle Cloud Infrastructure's AI services team and has played a pivotal role in enhancing machine learning services. Such revolutionary AI service can scale across 27 regions globally, making it a highly sought-after tool for industries and businesses. Hence, it is an integral part of the Oracle Cloud Service AI Suit. Aakash's blog, "Passionate Star - Computer Science Simplified," shows his passion for innovative technologies where he used to share practical and informative content on different topics, such as Web Development, System Design, SQL, Machine Learning, Competitive Programming, etc. On his Google Scholar page, you can also explore exciting content about AIML, cloud computing, and data screening frameworks.
The data preprocessing pipeline is the first and most crucial step in cleaning the data. However, in some circumstances, there are challenges in Time Series Data, such as varying scales, missing values, different sensor frequencies, and random spikes of training data. Aakash and his internal research team at Oracle solved such problems by developing a comprehensive data processing pipeline. The goal of developing such a groundbreaking pipeline was to incorporate advanced ML techniques including despiking, value imputation, dequantization, APR (Analytic Resampling Process), Unstairstepping, multivariate memory vectorization, tamper-proofing, and ARP Phase Synchronization. In addition, for these cutting-edge techniques, Aakash has patented and published this research.
In addition, this advanced service extends among industries globally, and SailGP is a prominent example. SailGP is an innovative sailing championship relying on OCI Anomaly Detection, generating around 400 million interference per race. SailGP assures the optimal performance of its critical assets by utilizing this service. Moreover, SailGP also offers a competitive advantage and enhanced efficiency.
Aakash dedicatedly works on each aspect to provide the best solutions using technology. He believes that leveraging advanced machine learning techniques can save millions of dollars. He is significantly impacting multiple sectors worldwide using such advanced and efficient technologies. Prognostic detection of anomalies benefits commercial and personal assets like heart rate monitors, nuclear power plants, electrical stations, and even automobiles. Businesses and individuals can take preemptive action by identifying sensor faults before they stop working. These advanced techniques reap monetary advantages and potentially save lives.
Nowadays, industries and businesses need anomaly detection solutions to meet market demand. Hence, Aakash and his team's contributions to the AI services division of Oracle Cloud Infrastructure stay at the forefront of industry advancements. Machine learning services curated by them are paving the way for more efficient and accurate anomaly detection by bolstering the operational health of critical assets across various sectors.
For more information and to learn about ML, You can also explore the potential benefits of the Anomaly Detection service at OCI Anomaly Detection.