What are the most useful tools for managing AI software development?

The recently launched Canva Magic Studio offers time-saving features for creating diagrams and visuals. Nat.Dev is a great tool for experiencing different large language model and trying out different parameters. Nat.Dev is free to use, but I found it worthwhile paying the subscription to access more powerful LLMs.

As you visualize your AI, I have found it useful to think about what are going to be downstream requirements on the data/model/architecture. For example, the AI will need to be explainable, and have a level of transparency some time in the future. Given that the actual design (and real time inference pattern) of a neural network is difficult (for humans) to visualize and understand, good "diagrams" of the AI's design (data/model/architecture) can be the first line of defense when explainability and transparency is required of the AI. Think about the visual design assets as a part of the compliance and trustworthiness of the AI in the future.

AI design tools can help you in the following ways: Visualization: Makes it easier to communicate ideas and concepts with your team and stakeholders. Prototyping: Tools like Balsamiq allows with prototype to gather feedback and make improvements before development begins. Experimentation: TensorFlow Playground's platform for experimenting with neural networks, helps understand how different configurations affect AI model performance. Collaboration: Many tools offer collaboration features, enabling team to work together on planning and design regardless of their physical locations. Efficiency: Using specialized AI design tools can save time and effort compared to manually sketching or coding prototypes and diagrams.

Designing AI systems is a blend of creativity and precision. Useful AI design tools include platforms like Figma and Sketch for creating user interfaces and wireframes. These tools facilitate the visualization of the AI's interaction with users, helping designers and developers align their efforts. Additionally, tools like Lucidchart or draw.io can be invaluable for creating flowcharts and diagrams to map out the AI's logic.

It is a software that allows users and businesses to build powerful AI tools to streamline various tasks with amazing speed and efficiency. Some tools are as: Google Cloud AI platform,Microsoft Azure AI,IBM Watson etc.

In many scenarios, AWS Sagemaker Studio is a good one-stop-shop for AI developments. It provides a single web-based visual interface where developers can access purpose-built tools to perform all machine learning development steps, from preparing data to building, training, and deploying ML models. In recent months, Sagemaker also introduced the ability to deploy and fine-tune large language model.

When selecting tools, I have found that it is important to get a sense of whether you are going to be a "consumer" of AI or a "producer" of AI. If you are going to consume, rearrange, and tune existing models, then the tools discussed thus far are good starters. If you are going to need to write, train and produce new models of your own - where you might be taking more of a hands on approach in the data mastery and mathematics, you will want to think about tools (programming languages to "produce" AI) such as R, C++ and in some cases RUST. What tools you use will also change as you think about what workloads you prefer to run locally on your machines, vs. on the cloud for cost optimization.

If you are building LLM apps, Vellum is worth checking out. (I have no relationship with them.) They provide several convenience features to help speed development and testing. LangChain is also becoming a standard in LLM development.

In the realm of AI development, selecting the right tools can make or break a project. Python remains the lingua franca of AI, with libraries such as TensorFlow and PyTorch being essential for deep learning. Jupyter notebooks offer an interactive environment for experimentation and code sharing. For version control, Git and platforms like GitHub are indispensable for collaborative development.

Azure Machine Learning Studio offers a wide variety of ways to train, evaluate, and deploy ML models. Additionally, it is rapidly evolving to be a centralized location for generative AI models from a variety of providers including OpenAI, hugging face, and meta. Finally, if you look at the improvements coming with previews for Promptflow, Azure is truly a world class place for AI development.

In the intricate dance of AI development, validation tools play the pivotal role of a vigilant choreographer. They ensure the AI performance is not just impressive but impeccable. Tools like MLflow, Sklearn, and SHAP are the unsung heroes, ensuring our AI models don't just work but work right. They're the guardians at the gate, catching biases and errors before they manifest. In a world increasingly reliant on AI decisions, these tools are the safety nets, ensuring that the AI systems we deploy are both powerful and trustworthy, striking the right balance between innovation and integrity.

This area of "tooling" is the most dynamic of other areas, and changing more rapidly . There is no real "marketplace" to search and discover these as yet (such as model marketplaces like cohere/huggingface etc.) so this is more of a git/reddit approach for now. This is one of the areas where you must encourage the data scientists and engineers in the project to stay vigilant for new tools, and resist they urge to use what they know. This is the space that is moving fast, because it is where most of the optimization of the ecosystem still resides. For example IBM's Fairness 360 Toolkit (AIF360) is a great tool, but many data scientists and engineers will not know of it or use it "because its IBM". This is an area to be vigilant.

Validation is the gatekeeper to ensure AI systems perform as expected. Tools like TensorBoard provide visualizations for monitoring model performance during training. Testing frameworks like PyTest or TensorFlow's tf.test help automate testing procedures. Moreover, consider tools like MLflow for managing experiments and tracking model versions, making validation more systematic and reproducible.

AI is a pivotal force driving business transformation. Understanding and leveraging AI deployment tools is essential for modern business leaders. Tools such as Docker, Kubernetes, and Prometheus facilitate efficient deployment and management of AI in production. What are the benefits? Enhanced operational efficiency through automation, a competitive edge over late AI adopters, and heightened revenue potential by tapping into AI's ability to analyze market dynamics and consumer preferences. Embracing these tools can significantly upgrade business operations, placing firms at the forefront of innovation.

