You’re managing an AI project. What are the most important things to know?

Managing an AI project requires understanding these key aspects: 1. Clear Objectives: Define specific, achievable goals aligned with business needs. 2. Data Quality & Quantity: Access to relevant, high-quality data is crucial for training and validation. 3. People. They need to be trained with the right knowledge and skills. 4. Flexibility. Given the rapid pace of technological change, your team needs to stay abreast of the latest innovations - and if appropriate - adjust the AI project to ensure you are adapting these (if appropriate). 5. Managing stakeholders - possibly one of the hardest tasks. Communicate clearly to executive leadership the necessary sunk costs and timeframes needed for a successful AI project.

To ensure the success of an AI project, you also need to understand baseline metrics to prove the added value of an AI/ML model. Following a holistic approach that combines model application, data-centric, and model-centric approaches might help you manage various risks and constraints during the project.

Resist the urge to rush into modeling before nailing down use cases and objectives. Meet extensively with internal stakeholders and end-users to understand pain points. Document detailed requirements for data inputs, functionality, expected outputs, and performance benchmarks. Quantify the current difficulties through metrics like revenue loss, high error rates, etc caused by gaps AI can fill. Map out the ideal future state upon successful implementation. Emphasise how solving the problem advances top-level goals like higher customer retention or lower risk levels.

When starting an AI project, I find these questions useful: - What is the business/ technical challenge that we need to solve? - What's the KPI that this project will have an effect on? - Who is the owner of that KPI? Is that person/ team sponsoring the project? - Is there any common platform/ MLOps architecture the project should adhere to? - Do we have the right training data? Is it available through well defined/ standard data engineering processes? - Who will be in charge of this project once in production?

In managing an AI project, the initial step is crystal-clear: define the problem you're solving and the value you aim to create. It's about digging deep into the needs and expectations of stakeholders and users, ensuring your AI solution truly meets them. At Bakstage.AI we first recognized a big problem in the customer experience industry, clearly defined how we believe we can solve the problem of driving conversions for businesses. It involved weighing the feasibility, viability, and desirability of how we would leverage AI to help solve this problem. A well-crafted problem statement guides your efforts, helps communicate your vision clearly, and sets a benchmark to measure progress.

The choice of AI methodology is not just a technical decision, it's a strategic one that impacts your project's scalability, adaptability, and long-term viability. Start by involving a cross-functional team in this decision-making process, blending technical expertise with business acumen and ethical considerations, to create a balanced, forward-thinking AI strategy. Also, maintain a dynamic approach: the field of AI is continually evolving. This means staying abreast of the latest advancements and being open to adapting your strategy as new techniques and technologies emerge. Also very important: establish a robust data governance framework, ensuring that your data is of high quality, ethically sourced and compliant with regulations.

With a well-defined problem and success criteria, next strategically select appropriate modeling techniques. Catalog existing internal data and assess suitability to requirements. Determine if additional external datasets could augment coverage. Survey different classes of methods in literature addressing related quandaries, weighing pros and cons of algorithms like Random Forests vs Neural Networks. Consider complexity of alternate approaches given timelines and team skills. Size infrastructure needs for storage, computing, retraining. Outline logical sequences of steps from raw data ingestion to model export.

To develop an AI project, consider these key aspects: - Data Preprocessing: Clean and transform data, handling missing values and encoding variables. Quality data is crucial for model accuracy. - Feature Engineering: Create new features to enhance model performance. - Model Selection and Tuning: Choose a suitable model based on the problem type. - Validation: Use cross-validation to assess model generalization. Employ metrics like accuracy or mean squared error for evaluation. - Deployment and Monitoring: Integrate the model into real-world systems and monitor its performance, adjusting as necessary. - Ethical Considerations: Ensure the model is fair and respects privacy. Implement bias mitigation strategies.

When dealing with AI projects, it's important to thoroughly research before jumping into solutions for a problem. There are many ways to solve a problem in AI, and it's crucial to choose a method that is both dependable and scalable for the specific challenges of AI projects.

Clarity and Scope: Clearly understand and articulate the problem you're trying to solve. It should be specific, measurable, and aligned with the overall goals of your organization. Stakeholder Needs: Identify who the stakeholders are and understand their needs and expectations. This ensures the solution is relevant and provides value. Data Requirements: Determine what data is needed to address the problem, including its availability, quality, and ethical considerations.

Below list of tools will be best to use.. 1. Data Analysis Tools: Utilize Python, R, or similar for data preprocessing and understanding. 2. Machine Learning Frameworks: Employ TensorFlow, PyTorch, or Scikit-learn for building AI models. 3. Project Management Software: Use Jira, Trello, or Asana for task management and team collaboration. 4. Cloud Platforms: Leverage AWS, Azure, or Google Cloud for computing resources and model deployment. 5. Visualization Tools: Implement Tableau, PowerBI, or Python libraries for data and results visualization.

