You’re about to start a new machine learning project. What are the best platforms to get started?

I'm excited to speak about diving into the clouds! With your background, you're perfectly poised to explore platforms like AWS, GCP, or Azure. Start by choosing a platform, setting up your environment, and diving into tutorials. Remember, simplicity first, then dive deeper. Community support will be invaluable. Keep pushing boundaries! My recommendation is to consider the different security policies for different cloud platforms. Another important point is vendor lock-in and the future scalability you need to consider.

Remember, there's a trade-off between the ease of using pre-built tools and the flexibility of custom solutions. You must assess your specific needs and resources. Additionally, consider data governance and privacy regulations, as cloud platforms often involve data transfer across different jurisdictions. Keep also in mind the long-term implications of vendor lock-in and the adaptability of the platform to future technological advancements.

OK, In my personal opinion, it is important to identify your strength i.e. are you comfortable with coding or not? Nowadays coding is not essential in the primary stage if your main focus is to understand how machine learning works. Many tools may help you in that such as MATLAB, RapidMiner, AWS, etc. However, if you want to optimize and design your model as per your need, then following a coding platform such as Python will be easier. But I will suggest to understand the requirements of your project first, and then it will be easier from your side to choose the most efficient platform.

To get started on a new ML project: - Beginner: you don't need the cloud. Most likely you are not going to build anything meaningful, unless you are the Mozart of ML. You will understand better the pain of running out of memory and disk on your local Macbook. - Intermediate: Sagemaker is a good end-to-end solution that works. Google Collab is good for any side project. - Pro: Do whatever your engineer team will take it from there.

In my experience, cloud platforms like AWS, GCP, and Azure revolutionize machine learning with vast resources and tools. AWS, in particular, shines for me, offering a rich suite of services such as SageMaker, Rekognition, and Lex. It stands out with its seamless integration, scalability, and security, making complex tasks more manageable. While GCP and Azure also offer powerful features, my affinity leans towards AWS for its robust and intuitive environment. However, one must navigate these platforms judiciously, mindful of costs and potential vendor lock-in. Despite these considerations, the transformative potential of these cloud services, especially AWS, is undeniable.

Some less advanced platforms which mainly target data analytics for business purposes are Alteryx and Microsoft Power BI. Below is a short description of each. Alteryx is a data preparation and analytics tool designed for ease of use, providing a visual interface for data blending, cleaning, and analysis. It's often used by data analysts and business users. On the other hand, Microsoft Power BI is a business analytics service that provides interactive visualizations and business intelligence capabilities with an interface for creating and sharing reports and dashboards.

Before choosing a local platform, assess your team's technical proficiency and the project's resource demands. Are you equipped to handle the setup and maintenance, or would the streamlined, scalable nature of cloud platforms be more beneficial? Your choice should align with your team's expertise and the specific demands of your machine learning project, balancing the need for control and customisation with the practical considerations of resource availability and technical support. Tools like Jupyter Notebook, Spyder, and RStudio facilitate a high degree of customisation and hands-on management, but they also require a more in-depth understanding of the underlying technology and dependencies.

In my journey, local platforms like Jupyter, Spyder, and RStudio have been pivotal for offline machine learning. Jupyter is versatile, supporting languages and frameworks like Python and TensorFlow. Spyder, tailored for Python, integrates well with libraries such as NumPy and Pandas. RStudio excels in R, offering robust package support. These platforms offer privacy and customization but come with challenges like resource limits and maintenance. Each tool, with its unique strengths, forms a crucial part of my machine learning arsenal, balancing flexibility with the intricacies of local environment management.

I always say "think local, go global". This applies for beginners and experts. Beginners will understand better on local and experts use the local to code and ssh to upload code and deploy. On stack you can use whatever. Even Spyder as an IDE that LinkedIn points out.

There are several platforms and technologies you may take into consideration while working on machine learning projects locally (on your own computer or within the infrastructure of your firm). The tools you use when working locally will vary depending on your tastes, the demands of the project, and the particular tasks your machine learning project entails. A lot of machine learning and data science professionals combine these technologies to provide a smooth development environment.

