Top enterprises have reaped the benefits of data science capabilities for years. Data science has become a key differentiator in the way companies stand out and compete in an increasingly unpredictable market. Competent leaders seek out mature, full-scale data science consulting services to help them identify revenue streams and grow faster.
Analyze the quality of your data to determine if it is good enough to provide signals for predictive modeling. Signal Analysis provides the foundation and insights for long-term model implementations and deployments. Our signal analysis offering is a common starting point for organizations that are new to Data Science or for new Data Science use cases.
Examine your data and design data pipelines, data quality checks, data catalogs, and data storage solutions for both Data Science and Analytics consumption. The success of Analytics and Data Science implementations depends greatly on the quality of the data and how it is structured and stored.
Explore your data and recommend strategic analytics initiatives to help improve your data quality, governance, and business outcomes. Provide roadmaps of your systems of intelligence journey and guidance to pick the best tools and technology after reviewing the business requirements and budget for long-term success.
Data visualization is only half the battle. Solve business problems using machine learning techniques like supervised, unsupervised, reinforcement learning, and share actionable insights that lead to results. Build machine learning models to uncover new business opportunities and enhance your customer experience.
Use advanced techniques like Natural Language Processing, Deep Learning, Robotics, Speech, and Vision to solve complex business problems. Simplify business processes with AI solutions to improve operational efficiencies and reduce cost.
Model performance degrades over time with constant changes to data and business processes. Having an ongoing
support and maintenance process is critical.
We recommend best practices for model deployment, monitoring, retraining, and management to support and maintain AI solutions at scale.
Machine learning operations, more commonly known as MLOps, is the pursuit of faster and more reliable delivery through a pipeline of data to insight and value. While DevOps is concerned with breaking down the silos of development and operations, MLOps builds upon DevOps, but adds the breakdown of the data engineer/ETL silo, the analytics silo, and the data science silo under its domain.
In this whitepaper, our team dives deep into MLOps and its benefits, use cases, and best practices. You will get insights on what’s next for MLOps, real-world examples of effective MLOps via Salesforce technologies, the business benefits of a long-term approach, and more.
From data collection, data preparation, data engineering, to evaluating machine learning algorithms, our experts can help you get a handle on best practices and next steps for success.
For customers who:
In just 90 days, we create intelligent customer and user experiences, build machine learning models and AI systems to generate insights and take action to improve the experience.
The explosion of data science into modern boardroom vernacular has led to high expectations for the field. As we move toward the future, it’s important to us as a data science consulting service that we build confidence today in what AI and advanced analytics can do.
We understand the power of artificial intelligence and how it will shape the future landscape of businesses.
Through our Elevate managed services, we provide customers with ongoing support and enhancements of their data science and analytics solutions.
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