Considering 'advanced' research in AI and related technologies? Here's a checklist to guide you...(Part 2)

(Not exhaustive, but a great launching pad)

Note for Scholars and Researchers: The following checklists are intended as a foundational guide for your planning process. It is recommended to create a personalized, adaptable living document that will function as a roadmap throughout your research journey.

Checklist for Leveraging AI Technologies and Infrastructure

Phase 1: Foundations and Landscape

AI Technology Overview: Define and categorize different AI technologies (e.g., machine learning, deep learning, NLP, computer vision, robotics) and their applications.

Cloud Computing Platforms: Explore major cloud platforms like AWS, Azure, GCP, and understand their AI services and tools (e.g., ML APIs, pre-trained models, data lake services).

AI Hardware Landscape: Learn about specialized AI hardware (e.g., GPUs, TPUs) and their role in accelerating AI workloads.

Emerging AI Trends: Research new and upcoming AI technologies like quantum computing, neuromorphic computing, and explainable AI.

Industry Applications of AI: Analyze how AI is transforming various industries (e.g., healthcare, finance, retail, manufacturing) through case studies and reports.

Phase 2: Deep Dive into Specific Technologies

Machine Learning as a Service (MLaaS): Understand the concept and offerings of MLaaS platforms, including model training, deployment, and management.

Deep Learning Frameworks: Choose a popular deep learning framework like TensorFlow, PyTorch, or MXNet and learn its basic functionalities.

Pre-trained AI Models: Explore the availability and applications of pre-trained AI models for various tasks like image recognition, text analysis, and speech-to-text.

Computer Vision APIs and Tools: Research computer vision APIs offered by cloud platforms and third-party vendors for use in your advisory services.

Natural Language Processing Libraries and Tools: Familiarize with NLP libraries like NLTK, spaCy, and explore NLP tools for sentiment analysis, topic modeling, and chatbot development.

Phase 3: Hands-on Experimentation and Practice

Build a simple AI application: Utilize available resources and tools to develop a basic AI-powered solution like an image classifier or chatbot prototype.

Experiment with cloud AI services: Try out MLaaS offerings, deploy pre-trained models, and experiment with different cloud tools to gain practical experience.

Participate in AI hackathons and challenges: Compete in AI-focused events to push skills, learn from others, and discover new technologies.

Contribute to open-source AI projects: Get involved in open-source projects related to the area of interest to contribute and learn from the community.

Attend AI workshops and conferences: Network with other AI professionals, learn from experts, and gain insights into industry trends at relevant events.

Phase 4: Building Expertise and Skills

Choose a niche area of AI expertise: Specialize in a specific AI technology or industry application to become a trusted advisor in that domain.

Develop consulting skills: Learn about client engagement, project management, communication, and problem-solving techniques relevant to AI consulting.

Research the competitive landscape: Analyze existing AI advisory services, identify gaps in the market, and define unique value proposition.

Build brand and network: Create a professional online presence, actively engage in AI communities, and connect with potential clients and partners.

Develop case studies and success stories: Showcase expertise and past projects to build trust and attract potential clients.

Phase 5: Continuous Learning and Adaptation

Stay updated with the latest advancements: Regularly follow AI news, research papers, and industry reports to stay ahead of the curve in your chosen field.

Expand knowledge beyond technology: Develop understanding of business needs, regulatory considerations, and ethical implications of AI.

Refine approach based on client feedback: Continuously improve by incorporating feedback and adapting to evolving needs.

Collaborate with other AI professionals: Partner with complementary experts to offer comprehensive advisory services and expand your reach.

Always be learning and adapting: Embrace your continuous learning journey in the dynamic world of AI to stay relevant and successful.

We work with researchers and development teams to investigate and build context related use cases, user stories, checklists and testcases (Specialized in automation, regression, UAT) helping them understand the coverage and visibility of the project requirements with focus on things that needs to be done and things that are not applicable. To achieve desired results within the time frame, defining and having insights on "What is not applicable" is very crucial to avoid scope creep and unnecessary research. 

We support Universities, Doctoral Students, Startup Companies, MNC's & Government Departments building AI teams. Key projects include Gaming, AI Development Projects, AI Integration Projects, Cloud Software's, Cloud Software Suites, Mobile Apps & Legacy Software Migrations.

Collaborate with us to have a different eye to support the planning and development efforts.