Google Colab is the Best platform to start machine learning Project. Benefits - ??Easy mounting of Google Drive & Store everything in same. ??Manual Configuration of Run Time T4 GPU ??? Git Hub Push functionality to respective Repository ?? Easy Share & File Privacy ?Colab AI helps Beginners to Start Machine Leaning Journey.

As noted by numerous experts, the Google Colab Notebook stands out as a top-tier and user-friendly platform. While running machine learning algorithms on cloud services is an option, it requires some configuration and may entail higher costs, time, and effort.

Github, Google Colab, Kaggle, AWS are widely used online platforms that provide a vast number of open-source machine learning projects and datasets. They offer an excellent platform to practice skills, collaborate and learn from other data scientists.

These platforms are particularly beneficial for those who lack the computational resources or technical expertise to configure local environments. However, while they offer convenience and collaboration opportunities, reliance on internet connectivity and potential issues with data privacy and control should not be overlooked. Projects dealing with sensitive data might find these platforms less suitable. Moreover, the performance can be contingent on network speed and platform load, possibly affecting the consistency of results.

Navigating online platforms like Google Colab, Kaggle, and Binder has reshaped my machine learning practice. They eliminate setup hassles, providing a smooth interface, and access to robust resources like GPUs. Colab and Kaggle excel with integrations for seamless data handling, while Binder transforms GitHub repos into interactive notebooks, fostering collaboration. These platforms are a boon for convenience, collaboration, and accessibility in machine learning projects. However, they come with their own set of challenges like internet dependency and potential latency. Despite these, the efficiency and collaborative potential they offer are immensely valuable.

Choosing the platform depends on a few criteria based on scope, vision, requirements, objectives, outcome, cost, and time. Nowadays, the project-to-project approach of choosing the platform differs. Cloud: It's the best platform if you want to achieve your objectives in a short time (quick to market, offers rich features, well-documented), but it can be expensive depending on the scope Local Platform: Project is very well defined with limited scope and requires less infrastructure to run on. It gives flexibility to create custom solution, but manual efforts to support and maintain will be drawbacks Online: involve quick results or proof of concept, confirming that objectives are achievable, would be an ideal way to opt for online platforms.

Besides the choice of platforms, consider the broader ecosystem of a machine learning project. This includes the availability and quality of data, the expertise of your team, and the specific goals of your project. For instance, a project with a rich dataset might leverage the advanced analytics of cloud platforms, while one with limited data might benefit more from local platforms with custom-built models. Also, the team's skill set plays a critical role: a team well-versed in Python and data science tools might find local platforms more flexible, whereas teams with less technical expertise might prefer the user-friendly nature of online platforms.

It depends the knowledge you have on setting up the environment , creating the models , tweaking and testing it. Also the complexity of the project. When I had to build an object detection model using cnn’s I chose the following path. Initially I learn led about deep nn and cnn on local environment (Spyder and Vscode Python) that made me also learn about configurations and when I had my prototype ready, since it required a lot of cpu/gpu power to run on my desired output level I turned to Google colabs (in my opinion the best online platform). Finally I turned back to my local env to try to optimise the model to run on low quality hardware (meaning to optimise the software itself).

Before everything else, think of the following: don't make your decision based on a hype. FOMO is the main reason for entrepreneurs to actually miss out. Steps: 1. Do I know what I am doing or only I think I know? Most probably you only think you know. If you really know what you are doing, make a simulation on your own. Do not involve anyone else. 2. If you know what you are doing, you don't need this guide. You only need confidence. 3. If you only think you know what you are doing, look for a consultant. 4. Work with the consultant to envision a solution. 5. Challenge the solution parts with your own SMEs. For example, infrastructure SMEs can tell you if a local solution can provide the performance KPIs. 6. Now, you have the solution.

Consider using foundational models. These are versatile models pre-trained on large datasets that leverage transfer learning. By using an open-source foundational model in the general field, the model can be trained for the specific application with less training data and setup. These models will also often publish the curated training dataset which can be vetted and expanded upon. Often they contain more data that has already been cleaned and labeled than can be expected in short time frames when starting form scratch: optimal for prototyping and short turn around times. These are more user-friendly and can be the optimal choice based on the design requirements.