AI software enters business workflow
When we hear the term AI software, some of us think of a futuristic world where machine learning has taken artificial intelligence to extreme levels. Fortunately, today’s AI services provide tools for all types of businesses to interact with complex data.
AI software examples
AI software called Natural Language Processing allows for the understanding of voice commands in home automation devices and provides intelligence for language translation.
Facial recognition is a machine learning use case that is used by social media platforms to accurately tag photos. Open-Source Facial Recognition is a deep learning model that recognizes not only that a face exists but also who the face belongs to.
The open availability of these and other models allows for data scientists to be immediately productive in their use of AI software for data analysis.
Infrastructure changes ahead for machine learning workflows
As more and more aspects of AI become mainstream, software and business services will include it as a critical part of their roadmaps. Existing infrastructure will have additional requirements geared more toward new problems a business is trying to solve with an AI software implementation.
The future-reaching nature and highly adaptable features of a centralized repository of machine learning models have already provided solutions to a large number of analytic problems with big data.
Algorithmia is leading the way to a machine learning–oriented future by providing a scalable deployment infrastructure that handles critical aspects of the machine learning lifecycle: deployment, manageability, scalability, and security. In this way, data scientists and DevOps can focus on using their expertise to do their intended jobs while Algorithmia seamlessly handles the rest. Designed to complement existing processes, Algorithmia will easily become your central hub for ML developments.
Typical languages for AI software development
Many programming languages used for AI software development are familiar to those accustomed to using powerful programs and scripting tools to automate various tasks. For instance, DevOps engineers use Python to manipulate data beyond normal read, write, and update routines.
Python is conducive to AI software creation tasks due to the familiar object-oriented design, extensive libraries, and fast development time to support neural networks and other NLP solutions.
Scala is a prominent machine learning language and is gaining popularity because Spark, a big data processor, is written in Scala. Scala is a compiled language and offers flexibility and scalability, which lends itself well to big data projects.
Of course, Java is popular for its ease of use and ability for data scientists to debug and package models used. Large-scale projects take advantage of Java’s simplified workflow, and it has aspects that make it desired for graphical representations of data.
In addition to these languages, Algorithmia provides a treasure trove of pre-developed machine learning models for most major AI software languages in languages such as Python, R, Rust, Go, Swift, and Scala.
AI software should “just work”
Before tools, processes, and infrastructure matured, DevOps engineers were busy pioneering methods to automate products and services all the way to production. Key aspects of this CI/CD pipeline include source code management, building, packaging, and deployment, all of which must be done in a secure, repeatable manner with little to no human interaction necessary.
This usually involves loosely tying a number of different products and technologies together. The easiest approach is using an existing AI platform; there is no need to recreate the wheel.
Frictionless AI and ML model management
Algorithmia handles everything that would normally require close collaboration between data scientists and DevOps engineers. Often times, data scientists serve dual purposes: developing new tools and workflows in addition to solving critical business problems.
Moreover, DevOps likely has never had to deploy a ML model. By incorporating an auto-scaling, serverless platform, Algorithmia allows for consistent deployment of your models for internal or external consumption.
As with all problem-solving initiatives that involve large data sets, accessing that data quickly and without the need to migrate to alternate formats is paramount. In addition to data hosted in the AI Platform, data stored with major cloud providers connect to the project with ease using an intuitive interface. By using the concept of “collections,” the Algorithmia AI Platform’s Data Model Layer allows teams of customers to work in a private subset of models, moderate model publishing, and organize models into logical groups based on teams.
Avoiding AI software engineering and infrastructure pitfalls
Another critical aspect of a successful AI model deployment pipeline is quality documentation. The need to achieve fast results while also gaining the confidence of stakeholders is only possible if the team is aware of the full capabilities of the AI platform they choose.
The scalability of the Algorithmia platform is the product of much development in cloud computing. After pushing your model’s code with Git, Algorithmia takes over. It not only handles the DevOps aspects of publishing your model as an API, it controls all aspects of preparing the model for scale.
This advancement in AI software engineering enables data scientists to deliver solutions in a fraction of the time while providing tried and true DevOps processes that will not be foreign to an established team.
Start your machine learning journey on the right foot
Choosing the right AI platform for your team is probably the most influential factor in determining the direction in which your ML model development will mature.
Many companies that offer solutions in the AI software realm also offer a myriad of other services; Algorithmia only does AI software. For a demo of what Algorithmia can do for your company’s ML program, sign up here.