Choosing the right tool is crucial, considering the plethora of programming languages available in the market. Each language has its strengths, with some excelling in statistical models, others in machine learning, and some specifically tailored for deep learning. It's essential to align the language with the project's requirements and take into account the expertise of fellow developers. In todays time, numerous pre-trained language models (LLMs) are accessible, effectively addressing a wide array of tasks. Leveraging these models allows for streamlined development – pick the optimal model, fine-tune it to align with business needs, and you're ready to deliver a multitude of AI solutions for your product.

AI leaders thoughtfully equip teams with tailored toolchains that accentuate talents not constrain them. They assess current internal infrastructure against project requirements, identifying gaps holding back productivity. With cloud offerings expanding continuously, they prototype on free trials before purchase approvals. They sanction best-of-breed platforms like Databricks for pipelines, MLFlow for experiments and Grafana for observability rather than force-fitting an outdated stack. Security and IT teams vet new tools on compatibility, access controls and compliance.

The third step in AI project development involves selecting the right tools and platforms, considering your team's technical skills, budget, and infrastructure. - Python is a top choice, offering robust libraries like TensorFlow and PyTorch for AI development. - Project Management Tools: Use Jira and Atlassian for efficient project management, facilitating task tracking and team collaboration. - Data Analytics Platforms: Platforms like Databricks provide unified analytics, crucial for big data processing and machine learning. - Cloud Services: Leverage AWS etc - Prioritize security and ensure compatibility with your data formats and sources. - Choose tools that offer seamless integration and scalability to adapt to evolving project needs.

The tools we employ are the embodiment of Da Vinci’s studio, where every instrument is a conduit to discovery. Whether Google Bard, TensorFlow or PyTorch, these are our paintbrushes, and with them, we craft not just models but the very future we aspire to see.

It is very easy to get lost in all the metrics of AI (AUC, precision, recall, accuracy, etc.). We not only get distracted by these metrics but spend inordinate amounts of time trying to understand, explain, and digest them. This takes away, unfortunately, from the business metrics which should be the focus. For example if you are building an ML model to identify urgent messages from patients let's say because you want to respond faster to those messages, measure your average response time for truly urgent messages before/after and make sure it goes down. This sounds obvious but a typical AI/ML project will talk way more about the model accuracy, thresholds, sensitivity, specificity, etc. and forget to measure if they got the result!

AI leaders don’t just set models free, they implement robust evaluations ensuring sustained value delivery over time. Testing happens continuously, not just at completion. They assess outputs systematically against the success metrics defined with business partners upfront. Monitoring post-deployment establishes performance baselines quantifying real-world impact towards KPIs like risk reduction and revenue lift. Those indicators guide ongoing iterations and investment decisions

In evaluating our AI’s performance, we channel the spirit of Marie Curie, delving into the results with a relentless pursuit for truth and utility. Each metric tells a story, and like Curie’s radiographs, reveals what was once invisible.

What I always like to do when evaluating metrics is to take a step back and think about a holistic approach. Beyond just assessing technical performance metrics, I consider the broader impact of our AI solution on business processes, user experience, and ethical implications. It's about striking a balance between the quantifiable outcomes, like accuracy or speed, and qualitative aspects, such as user trust and ethical alignment. Also very important: I view evaluation as an ongoing process rather than a one-off task. This means continuously integrating feedback into iterative cycles, enabling the AI solution to evolve and adapt in a rapidly changing environment.

It is important to keep in mind that evaluation of results goes beyond simple model metrics. It should also cover topics like explainability and complexity. Often less complex and less accurate model that can be easily interpreted gives more business value than a more accurate black-box model.

Identifying unique risks is crucial, as they vary per project. Recognizing specific risks tailors your Risk Management System (RMS) for effectiveness. Quality Management System (QMS) and RMS are integral, even without formal certification like CMMI or ISO. These systems ensure viability and must be aptly suited to the specific AI product. This approach not only mitigates risks but also helps to make your project run smoothly and cost-effectively.

You need to be careful about these risks, - Data Privacy: Comply with GDPR, HIPAA and other regulations. Implement explicit consent mechanism for use of data. Security: DDoS attacks on AI systems can have huge cost implications, implement preventive mechanisms with appropriate audits and logging. Bias and Fairness: use diverse and representative datasets, apply fairness metrics. Continuously monitor and adjust training data and fine tune models in production. Accountability and Transparency: Invest in explainability, proper logging and monitoring Compliance: AI projects must comply with a growing body of regional regulations. Ethics: Consider impact of AI on employment, societal norms, and individual rights. Engage with stakeholders.

