Amazon SageMaker Data Wrangler helps you understand, aggregate, transform, and prepare data for machine learning (ML) from a single visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. Data science practitioners generate, observe, and process data to solve business problems where they need to transform and extract features from datasets. Transforms such as ordinal encoding or one-hot encoding learn encodings on your dataset. These encoded outputs are referred as trained parameters.

As enterprises move from running ad hoc machine learning (ML) models to using AI/ML to transform their business at scale, the adoption of ML Operations (MLOps) becomes inevitable. As shown in the following figure, the ML lifecycle begins with framing a business problem as an ML use case followed by a series of phases, including data preparation, feature engineering, model building, deployment, continuous monitoring, and retraining. For many enterprises, a lot of these steps are still manual and loosely integrated with each other. Therefore, it's important to automate the end-to-end ML lifecycle, which enables frequent experiments to drive better business outcomes. Data preparation is one of the crucial steps in this lifecycle, because the ML model's accuracy depends on the quality of the training dataset.

Data preparation remains a major challenge in the machine learning (ML) space. Data scientists and engineers need to write queries and code to get data from source data stores, and then write the queries to transform this data, to create features to be used in model development and training. All of this data pipeline development work doesn't really focus on the building of ML models, but focuses on the building of data pipelines necessary to make the data available to the models. Amazon SageMaker Data Wrangler makes it easier for data scientists and engineers to prepare data in the early phase of developing ML applications by using a visual interface. Data Wrangler comes with over 300 built-in data transformations to help normalize, transform, and combine features without writing any code. You can now use Snowflake as a data source in Data Wrangler to easily prepare data in Snowflake for ML.

We usually think of surveillance cameras as digital eyes, watching over us or watching out for us, depending on your view. But really, they're more like portholes: useful only when someone is looking through them. Sometimes that means a human watching live footage, usually from multiple video feeds. Most surveillance cameras are passive, however. They're there as a deterrence, or to provide evidence if something goes wrong.

Pretty much every data rookie starts with Excel. It is a wonderful program for storing, cleaning and analysing (yes, you read that correctly) your data. Strictly speaking, Excel isn't free, but really – who pays for it these days? If you buy a Windows PC or laptop it'll usually come pre-installed, and if you get a new PC at work your employer will have it pre-installed for you. If you're prepared to look the other way, there are guys who know guys who can get you a copy that fell off the back of a lorry, but I wouldn't endorse that.

Pretty much every data rookie starts with Excel. It is a wonderful program for storing, cleaning and analysing (yes, you read that correctly) your data. Strictly speaking, Excel isn't free, but really – who pays for it these days? If you buy a Windows PC or laptop it'll usually come pre-installed, and if you get a new PC at work your employer will have it pre-installed for you. If you're prepared to look the other way, there are guys who know guys who can get you a copy that fell off the back of a lorry, but I wouldn't endorse that.