AI projects can be challenging to manage, especially when it comes to setting and meeting deadlines. Unlike traditional software development, AI involves a lot of uncertainty, experimentation, and iteration. How can you adjust deadlines for AI without sacrificing quality? Here are some tips to help you balance speed and accuracy in your AI endeavors.
Before you start working on an AI solution, you need to have a clear and realistic understanding of the problem you are trying to solve. What are the goals, expectations, and constraints of the project? What are the data sources, quality, and availability? What are the potential risks and pitfalls? By defining the scope and requirements of the problem, you can avoid wasting time and resources on irrelevant or unfeasible tasks.
Data collection and preprocessing consume at times upto 80% of the total effort for an AI project. This piece should not be underestimated and thoroughly planned for, success will automatically follow.
One time at work, I encountered a challenging AI project with a looming deadline. In my experience, the key to adjusting deadlines without compromising quality lies in task breakdown and prioritization. One thing I've found helpful is employing agile project management techniques, which allow for dynamic deadline adjustments while upholding high-quality standards.
The first stage in starting a project/product is understanding the business problem, personas, and determining whether this kind of problem can be solved with AI/ML. Once the business problem is understood, the next steps involve defining the business outcomes, definition of AI/ML goals, data requirements, resource allocation, risk assessment, milestones and timeline (roadmap). Sometimes, tackling a complex business problem with AI/ML may require breaking it down into multiple smaller AI solutions. This approach offers the advantage of a more focused and agile development process, where each AI component can be delivered more quickly. Additionally, by prioritizing AI components, it aids in effective deadline management.
Data Scientist @ Wi-Tronix | ASU | BITS Pilani | Ex-Slytek, Drishti, and SemiCab
Adjusting deadlines for AI projects without compromising quality is a delicate balancing act. To succeed, you must start by thoroughly understanding the problem at hand. This entails defining clear project goals, expectations, and constraints, and assessing data sources' quality and availability. By doing so, you can effectively manage project scope and avoid unnecessary detours, ensuring your team's time and resources are wisely allocated. This essential initial step allows for more realistic timeline adjustments when necessary while maintaining the integrity of the project's outcome. #AI #ProjectManagement #Quality #Deadlines #ArtificialIntelligence
To adjust AI project deadlines without compromising quality, start by identifying critical components and potential trade-offs. Prioritize tasks and allocate more time to complex model training or data quality improvement. Implement agile methodologies to adapt quickly. Communicate changes transparently with stakeholders. Use continuous testing and feedback loops to maintain quality standards throughout the project, ensuring a balanced approach to deadlines and quality.
There are many different ways to approach an AI problem, depending on the complexity, data, and desired outcome. You can use existing frameworks, libraries, and tools to speed up the development process, or you can create your own custom models and algorithms. You can also leverage pre-trained models, transfer learning, or synthetic data to reduce the need for extensive data collection and training. Whatever approach you choose, make sure it is aligned with the problem and the deadline.
Choosing the right approach in adjusting AI project deadlines is pivotal. It enhances efficiency by optimizing resource allocation, risk management, and task prioritization. It ensures quality through regular quality assurance, automation, and scope management. The approach also fosters clear communication with stakeholders and allows for lessons learned from past projects, ultimately streamlining the project's timeline while preserving quality.
Data Scientist @ Wi-Tronix | ASU | BITS Pilani | Ex-Slytek, Drishti, and SemiCab
Adjusting deadlines for AI projects without sacrificing quality requires a strategic approach. The key is to strike a balance between speed and accuracy. First, choose the right approach based on the project's unique requirements. Utilize existing frameworks and tools to expedite development or opt for custom models and algorithms for specialized needs. Leverage pre-trained models, transfer learning, or synthetic data to minimize data collection and training time. Ensure that your chosen approach aligns with project objectives and timelines. By making informed decisions, you can manage AI projects effectively, meeting deadlines without compromising quality. #AI #ProjectManagement #ArtificialIntelligence #QualityControl #Deadlines
It's essential to consider the ROI. This begins with data exploratory, where we assess several key factors: Data Requirements: Determine whether the data needs to be labeled or if synthetic data generation is required. Algorithm Selection: Identify the most suitable algorithms for the specific ML problem. Existing APIs and Models: Explore the feasibility of leveraging existing APIs and pre-trained models. Retraining and Fine-tuning: Understand any constraints related to retraining or fine-tuning models if necessary. In-house Dev vs. Existing Models: Evaluate the advantages and disadvantages of using an existing model vs. developing an in-house solution. Costs and Risks: Assess the costs and risks associated with each approach.