A comprehensive risk management strategy is indispensable in AI projects. In sectors such as Healthcare, where model explainability, data compliance, and transparency are pivotal, a robust risk management strategy is essential for AI projects. For example, in predictive models for patient outcomes, early identification of potential biases based on demographics is critical. Strict data governance practices, aligning with regulations are indispensable to uphold patient privacy. Model interpretability is crucial, ensuring that diagnostic models can be understood and trusted by healthcare professionals. This transparency fosters collaboration between AI technologies and practitioners.

First and foremost, start with the team: you should try very hard to embed a culture of ethical AI within your team, ensuring everyone understands the importance of responsible AI practices. You need to safeguard your project and at the same time reinforce your organisation's reputation as a responsible AI innovator. Also remember that risk management is not a static process but a dynamic one, necessitating continuous monitoring, learning, and adaptation. Risk assessment in AI makes no difference.

Data Privacy and Security: Address risks related to data privacy and security, ensuring compliance with regulations like GDPR. Model Drift: Monitor for model drift over time as new data comes in and the external environment changes. Contingency Planning: Have backup plans in case the AI solution doesn't work as expected, including fallback to manual processes if necessary.

Learning and growing from your AI project transforms experience into wisdom. This stage should be approached as an integral part of your AI journey, not merely an afterthought. Cultivate a culture of continuous learning within your team, encouraging them to reflect critically on both successes and setbacks. Documenting and sharing these experiences within and beyond your organisation fosters a knowledge-sharing ecosystem, invaluable for future projects.

???????????? ????, ???????? ???????????????? ??????'?? ???? ????????????????????. It is currently taking a large amount of effort for all of us to keep up with the current developments, and this will likely continue into the next year. This push to learn will drive the most determined into realms of opportunity. If you choose to stay ignorant, the gap to catch up is far greater than most technological changes.

We are working on building a Fraud detection platform. One of the areas of learning was to see if a rule-based system could complement our existing AI/ML model. This system has the added advantage of being explainable and providing valuable insights.

Our project is a living organism, growing and adapting. The spirit of continuous learning is embodied in our team, reminiscent of the lifelong dedication to growth of polymaths like Benjamin Franklin, who never saw a sunset in his learning journey.

Reflection is important. After completing the AI project, take time to reflect on the experiences, challenges faced, and lessons learned. Reflective practices help in understanding what worked well and areas that could be improved. Record insights, challenges, and best practices systematically. Documenting experiences creates a valuable knowledge base for future reference and learning. Share your AI project experiences with peers, colleagues, and the broader community. This involves communicating your findings, methodologies, and outcomes.

AI project management is quite different from usual project management setting since AI projects require experimentation and innovation. Innovation needs time. In an organizational setting, we are usually time-boxing a longer innovation cycle to shorter one and create a minimum viable product that generates maximum value in terms of revenue. This can be quite risky if we just ship the product due to timeline constraints. This risk needs to be clearly addressed since what would have otherwise been a successful project might turn out to be a failure. Secondly, whatever metrics used in the model development/ test phase might not actually work in real-time. In real-world/ complex scenario only real-time validation is the proof of real success.

-Define the project goals clearly. Whether it's automating tasks, improving decision-making, or enhancing user experience, knowing what success looks like is crucial. -Access to high-quality, relevant data is essential. Ensure it's sufficient for training and diverse enough to avoid biases. Data integrity, privacy, and ethical considerations are paramount. -Understand the algorithms and models being used. Each has its strengths, weaknesses, and suitability for different tasks. Choosing the right one is crucial. -Rigorous testing and validation are necessary at every stage. Evaluate models' accuracy, performance, and robustness against different scenarios.

Be prepared to adjust course based on emerging challenges, new data insights, & evolving user needs. I spearheaded an AI chatbot development project, but our rigid plan crumbled upon user feedback. By adopting an agile approach, we iteratively refined the chatbot based on real-world interactions, resulting in a user-friendly & successful conversational partner. Have open communication channels, encourage team member input, & use their expertise to overcome technical hurdles. I led a multinational AI healthcare project, but communication silos threatened to cause failure. By establishing clear communication protocols & cross-cultural collaboration, we transformed the team into a united force, delivering a life-saving AI diagnostic tool

I would add a step after defining the problem: "Wait. Validate the Problem 3 Times." 1- Ensure the problem is clearly and accurately defined. 2- Assess the problem's viability by ensuring it has a substantial market and companies interested in addressing it. Conduct a competitive analysis, learn from competitors, and pinpoint a niche problem. 3- Verify the significance of the problem. Ensure it is influential enough to prompt changes in the internal policies of your target clients, granting you access to the necessary systems and data. I've witnessed many organizations opting to develop existing products internally due to restrictions in data access and security policies preventing engagement with external vendors.

One thing I have noticed about AI is that its transformative capabilities often bring about hype and jitters. Employees in particular, deal with fear and concerns about being replaced by AI at some point in their careers. When undertaking an AI project, organizations must first secure buy-in through clear communications as a way to reduce internal reluctance to adopt an AI project.