Solutions
OceanML is a Canadian applied AI company, with experience in several industries including energy, resource development, cleantech, government, finance and health care. We build solutions for clients to unlock meaningful value in workflows with insights and incredible dashboards, at a great price.
Launching this fall
Manta Proposal Automation Platform
A SaaS web app that is designed to reduce time needed to write RFP responses and grant applications by 50%. It helps with your bid strategy, and generates well-structured proposal content based on specific criteria and requirements. Manta is tailored to your specific business needs, quality, and style to make the proposal writing process a breeze. OceanML gives back valuable time for your team.
OceanML Services
Custom ML Development
OceanML offers Custom ML model development. We work with clients to design, build, and deploy machine learning models, tailored to address specific problems and use cases. It involves creating a model architecture, selecting features and requirements, choosing appropriate algorithms, and training the model on relevant data.
Custom ML model development requires a deep understanding of the problem domain, as well as expertise in data analysis, statistical modeling, and machine learning algorithms. OceanML collaborates with client SMEs, including data scientists, engineers, and other technical experts who have the best knowledge of the problem.
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The goal of OceanML's custom ML model development is to create solutions that can accurately predict outcomes or make decisions based on input data for our clients. The techniques used are: supervised learning, unsupervised learning, reinforcement learning, and deep learning. Once the model is developed, it is tested, refined, and deployed to production environments, where it can be used to generate insights, inform decisions, and drive business value for clients.
ML Strategy Consulting
OceanML's integrated strategy consulting provides expert advice, guidance, and support to businesses seeking to leverage machine learning to solve complex problems and improve their operations.
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Our highly skilled data scientists, machine learning engineers, and industry experts work with you to identify the specific business problems and develop tailored solutions using ML algorithms and techniques. This includes generative AI prompt engineering to generate marketing content, insights for R&D, and improve operations.
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We offer guidance to:
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Use generative AI effectively
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Identify the appropriate ML models
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Choose data analytics methods
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Develop algorithms
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Train and optimize models
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Deploy, monitor and maintain models
OceanML also provides guidance on data collection and preparation, infrastructure and platform selection, and overall project management. We help clients unlock the full potential of machine learning technologies to drive innovation, increase efficiency, and achieve our client's business objectives.
Exploratory Data Analysis
Exploratory data analysis (EDA) is the process of examining and analyzing our client's dataset to understand its characteristics and gain insights into the data. The goal of ML EDA is to identify patterns, trends, and relationships in the data that can be used to inform the development of ML models.
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During the ML EDA process, various techniques are used to summarize and visualize the data. These techniques may include statistical summaries, histograms, plots, and heat maps. Exploratory data analysis also identifies and handles missing values, outliers, and other data quality issues.
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The insights gained from ML EDA is used to inform various aspects of the ML modeling process, including feature selection, data preprocessing, and model selection. By gaining a deeper understanding of the data, we can build more accurate and robust models that are better suited to for our client's challenges.
Machine Learning Operations (ML Ops)
Machine Learning Operations is the set of practices and technologies that enable organizations to develop, deploy, and manage machine learning models at scale. We base our ML Ops on DevOps principles and practices, adapted to the specific needs of our machine learning workflows.
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The goal of OceanML's ML Ops is to streamline and automate the machine learning pipeline, from data preparation and model development to deployment and monitoring, while ensuring the reproducibility, scalability, and reliability of the entire process.
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OceanML's ML Ops involve a combination of tools, frameworks, and processes. These include version control, containerization, orchestration, continuous integration and deployment (CI/CD), automated testing, and monitoring. We also emphasize collaboration and communication among data scientists, engineers, software developers, and operations teams, to facilitate the rapid iteration and improvement of machine learning solutions.
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