From very limited usage in the business world before 2012, machine learning dependency has gone up exponentially since the boom. Today there are 9k machine learning startups and companies according to Crunchbase. Here are the top machine learning start-ups that aim to excel in 2022. Algorithmia – Algorithmia's expertise is in machine learning operations (MLOps) and helping customers deliver ML models to production with enterprise-grade security and governance. Algorithmia automates ML deployment, provides tooling flexibility, enables collaboration between operations and development, and leverages existing SDLC and CI/CD practices.

Algorithmia enterprise invented the AI Layer. Algorithmia's serverless infrastructure is custom built to host scalable AI and machine learning models and advanced algorithms. It provides developers the ability to turn algorithms into scalable web services with a single click. Afterward, application developers can incorporate the algorithm into their applications with under ten lines of code. Founded in 2013, Algorithmia hosts the web services, makes them discoverable, and enables algorithm developers for usage.

"What good is an ML model if it isn't fast? Having machine learning in a company's portfolio used to be an investor magnet. Now, the market is bullish on MLaaS, with a new breed of companies offering machine learning services (libraries/APIs/frameworks) to help other companies get their job done better and faster. According to PwC, AI's potential global economic impact will be worth $15.7 trillion by 2030. And, as interests slowly shift towards MLOps, it is possible that these companies, which promise to scale and accelerate ML deployment, might grab a bigger piece of the pie. Last week, OctoML raised $28 million. The Seattle-based startup offers a machine learning acceleration platform built on top of the open-source Apache TVM compiler framework project. The $28 million Series B funding brings the company's total funding to $47 million. For OctoML's CEO, Luis Ceze, there is still a significant gap between building a model and making it production-ready. Between rapidly evolving ML models, wrote Ceze in a blog post, ML frameworks and a Cambrian explosion of hardware backends makes ML deployment challenging. "It is not easy to make sure your model runs fast enough and to benchmark it across different deployment hardware.

Enterprises accelerated their adoption of AI and machine learning in 2020, concentrating on those initiatives that deliver revenue growth and cost reduction. Consistent with many other surveys of enterprises' AI and machine learning accelerating projects last year, Algorithmia's third annual survey, 2021 Enterprise Trends in Machine Learning finds enterprises expanding into a wider range of applications starting with process automation and customer experience. Based on interviews with 403 business leaders and practitioners who have insights into their company's machine learning efforts, the study represents a random sampling of industries across a spectrum of machine learning maturity levels. Algorithmia chose to limit the survey to only those from enterprises with $100M or more in revenue. Please see page 34 of the study for additional details regarding the methodology.

Algorithmia is today debuting new reporting tools which aim to solve machine learning (ML) governance challenges. Research conducted by the company found that the number one challenge organisations are facing with their deployments is governance. "We're still in the early days of ML governance, and organisations lack a clear roadmap or prescriptive advice for implementing it effectively in their own unique environments. Regulations are undefined and a changing and ambiguous regulatory landscape leads to uncertainty and the need for companies to invest significant resources to maintain compliance. Those that can't keep up risk losing their competitive edge. Furthermore, existing solutions are manual and incomplete. Even organisations that are implementing governance today are doing so with a patchwork of disparate tools and manual processes. Not only do such solutions require constant maintenance, but they also risk critical gaps in coverage."

I remember the first time I created a simple machine learning model. It was a model that could predict your salary according to your years of experience. And after making it, I was curious about how I could deploy it into production. If you have been learning machine learning, you might have seen this challenge in online tutorials or books. You can find the source code here if you are interested.

In May this year, Gartner predicted global IT spend would fall 8% over the course of the year as business and technology leaders refocused budgets, prioritizing things like cloud collaboration solutions and cybersecurity. We might be prone to think investments in technologies like artificial intelligence (AI) and machine learning (ML) have been shelved for now. But a new report from Algorithmia suggests that's not the case. Not only has the upheaval of 2020 not impeded AI/ML efforts that were already underway, but it appears to have accelerated those projects as well as new initiatives. A key takeaway from the blind study, which included 403 business leaders involved in machine learning initiatives at companies with US$100 million or more in revenue, is that enterprise IT departments are increasing machine learning budgets and headcount despite the fact that many haven't learned how to translate increasing investments into efficiency and scale.

Object storage is critical to machine learning development, and provides a place to store training, test, and validation data sets as well as pretrained and fine-tuned models. It's also a huge revenue generator for cloud companies, as AWS' S3 is reported as its most popular service, so finding free and unlimited cloud object storage is quite difficult, but not impossible. Algorithmia is a serverless machine learning deployment platform (more on deploying models below). There are a number of utilities that Algorithmia provides in order to facilitate the deployment of models, including free object storage. According to their documentation, "Algorithmia's Data Sources make it easy to host your data files on the Algorithmia platform for free, while our Data API makes it a cinch to work with your hosted data." Their Data API is available in Python and has a getFile()/putFile() structure that makes it easy to download and upload data sets, models, and other utilities that may facilitate machine learning.

Companies are planning to increase their spending on artificial intelligence and machine learning as a result of the pandemic, and IT leaders believe that those initiatives should have been a higher priority for their organizations all along, according to a newly released survey by Algorithmia, a provider of ML operations and management platforms. Algorithmia's "2020 Enterprise AI/ML Trends" survey was completed in August by over 100 IT directors and above who are involved with those initiatives and work in companies with at least $1 billion in annual sales and 5,000 or more employees, the company said. There is little doubt the events of the past six-plus months have disrupted the plans of IT organizations. In fact, 42% of IT leaders responding to Algorithmia's survey said that at least half of all their AI/ML projects were impacted from a priority, staffing, or funding standpoint because of the COVID-19 pandemic. SEE: Microsoft's new feature uses AI to make video chat less weird (TechRepublic) But that doesn't mean those projects are going away--instead, their focus may have shifted, Algorithmia said. For example, 54% of IT leaders said their projects were focused on financial analysis and consumer insight prior to the pandemic.

For most professional software developers, using application lifecycle management (ALM) is a given. Data scientists, many of whom do not have a software development background, often have not used lifecycle management for their machine learning models. That's a problem that's much easier to fix now than it was a few years ago, thanks to the advent of "MLops" environments and frameworks that support machine learning lifecycle management. The easy answer to this question would be that machine learning lifecycle management is the same as ALM, but that would also be wrong. That's because the lifecycle of a machine learning model is different from the software development lifecycle (SDLC) in a number of ways.