MLOps, a combination of Machine Learning and Operations, is a practice which aims in making the machine learning production seamless and efficient. The data science community agrees that the MLOps is an umbrella term for best guiding and practices around machine learning- not a single technical solution. It is a practice for communication between data scientist and operation professionals which helps in managing the ML production lifecycle while focussing on business and regulatory requirements.
There are three components of MLOps :
- Machine learning
- DevOps (IT)
- Data engineering
These three components are the key elements which works towards the machine learning lifecycle loop within an organization.
Why MLOps is important?
Mlops is deeply collaborative in nature, automate as much as possible to enable richer and consistent insights with machine learning. In any organisation, MLOps allows the data scientist to execute their best operations which can take the business decisions off their plates where they can deploy models, helping to reveal deeper insights more quickly and efficiently.
Advantages of MLOps:
The major advantages of MLOps for any company are:
- Through robust ML lifecycle management, it helps in rapid innovations.
- Helps in creating reproducible workflow and models.
- Helps in deploying the high precision models easily in any location.
- Easy evaluation of the key features with minimal bias.
- Helps in improving the model performance by using advance data bias analysis.
Major disadvantages to MLOps:
One of the main obstacles for Machine Learning being deployed in production is the level of disruption that may occur with the inclusion of ML into forms/ front end applications. This may come from preconceived views on ML or poor deployments in the past. The second challenge for the business is about the evaluation consideration of model risks when actualizing a machine learning model. One of the major technical challenge is regarding the lack of coordination and improper handoffs between the data scientists and operation teams which can lead to delays and errors.
Where does it overlap with data or model governance?
Under the model governance, MLops by applying to monitor the attributes on a massive scale can provide rich model performance. For analysing critical moments MLops can provide the ability to take the snapshots of the pipeline. Not only this, the logging facilities and audit trails can be used for continuity of compliance and reporting. Parallelly by applying the MLOps practices, an organization will ensure that it should be compliant and conducting governed practices.
How can it be achieved with Oracle tech?
Oracle has extended its collaboration platform for data scientists to manage and build machine learning models with the help of managed service available from the Oracle public cloud. This offering is Oracle cloud infrastructure, which is a data science platform which works with the variety of data sources. It not only works with different data sources but also supports use of open-source (Python) libraries and frameworks. In addition, the Oracle cloud infrastructure enables Git/Github to be used enabling code control throughout the development cycle. For performance and scalability, algorithms can be passed down to the database to be ran through Oracle Machine Learning (OML) or with Enterprise R.
Additionally, Oracle also utilises the graph data store (spatial and graph for Oracle databases) for various risk management, use cases like detecting and analysing cyber threats or understanding who in the business has access to which part of a data warehouse. These graph stores and ML models feed on each other enabling a greater depth of understanding and insight as well as enriching MLOps.
Is it easy to achieve?
According to the top researchers, MLOps is difficult to achieve for some companies. The root cause of this is the fundamental difference between the ML and traditional software. As ML is not just the code, it is code plus data. Since the model’s behaviour depends on the input data (Training data) which it will receive at the prediction time, which you cannot determine in advance. This means for auditing purposes; we need to know the model settings but also what the model was trained on. This all needs to be considered during MLOps and letting the business and potentially your customers know how this is achieved and used.
“ML is pretty mature, but Business impact and deployment is not”
How does Vertice achieve MLOps?
An oracle platinum partner, Vertice has a dedicated Oracle-certified data science practices in Europe. In Vertice we use the Oracle Infrastructure Cloud (DS platform), Oracle Analytics cloud and Autonomous data warehouse to perform the MLOps, enabling your business to get the most from your data.
To manage the ML scripts and models we use Machine Learning catalogue where we create the data flows in Oracle Analytics Cloud (OAC) to train the ML models to predict and visualize the results. Depending on the requirements or restrictions and by combining existing open-source libraries, Vertice can implement their own in-house MLOps processes.
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