Our out of the box solution has been trusted by our valued clients since 1998 to provide top industry professionals as your virtual team. Count on us to provide the right resource at the right time. Key AI powered matching and 6 step human capital management processes ensure a great fit for your critical needs.
We help you develop clear data-driven problem, hypothesis and success metrics. Frustrated with what you’ve tried already? Rest assured that your business objectives will track with your data and technology infrastructure with our proven model selection, validation and application processes.
fixed price projects
Project too much for you to take on yourself? Our team can reduce your risk and speed time to value on critical programs and projects essential for your business to maintain competitiveness in a hyper-fast changing landscape. Operationalizing your data-science opportunities is what we do.
What does our AI and MLOps process look like?
We can take your raw data through data selection sourcing and synthesis into data engineering and modeling, including data cleansing, normalizing, feature engineering, and scaling. Our modeling services include model selection, training, evaluation and tuning.
We operationalize these models for immediate and continuing business value through registration, deployment, monitoring and retraining.
Critically, we apply foundational constraints to the work we do to ensure it meets industry legal, ethical, bias-free and security best practices.
Cloud Native Performance
The cloud has made it possible to quickly and easily prototype Machine Learning proof of concepts that may tick off some off an executive’s box.
But implementing a fully scalable and durable productionized ML Data Science Pipeline is critical to deriving continued business value and non-displaceable competitive advantage from these efforts. There are many hidden bear traps to getting it wrong but huge dividends when implemented correctly.
Machine Learning Expertise
We can provide individuals with the expertise to round out an existing team or build the entire team for you from scratch. Whether you need data scientists, data engineers, machine learning engineers, research scientists, data analysts, MLOps, DevOps or developers – Conexis is ready to meet your needs head on.
Data scientists provide input, output and feedback to build the model (as the definition). Examples include linear regression for sales forecasting and risk assessment, support vector machines for image classification and financial performance comparisons. Decision trees for predictive analytics and pricing.
Use deep learning to arrive at conclusions and patterns through unlabeled training data.
Example algorithms include A priori for sales functions, word associations and searches. K-Means clustering is used for performance monitoring and searcher intent.
Semi supervised learning
Builds a model through a mix of labeled and unlabeled data, a set of categories, suggestions and labels.
Examples include generative adversarial networks for audio and video manipulation and data creation. Self-trained Naive Bayes classifiers can be used for Natural language processing.
Self-interpreting, based on a system of rewards and punishments learned through trial and error, seeking maximum rewards. Example algorithms include Q-learning for policy creation and consumption reduction. Model based estimation is used for linear tasks and estimating parameters.
Navigating environments where there are multiple objectives is challenging. Putting these design patterns into a production environment is even more complex. It’s important to understand and balance the needs of different teams across an organization and how each team’s needs relate back to the model.
Optimizing for the correct balance in the context of data quality, reproducibility, data drift, scale and multiple objectives is critical to your success and is our focus.
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