Once you have chosen the right approach, you need to plan and prioritize your tasks and milestones. Break down the project into smaller and manageable chunks, and assign them to different team members or roles. Estimate the time and resources needed for each task, and set realistic and flexible deadlines. Prioritize the most critical and impactful tasks, and leave some buffer for unexpected issues or changes.
Data Scientist @ Wi-Tronix | ASU | BITS Pilani | Ex-Slytek, Drishti, and SemiCab
Managing AI project deadlines without compromising quality requires a strategic approach. The inherent uncertainty and iterative nature of AI make planning and prioritizing essential. To strike the right balance, start by breaking the project into manageable tasks, assigning them to specialized team members, and estimating time and resources for each. Set realistic yet flexible deadlines, giving due consideration to critical tasks and leaving room for unforeseen issues. In this way, you can adapt to the dynamic nature of AI projects while ensuring a high-quality outcome. #AI #ProjectManagement #Deadlines #QualityAssurance #ArtificialIntelligence
One of the most important aspects of AI development is testing and iterating. You need to constantly monitor, evaluate, and improve your AI solution, using various metrics and feedback mechanisms. You also need to be ready to adapt to new data, requirements, or challenges that may arise during the project. Testing and iterating can help you ensure the quality and performance of your AI solution, as well as identify and resolve any errors or bugs.
In AI project management, 'timeboxing' research phases is key. By allotting a fixed portion of the project timeline to exploration, teams avoid the pitfall of perpetual research, moving forward with the best current solution instead of seeking unattainable perfection. Additionally, incremental delivery is vital in AI projects. This method allows for constant evaluation and realignment with changing needs and new technologies. Teams using this strategy can perpetually enhance AI models, which is essential in the ever-evolving realms of AI and machine learning where the 'ideal' solution is fleeting and subjective.
Data Scientist @ Wi-Tronix | ASU | BITS Pilani | Ex-Slytek, Drishti, and SemiCab
Embrace the mantra of "test and iterate." The dynamic nature of AI demands ongoing monitoring and refinement. Continuously assess your AI solution using a range of metrics and feedback mechanisms, and be prepared to adjust in response to evolving data, changing requirements, and unexpected hurdles. This iterative approach empowers you to safeguard quality, optimize performance, and swiftly address any errors or bugs that might emerge. Balance is key in AI, and by embracing constant testing and iteration, you can steer your project towards success without sacrificing excellence. ???? #AI #ProjectManagement #Deadlines #Quality #TestingAndIteration #ArtificialIntelligence #AIProjects
In AI development, continuous testing and iteration are fundamental. It's essential to start testing the initial version of your model with defined 'Personas' early in the process. Integrate the model and collect feedback from these 'Personas' to iterate towards fulfilling the business outcomes. This iterative approach minimizes risks and ensures that your model continuously improves while adhering to acceptance criteria, management deadlines, and defined outcomes.
Finally, you need to communicate and collaborate effectively with your stakeholders, clients, and team members throughout the project. You need to keep them updated on the progress, challenges, and results of your AI solution, and solicit their input and feedback. You also need to manage their expectations and address any concerns or questions they may have. Communication and collaboration can help you build trust and rapport, as well as avoid misunderstandings or conflicts.
A couple of pointers to hit the ground running with AI projects : - Start with an MVP/Pilot (ie., Smallest possible Scope-->Less Effort-->Quicker feedback on performance) - Define and Align stakeholders on what success looks like (at times, you may not have the success criteria on day one) - Monitor the Success metrics (every day,every week) & then Scale. - Build a Regular cadence mechanism to check the performance of the AI models. - Refine the AI model--> Re-deploy--> Monitor and keep improving...
To adapt AI project deadlines while maintaining quality, break the project into smaller tasks, set realistic expectations, employ a project management tool for progress tracking and early issue identification, communicate changes with the team, and remain flexible. Prioritize essential tasks, leverage automation tools, outsource when necessary, and conduct thorough AI model testing before deployment to ensure quality standards are met. This approach ensures on-time delivery of high-quality AI products and services.
- AI Models are not 100% predictable. Thus, have a stable and progessive fallback experience in case user doesn't get the desired results. - Start your team's RAI (Responsible AI) discussion as early as you can.
In my opinion, it's inevitable that we cannot reach at a precisely defined model during a certain time. In my view, choosing a development methodology that allows us to create an initial release and then return to the model in subsequent iterations to modify and improve it is one of the best ways to maintain both quality and not lose time. For example, I think Agile could be one of these methodologies.