The AI renaissance has been fueled by 2 trends
These datasets combined with the training capacity available in the cloud, are producing models that are very close to humans on various activities and in some cases even surpassing human abilities.
Almost in unison with this infrastructure revolution the AI/ML community has forged ahead with tools and frameworks like TensorFlow, PyTorch, Keras, Caffe, AWS SageMaker, Vertex AI from Google, Azure AI from Microsoft to make it easier for folks to train and build models. Startups like Weights and Biases, Fiddler.ai have given tools to the Datascientists and AI/ML experts to spend more of their time on building innovative architectures and cast their focus on a wider and wider range of problems that can be addressed by AI/ML.
As expected in these early days of the AI/ML cycle a lot of effort has been put into the ‘supply’ side of AI/ML - the MLOps to build new models. These tools are the foundation on which ‘consumption’ of AI/ML is going to be possible. As with any new technology and especially one with so much hype, the adoption or the mass consumption of AI/ML is what is going to decide whether this technology remains a niche player; like the promise of Map-Reduce, Big Data and Hadoop which was never able to make it past the few early adopters; or becomes a breakout technology like the mobile revolution that will have a positive impact on almost every person on the planet.
At the time of this writing stackoverflow has just published their developer survey of the year 2022 . To quote that survey – “Hugging Face Transformers surfaces as the most loved library while Tensorflow remains the most wanted”. These are frameworks that make it very easy to build applications that use AI. Even more impressive is this quote from the survey – developers want to build machine learning models but enjoy using pre-trained models more. This is not surprising at all because unlike the AI/ML supply side most consumers of AI/ML are not ML experts. In fact, if AI/ML has to really take off then every developer should be able to use AI/ML without being an expert at producing ML models.
Developers these days are faced with a wide range of choices when it comes to consuming “AI as API”. There are multiple vendors and startups who are offering some form of AI consumption. We at Tiyaro are on a mission to make AI accessible to every developer. We are building the AI consumption platform where ‘AI as API’ is just one piece of the puzzle for us. The choice available to developers today are
Large vendors like
New entrants like
All these offerings give the users an option to consume AI directly as API. But not all vendors are created equal. Let's go through the 5 most important things to look for to take your AI/ML ideas from aspiration to practice.
You understand your application or business needs better than anyone else. You’ve decided that you want to use AI/ML to solve your problem. So why not let your application and business expertise guide you in your choice of AI/ML consumption platform. The 5 things that we enlist here are deeply rooted in the philosophy of letting you use your business expertise to solve your problem. Let that be your guiding principle when choosing your AI/ML partner.
Your business is not static, your requirements change, your user behavior changes. So your AI/ML platform should be able to meet your current and your future needs. For instance, an online retailer may be interested in automated product attribution (a computer vision problem) today but in the near future he may want to flag fake reviews (a NLP problem) of a product. So ideally you are looking for a platform that not only gives you a wide range of choices of models. But you also want a vendor that can address all types of AI/ML problems like Computer Vision, Natural Language Processing (NLP), Time Series Forecasting, Audio, Video etc.
Well having a vendor who can solve multiple types of AI/ML problems is good. But you can't stop there. You also want to see where these models are coming from. There are vendors who only provide open source models, there are others who only support custom built private models. Ideally you would want to choose a vendor that can give you access to models from most if not all the model developers out there. Because ultimately you want to find the best model for your use case.
To summarize, you are looking for a breadth in both the types of problems that can be solved (Vision, NLP, TimeSeries etc) and the source of the models (opensource, custom, 3rd party vendors, or even your own custom models)
Here is a matrix that is useful for comparing the various platforms. By no means is this matrix complete in terms of the ML problems that are articulated on the X-Axis or the Vendors that are listed in the body of the table. The point here is that you can quickly use this matrix to evaluate any vendor of your choice and see how he compares in terms of the breadth of their offering.
|Vision||NLP||Timeseries||Others (License Plate, Landmark detection, Emotion Detection etc)|
|OpenSource Models||HuggingFace, Tiyaro||HuggingFace, Tiyaro||Tiyaro||Tiyaro|
|Custom Models||Rekognition, Google Vision, Clarifai, Tiyaro||Rekognition, Google Vision, Clarifai, Tiyaro||Tiyaro||Rekognition, Google Vision, Clarifai, Tiyaro|
|Models from 3rd Party ISVs||Tiyaro||Tiyaro||Tiyaro||Tiyaro|
So you have a problem that you think you can solve with ML. How do you go from your business problem to a model API that you can use to solve the problem? You have 2 options.
Having a listing of 10s or 100s of models that can solve your problem is a great place to start. But ultimately you want to find that ‘one’ model and the more easily and quickly you can make that ‘informed’ choice the sooner you can get back to solving your problem.
Given the large number of open source and custom 3rd party pre-trained models that are available to the developers there is a high likelihood that you may find the model that works best for you at this step (If you are thinking about the what if I need a custom model after all evaluations, we are going to cover that in the next step). And only one thing remains: Start consumption. After you have evaluated and chosen the right model the next step in your evaluation is how quickly you can start consuming it. We are talking about vendors serving models as API here so how complex could it be. Well, you need to put all the various factors together. Many times it is not just accuracy of the model that is important, sometimes good enough is good enough. So the ability to put all static evaluation and dynamic evaluation together, weighing the pros and cons of accuracy vs cost, or accuracy vs latency, transparency of pricing. You want a vendor who gives you absolute clarity on the pricing, the accuracy, the latency, the comparative data from multiple likely models and a generous free tier so you can quickly make ‘informed’ decisions. A vendor that can make these things simple allows you to start consuming models “instantly”. You went from a business problem to exploring models to evaluating them in minutes and now you put it all together with the pricing, latency, cost and within minutes you are ‘consuming’ AI.
As much as developers love pre-trained models. The true nature of AI/ML applications is such that you may need to customize certain models for your use case. Vendors are trying to solve this problem in multiple ways.
AI/ML is at the cusp of mainstream adoption. Developers today can start consuming AI/ML as a service and within minutes you can go from a business case to a working AI/ML model. With the wide range of offerings in the market following the basic guidelines outlined in this blog should give you a good framework to make a choice that is right for you.