How APIs Breathe Life Into ML Organisations
“API monetisation and API first strategies have become a new normal with businesses with digital maturity.” Last year’s pandemic catalysed digital maturity across organisations. The niche markets found even more niche business opportunities, thanks to the widespread adoption and development of APIs (Application Programming Interface).  In their most basic form, APIs are doorways between two software applications and become extremely powerful when tailored to the needs of the developers. Web, mobile and automation are some of the key applications powered by APIs. According to a report by Google Cloud, API programs are the core drivers of digital transformation by playing a significant role in digital experiences, business operations, innovation, and growth. Current State Of API Economy (Image credits: Google Cloud) Companies around the world possess valuable data ready to be capitalised. All they need are the services that can bridge the gap between customers and third parties. APIs fit right into this mix. For instance, the banking sector has witnessed a tremendous revolution with the advent of fintech products. The infrastructure behind the payment gateways are powered by the APIs like those of Stripe or Razorpay. These fintech API providers are multi-billion dollar companies today. Machine learning-based API service providers are next in line to take the markets by storm. “Databricks API  supports services to manage clusters, instance pools, libraries, tokens, and MLflow models. Databricks is currently valued at $28 billion.” For example, last week, Databricks, a company that offers unified platform services raised $1 billion that rocketed its market value to $28 billion. Though AWS too offers Spark services, Databricks’ Spark services seem to have an edge over them. They offer additional customisations while combining the synergies of top players to serve an user. According to an Apigee survey, AI- and ML-powered API security and monitoring solutions used for anomaly detection and security analytics grew 230% year-over-year between September 2019 and September 2020. When easily reusable, APIs let developers modularly combine, and recombine functionality and data for new uses, with virtually no marginal cost for each additional use of the API. If one developer builds a new application by leveraging an API that looks up store locations, another developer can leverage the same API for another application without the enterprise incurring any additional overhead. The APIs will (source: Gartner): Make it easier for data scientists to find and choose from the huge variety of available algorithms, experiments, datasets and solution accelerators.Enable organisations to build advanced analytics solutions in a faster way.Address the skill gap in advanced technology.Help commercialise their solutions easily.Enables ease of ML model choosing. (API traffic by domain in 2020. Source: Google Cloud) APIs also allow the organisations to take smart decisions by providing details of the product consumption at the user level, which in turn can be used by the developers to enhance the end product. This sounds like every other business strategy, but APIs make it more accessible. It helps them understand the value of an organisation’s digital assets. Beyond helping enterprises, writes Bala Kasiviswanathan of Google Cloud, API analytics can help both IT and business leaders refine the KPIs they use for analytics. “If an API becomes popular with developers in a new vertical for example, that may persuade the enterprise to focus on KPIs like adoption among these specific developers, rather than on overall adoption,” said Bala. AI Through API In 2019, machine learning as a service (MLaaS) raked in an estimated $1 billion and is expected to grow to $8.4 billion by the end of 2025. The success of these services can be traced to the customised APIs. For example, Google’s prediction API, can be used to classify an image for $0.0015 and even perform sentiment analysis on text for just $0.00025 only. The user gets to avail Google’s state-of-the-art tech and Google gets compensated for its research. APIs can act as conduit between innovation and incentives. No matter what kind of machine learning product you are building, it eventually boils down to whether the customer can deploy these models with just a few clicks. APIs help do this. Research labs like OpenAI resorted to releasing APIs to commercialise their exotic research. The much talked about language model, GPT-3 was tapped through these APIs and was leveraged to set up many million dollar startups. Now, customers can access state-of-the-art ML models without the headaches of training from scratch; GPT-3 training that cost OpenAI over $4 million. If you are an API service provider, then here are a few takeaways from OpenAI’s success: The team at OpenAI made sure their API is built to be simple and flexible. OpenAI would terminate API access if the users use it for applications such as harassment, spam, radicalisation, or astroturfing. AI, unlike other SaaS domains, can find malicious players easily (think: someone using GPT-3 to write fake speeches for Presidents that can start a war). For AI research to survive, there needs to be a commercial twist and APIs sit at the heart of this strategy. Also Read: This Framework Leads to 50% Cost Reduction From ML API Calls Going forward, more Cloud and AI  based services will be offered as API-centric services. Services like AWS Lambda are designed for producing exclusively API/event-centric application services. According to Gartner, adoption of API-centric models for SaaS delivery is expected to increase and the API economy has already established itself as a precursor of digital transformations and the primary way to grow an ecosystem.
“API monetisation and API first strategies have become a new normal with businesses with digital maturity.” Last year’s pandemic catalysed digital maturity across organisations. The niche markets found even more niche business opportunities, thanks to the widespread adoption and development of APIs (Application Programming Interface).  In their most basic form, APIs are doorways between two […]
The much talked about language model, GPT-3 was tapped through these APIs and was leveraged to set up many million dollar startups.
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["machine learning", "OpenAI", "AI", "sentiment analysis", "AWS", "ML", "RAG", "anomaly detection", "analytics", "MLflow"]
["AI", "machine learning", "ML", "analytics", "OpenAI", "MLflow", "RAG", "anomaly detection", "sentiment analysis", "AWS"]
I have a master's degree in Robotics and I write about machine learning advancements.