The hypervisor providers are pairing up with hyper scalers to do faster for AI, what they did not do fast enough for cloud - and - that is to package "deployment" and management as a part of a standard cloud offering. While Docker and Kubernetes dominate the space, there are new cloud services popping up from AWS, Google and Azure that will automate and "clickify" a lot of the declaring and declarative-driven tools in market. Watch for this area to be insanely simplified, and democratized to talents that do not need to know how to write sophisticated declarative instructions using a keyboard, but instead just use a mouse.

Once an AI model is ready for the real world, deploying it effectively is crucial. Docker containers and Kubernetes orchestration simplify deployment across different environments. Cloud platforms like AWS, Azure, and Google Cloud offer services for scalable and reliable AI deployment. Tools like Apache Kafka can facilitate real-time data streaming for AI applications that require continuous updates.

Leverage MLOps tools available in the market to make the pipeline more robust and achieve the required speed. Amazon Sagemake for example, if you are on AWS, provides you a range of pipeline specific integration (CI/CD) and operationalisation capabilities. Databricks comes up with similar tools and aligns with MLFlow Microsoft Azure Machine Learning provides end-to-end data and model management. Enterprise tools like Data Robot, Domino Labs are great example tools can be leveraged.

Azure Machine Learning Studio allows you to deploy and monitor machine learning models to containers, kubernetes, and web service endpoints for batch or real time predictions. It also supports the automated maintenance of these endpoints to support MLOps workflows.

AI collaboration tools are transforming the business landscape. MS Teams, for example, enables AI assisted communication. For business leaders, mastering these tools isn't just a tech endeavor; it's a strategic move to drive growth and stay ahead in this dynamic industrial era. Their impact is tangible. By integrating AI collaboration tools, businesses not only streamline operations but also secure a decisive competitive edge in the market.

My team uses Airglass daily, so I am biased. Airglass enables you to stand behind your content/screen as a group and collaboratively point at things displayed on the screen with your hands/fingers. It also allows you to collaboratively draw over things displayed on the screen with your mouse or touchscreen. Given the complexity of discussions that are needed to discuss an AI project, where text, illustration, and visualization content have to be collaboratively discussed, I've found the Airglass to improve the communication, sharpen the comprehension, and increase the retention of teams working on AI.

Collaboration is the cornerstone of successful AI software development. Communication tools like Slack and Microsoft Teams foster real-time collaboration, while project management tools such as Trello or Asana help teams stay organized. Collaboration platforms like Microsoft Azure DevOps or GitLab provide integrated solutions for version control, continuous integration, and automated pipelines.

Two commercial / enterprise tools I would recommend: DataRobot and Domino AI DataRobot is one of the leading collaboration tool that offers a unified AI landscape that monitor and govern all of our AI workloads, models, and performance - including a full observability and governance, and operational costs. Domino AI is another excellent tool, and one of my favourite. It is your Swiss army knife for not only your entire data loading, model training, and deployment; it is easy to use and integrated with MLOps capabilities. Both these tools support a wide varieties of collaborative environments across a spectrum of personas -data engineer, ML Engineer, data scientists, devops engineers, and many others.

Collaboration tools should ideally enable collaboration between different personas involved in the lifecycle, e.g. collaboration between ML engineers and ML scientists, collaboration between ML scientists and project management. Ensure that the right tools exist for each persona, for example scoping ML science work on Jira can be quite inefficient.

Managing AI software development effectively requires a combination of tools to streamline the process. * Version control systems like Git for tracking code changes and enabling collaboration. * Integrated development environments (IDEs) with AI-specific plugins for efficient coding and debugging. * Project management tools like Jira or Trello to plan and track tasks. * Containerization platforms like Docker for consistent deployment across different environments. * Collaboration and communication tools like Slack or Microsoft Teams to foster teamwork and knowledge sharing among developers. * Continuous integration and continuous deployment (CI/CD) pipelines for automated testing and deployment of AI software.

The proliferation of AI tools in today's technological landscape is nothing short of remarkable. Every day, new algorithms, applications, and platforms emerge, each promising to revolutionize various sectors, from healthcare to finance to entertainment. This rapid expansion underscores the transformative power of AI and its potential to reshape industries and redefine paradigms. Learn and keep yourself up-to-date to adapt to the new changes.

While tools are essential, it's crucial not to lose sight of the broader context. Understand the problem domain thoroughly before selecting tools. Moreover, consider the ethical implications of AI development and ensure your tools support responsible AI practices. Compliance with data privacy regulations like GDPR or HIPAA is non-negotiable. Furthermore, documentation is often overlooked but vital. Tools like Sphinx for Python or Docusaurus for creating documentation can save countless hours of confusion. Lastly, fostering a culture of continuous learning is crucial. AI evolves rapidly, and staying up-to-date with the latest tools and best practices is a must.

Use the leading tools for sure, but keep an eye out for new tools, and ask your team to participate in their beta programs. We are still using software development tools for intelligence development, and there is new toolset that will emerge. The new toolset will deliver a strong competitive advantage. We have not hit the "IDE moment" of AI as yet, so I advise my team to keep an eye out for new tools, so we can get the competitive advantage as soon as it is available.

Some other types of tool for ML developers and engineers are crucial to protect from adversarial attack: Poisoning Attacks, Evasion Attacks, Model Extraction - by means of injecting deceitful data / inputs. Amazon Sagemaker Model Monitor and Amazon SageMaker Debugger helps us detecting adversarial inputs injections. We should keep an eye on and put controls in place with implement of appropriate security measures - such as input validation and user authentication. Amazon Bedrock has some cool built-in security features to protect against prompt injections. Also we must think about AI Governance and Guardrails. NVIDIA's NeMo is a great Guardrails framework for Gen AI models. DataRobot and Domino for ML Governance tool.