The field of Machine Learning and AI has been seeing an unprecedented rise with an estimated guess that this industry will have a market cap of around $15 trillion by 2030. The companies that are incorporating AI systems into their workflow are rising and will continue to do so as they are seeing their benefits such as costs and time savings and increased efficiency. 

Due to such unprecedented demands, the salaries of data scientists, data engineers, and ML engineers have seen a tremendous rise within the last decade and the demand for them is continuously increasing still. But the talent pool is limited as most of these technologies did not exist a couple of decades ago and the education system is still catching up with providing the required talent. According to one report published by the research firm Gartner, there will be a huge gap between the demand and supply of data scientists with the required expertise and the experience, making it the most well-paid and in-demand job with average salaries reaching around $150k per year. Huge companies can afford such people and teams to work for them in implementing AI solutions into their products but other smaller ones find it extremely hard to hire and maintain such talent with them for longer durations. 

Even if you have managed to build such teams with competent people, the actual task of ML and data science is still going to be cumbersome as it involves a lot of things. Finding the right data, augmenting it such that it can be analyzed, finding the right metrics and features that the ML model can use, building ML models that perform the best, tuning and creating ensembles of them to get the best accuracy and then finally deploying and constantly monitoring the model to keep it alive and working, is a typical workflow that such teams have to go through. All this can easily take months to get the model ready and still if it doesn’t solve your use case problems or the stakeholders are not satisfied with its explainability of it, the whole process has to be repeated from the data part and the entire cost required sublimates into the air wasting a lot of time in the process.  

Although the term is data science, the science or experimentation part of it is completely underused as the cost to build and train such models is high. Again, huge companies can still at least afford to run such chance-based experiments that do not always lead to success, small and even mid-sized companies cannot go into that loop of constant iteration for ML. Hence, the only best option for them remains the DSML platforms. 

What are DSML platforms? How can they help? 

DSML platforms enable businesses to incorporate AI and ML-based solutions into their traditional workflow with a fraction of the cost and huge savings on time. These platforms help in setting up your data science workflow with ease and also without the required domain knowledge. They provide an all-in-one (not all of them) solution to help you up with the data preparation part to the model deployment part, so you have a single platform to work on your data science problems. 

Hence, the hassle of setting up a data science team disappears and a huge cost is saved. Also, training and experimenting with small models with limited data and testing them out before beginning to train actual models can help in building the right models and can again save time in months and help with computational cost savings required to train such models.  

Selecting the best one for yourself 

The platform that can work the best for you can vary a lot based on your use case and the problems that you are trying to solve. It also depends on who will be using this platform in your company and what is their previous expertise in ML.  

If you already have a data science team set up and you want to incorporate such platforms to help them, then you can go with platforms that provide more of a code-based approach which offers flexibility and allows for tweaking and testing your models with code. Here, the person operating should be a senior data scientist with years of experience building and deploying ML models. The coding language can also matter but mostly Python has and will always be preferred for ML-related tasks but still, that should be a point to consider before deciding on any of such platforms. 

If you are a company with a very small/no data science team or a team of business analysts with no coding knowledge or software engineers who have no expertise in ML, then the options that you should go for are the platforms providing a low code or even a no-code approach. Such platforms consider user comfortability and provide an interface where very limited or even no coding is required. You can ingest data from multiple sources, pre-process and create visualizations to generate insights, and build/train models within a few clicks. Even business executives with no tech expertise can dive deeper within the data and train models to see results with ease and focus more on the business side of the whole process instead of wasting time figuring out the complexities of code and machine learning. 

If you are someone just wanting to try out ML in their workflow before setting up and investing in a full-fledged data science team, ML platforms are the best way for you to analyze, build and train ML models and see how they fit into your conventional workflow before hiring a team and figuring it out later. It will also help you in knowing the right tech positions for you to fill while building the data science team, improving your team’s efficiency, and saving a lot of costs in hiring. 

Finally, if you are a very small company, a social institute, or a non-profit organization that cannot afford to spend a lot of money but still need some AI-based solutions at your disposal, you fellas can also look into such systems that have more of a no code approach and try implementing them into your workflows and play around with them to get a solution to solve any minor problems that can be solved with AI. Some platforms also provide a Frugal AI approach that uses a very limited amount of data to generate similar insights and model performances. So, if sourcing the data is a problem then you can also opt for such platforms. 

So, what should one look for before making the final decision? 

1. Code approach: See if the platform provides a code approach that is suitable for your team’s expertise. Selecting a code-heavy approach might be difficult for non-programmers and on the other hand a no code approach will be inflexible and too easy and non-functional for data scientist or ML engineers.
2. ML models: Look at the pre-built models that they provide and see if they can be trained for your use case and your data. Talk with such companies beforehand to find out if they provide solutions and ML models for your particular use case.
3. AutoML: AutoML helps with the entire ML pipeline, from data engineering to model development, and more AutoML means more automation for the data science-related workflow and more of a click and button-based automatic solution.
4. Explainability: See if explainability has been included within the solution so that you get proper explanations for your model’s output so that any biases can be detected and eliminated beforehand to help you deploy models which are fair, robust and reliable.
5. Pricing: Pricing plans should also be a point of consideration before selecting the right platform. Look for their subscription plans and decide on a platform that suits your needs and budget the best.

Who does ANAI aim to serve? 

ANAI is an all-in-one ML-based solutions platform that manages the A to Z of AI, from data engineering to explainability. We offer a solution that focuses more on a no-code-based approach with a low code solution to be released in the future. ANAI’s AI engine easily outperforms and provides the best performance for any AI-based system.  

AutoML is also an integral part of ANAI, helping you with data ingestion from more than 100 plus data sources, automatic ML-based data wrangling, exploratory data analysis, automatic data pre-processing, and cleaning. Also, you get 150 plus pre-built ML Models to choose from (with more being added daily), automatic tuning and testing for your model, and an eXplainable AI (XAI) and Responsible AI (RAI) based solution for explainability of your models and building a trustable and accountable AI. 

 AI ML Platform Selection Criteria, Criteria to select AI Platform, How to choose AI platform

 

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