MLOps hopes to build automation and improve the nature of ML production while likewise concentrating on business and regulatory necessities.
It is estimated that the fourth industrial revolution is largely driven by the AI/ML capabilities of organisations. According to experts, if you neglect creating data transformation and a model-driven organization, then you are destined to fail. The change in machine learning is bringing greater than that any past disruption has achieved, and there are three central mainstays of AI adoption: data, innovation, and organisational culture and processes.
For the ideal adoption of ML across millions of organisations, there requires a standardisation of the machine learning workflows so there is no obstruction to implementation. But, this is not the case currently, as the complex processes involved in ML models makes it very challenging to deploy them. Also, there are a plethora of tools and frameworks to make it more complex. Then, there are many challenges that companies can face when it comes to using ML in the enterprise space such as the ones related to diagnostics, governance, scalability, data compliance, etc.
DevOps vs MLOps: Transforming ML Pipelines With DevOps Practices
To overcome such challenges, experts say MLOps is on the fast rise to bring efficient collaboration and communication between data scientists and ops teams. MLOps hopes to build automation and improve the nature of ML production while likewise concentrating on business and regulatory necessities. Like the DevOps or DataOps approaches, MLOps also focuses on the entire lifecycle – from continuous integration/continuous delivery and orchestration to diagnostics and governance.
Many of the existing MLOps work on CI/CD pipeline utilizing a managed machine learning service and cloud-based development services. Unlike using DevOps for app development, MLOps is very different as it is extremely iterative and involves so many languages, libraries, toolkits, and environments for different types of ML models.
As part of MLOps, there could be multiple pipelines running in parallel with ML-specific interdependencies that require agile management to ensure a smooth integration. And, then there is specialized hardware involved too, utilizing powerful CPUs and GPUs using frameworks like TensorFlow, Caffe, Apache MXNet, etc.
The other thing that makes MLOps different from classic DevOps is the toolchain. MLOps would require not just using things open source repository, automation software, orchestration, and containerisation, but also things that cater to compliance, risk and business goals.
MLOps Offerings In 2019 That Signal The Burgeoning Trend
As part of MLOps, the integration of ML code, datasets and models into operational procedures becomes incredibly important for organisations. Many vendors have been introducing solutions which cater to the specific integration and develop ML pipelines and workflows.
Let’s look at a few vendors and their announcements in 2019 which highlights the burgeoning MLOps market.
Cloudera has recently announced an initiative around the operations of the machine learning (ML) workflow. Here, the company is extending the Apache Atlas platform to accommodate the governance of machine learning assets with BlueData’s EPIC software platform.
In September 2019, HPE (Hewlett Packard Enterprise) has also announced its foray into the expanding MLOps market with an array of software and tools for managing models. Those tools combine container-based software for AI development and management acquired in a deal, last year for BlueData.The MLOps service is meant to extend the capabilities of which allows enterprises to create Hadoop and Spark clusters in virtual environments.
In June 2019, DataRobot acquired ParallelM after its $206 million mega funding and indicates the rising prominence of MLOps. ParallelM- MLOps tools can be used to scale deployment, management and governance of machine learning (ML) pipelines in production.
Experts also say there is a trend where we can see the rise of model-as-a-service on cloud marketplaces where companies will be offering multiple ML models based on license base subscription and are charged on the basis of consumption. This way companies won’t have to build ML models from absolute scratch.
Open source will also be playing an important role. We can see the increasing use of frameworks like MLflow for MLOps, which is an open-source project is being led by Databricks and built into the Databricks Unified Analytics Platform, which is available on Amazon Web Services and in the Azure Databricks service.
While MLOps also began as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. Applying DevOps practices to machine learning workloads not only brings models to the market faster but also maintains the quality and integrity of those models. To overcome the prototype phase, smooth, automated and dependable operations have to exist. Thus, we may see much higher adoption in 2020 for MLOps — machine learning operations practices to standardize and make the lifecycle of machine learning in production more efficient.