Applied AI Lab
Harness the power of collaboration to drive innovation
Increase AI Adoption Through Collaboration
Accelerate the realization of artificial intelligence (AI)’s complete capabilities and foster continuous innovation within your organization by co-developing and piloting cutting-edge machine learning (ML) models alongside AltaML’s team of AI experts. Seamlessly integrate AltaML’s expertise with your organization’s unique strengths, empowering and complementing your team with knowledge and skills in this collaborative and transformative partnership.
Streamline AI Implementation
Accelerate AI maturity within your organization. Unlock the power of AI-driven operations faster, improving your return on investment (ROI) and gain a competitive edge.
Optimize Roadmap and Resources
Navigate the AI landscape strategically. Drive transformative outcomes with seamless implementation of your AI strategy with support from a versatile team of experts.
Upskill Team and Increase Capacity
Supplement internal knowledge gaps while increasing development capacity. Gain valuable insights, external perspectives, and leverage the diverse skills of AltaML’s team.
Shaping the Future of Your Organization Together
Revolutionize operations and unlock unprecedented value with AltaML’s Applied AI Lab. Accelerate your AI adoption journey while aligning your goals with high-value ML models that will propel your organization forward. This collaborative and supportive environment empowers teams to push boundaries, explore new possibilities, and uncover valuable insights, shaping your organization’s future through experimentation and piloting cutting-edge models alongside AltaML.
At the beginning of this engagement, AltaML collaborates closely with you, conducting various initiatives to set the foundation for a successful program, including establishing a comprehensive program plan, setting program and technical meetings, planning training and ideation sessions, and finalizing status report requirements. Key stakeholders are identified immediately so that internal decision-making processes, governance, and protocol can be set to ensure security, privacy, and data transfer compliance. Mapping out all critical elements upfront sets a strong foundation for success and a well-managed AI program.
AltaML is dedicated to boosting AI literacy throughout your organization by providing educational sessions based on your specific needs and requests. This ongoing effort is designed to foster continuous learning, support project success, and promote the widespread adoption of AI tools. Collaborating closely with you, AltaML identifies future AI opportunities while understanding your unique requirements and the value derived from these education sessions. Offering a range of options catering to your needs, we ensure the education content is tailored to equip your team with the knowledge and skills to thrive in the evolving AI landscape.
AltaML collaborates with key stakeholders during ideation sessions to define and refine potential AI use cases. Through careful analysis, two prioritized use cases are identified, supported by comprehensive idea canvases. To ensure responsible AI deployment, AltaML evaluates the use cases for ethical considerations and biases. Dedicated sessions further define the problem to be solved, capturing additional details. AltaML’s Data team collaborates with your team to collect necessary information from identified sources, which is vital for machine feasibility assessment and developing tailored learning models for the use cases.
After prioritizing use cases for experimentation, AltaML follows a systematic approach that includes a use case feasibility assessment, balancing ML model performance, cost, effort, and risk to address the business problem. Activities such as business context definition, data assessment, machine learning approach assessments, and use case feasibility assessments are conducted. AltaML dynamically and iteratively extracts insights from data, conducting machine learning modeling while considering ethical and bias risks. Regular communication and collaboration occur, with progress updates, feedback sessions, and collective development of the next steps. A thorough quality assurance step ensures high-quality deliverables. This phase concludes with a final report, executive overview presentation, lightweight business case, and estimates for subsequent steps.
This phase involves detailed analysis, planning, and design for implementing the ML model. It includes gathering stakeholder requirements, defining model prediction requirements, and developing testing plans. Technical planning tasks focus on solution architecture, IT infrastructure, data ingestion, privacy and security, and user display requirements. The pilot execution includes testing the deployed model, machine enhancements, and validating results. A final pilot report and recommendations for the next phase of model solution deployment are prepared.
Throughout the project, you will be provided with various resources to support you along your AI exploration and adoption journey. These resources include use case feasibility assessments, a data insights report or dashboard, a lightweight business case, code and model cards, etc. A final report summarizing the entire engagement is created and presented to key stakeholders and will provide recommendations for moving forward with each use case.